System

Abstract

To provide a system for reducing burdens on a child patient and his / her family and improving the efficiency of work at a medical site.SOLUTION: A specific processing part 290 of a data processing system 12 in a system for supporting the hospital life of a child patient has a function for storing patient information, a server function for acquiring the stored patient information and transmitting an instruction to a robot, assists the child patient to the robot on the basis of the instruction from the server function, and monitors and analyzes the working state of a robot means and the health state of the patient to predict the next action.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

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

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

[0004] Hospitalization of pediatric patients places a heavy burden not only on the patients themselves but also on their accompanying family members. Family members face time constraints and work restrictions due to long periods of accompanying the patient, and are more likely to develop health problems. Furthermore, the workload of medical staff increases, making it difficult to provide efficient medical care. It is necessary to improve this situation, reduce the burden on pediatric patients and their accompanying family members, and improve the efficiency of medical work. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a robot system that supports pediatric patients during their hospital stay. Specifically, the system includes a database for storing patient information, a server for acquiring information from the database, and a robotic system for sending instructions to the robot. Based on these instructions, the robotic system provides meals, guides the patient to the toilet, and assists with reading and online classes. Furthermore, the system monitors and analyzes the robot's work status and the patient's health status via an analysis unit, predicting and planning the robot's next actions, thereby achieving efficient and continuous support. This reduces the burden on accompanying family members, makes pediatric patients' hospital stays more comfortable, and improves the efficiency of medical operations.

[0006] "Pediatric patient" refers to a minor patient who is hospitalized at a medical institution.

[0007] "Hospital life" refers to all aspects of life in a hospital room at a medical institution, including treatment, meals, rest, and study.

[0008] "Robotic system" refers to a system that includes automated mechanical devices and their control and communication technologies designed to assist pediatric patients in their hospital stay.

[0009] "Patient Information" refers to personal medical data such as a patient's name, age, diagnosis, and treatment.

[0010] "Database means" refers to a storage device and its management software for organizing and storing patient information.

[0011] "Server means" refers to a central computer system that transmits and receives data over a network.

[0012] The term "robot means" refers to a robot that performs physical operations based on instructions from the server means and provides specific care to a pediatric patient.

[0013] "Analysis means" refers to a system including software and hardware for monitoring and analyzing the robot's working status and the patient's health status.

[0014] "Serving food" refers to the act of a robotic means delivering food to a pediatric patient and assisting the patient in eating.

[0015] "Toilet guidance" refers to the act of a robotic means safely guiding a pediatric patient to the toilet and providing assistance as needed.

[0016] "Reading aloud" refers to the act of a robotic device reading aloud stories, picture books, etc. to a pediatric patient.

[0017] "Online teaching support" refers to the act of using a robotic means to support online learning activities for a pediatric patient.

[0018] "Work status" refers to the current task being performed by the robot means and its progress.

[0019] "Health status" refers to the physical condition of the pediatric patient, including their physical condition and the progression of their illness.

[0020] "Reporting in real time" means that the robot means immediately transmits data on work status and health condition to the server means without any time lag.

[0021] "Recording data" refers to the act of storing and saving data received by the server means in the database means.

[0022] "Data analysis" refers to the process of analyzing trends and predicting next actions based on collected data. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0027] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0028] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0029] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0037] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This invention relates to a robot system that supports pediatric patients in hospital. This system includes a database for storing patient information, a server for acquiring patient information and sending instructions to the robot, a robot that provides assistance based on the instructions, and an analysis unit for monitoring and analyzing the working status and health condition and predicting the next action.

[0045] server

[0046] The server is a central computer system that manages patient information and communicates with the robot. The server maintains a database and stores patient information entered by the user (doctor). The server also provides an API (Application Program Interface) and receives requests from the robot and user terminals. This API enables the robot to obtain information, send instructions, and report work status.

[0047] Terminal (robot)

[0048] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from a server, the robot performs tasks such as serving meals, guiding them to the toilet, reading aloud, and supporting online classes. The robot also reports its own work status and the patient's health status to the server in real time, and these are recorded in a database.

[0049] User

[0050] The users, doctors and medical staff, input patient information through the server and instruct the robot on tasks. They can also check the patient's health condition and the robot's operating status from the server and issue new instructions as needed.

[0051] Detailed explanation of program processing

[0052] The server first stores patient information in a database, and the stored information is retrieved through an API when a user issues instructions to the robot. For example, if a user issues an instruction to feed a pediatric patient, the server sends the instruction to the robot.

[0053] When the robot receives the instruction, it starts the specified task (e.g., serving food). The robot constantly monitors the progress of the task and reports the results to the server in real time. The server records the received information in a database and, if necessary, analyzes the data using analytical means to predict the next action.

[0054] Specific examples

[0055] Example 1: Meal assistance

[0056] The user (doctor) enters new patient information and saves it in the database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in the database and uses analytical tools to plan the next task.

[0057] Example 2: Toilet Guidance

[0058] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0059] As described above, this invention provides a concrete means to support pediatric patients during hospitalization and reduce the burden on accompanying family members and medical staff. Furthermore, this system can monitor the patient's health status and task progress in real time, allowing for efficient continuous support.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[0063] Step 2:

[0064] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[0065] Step 3:

[0066] Once the patient information is saved on the server, the user can call the server API to instruct the robot to perform a specific task (e.g., serving a meal). The server stores the patient ID and task information and prepares it to be sent to the robot.

[0067] Step 4:

[0068] The server sends instructions to the robot, specifically, the necessary task information (e.g., "Provide food for patient ID 1") to the terminal to which the robot is connected.

[0069] Step 5:

[0070] The terminal (robot) receives instructions from the server and starts executing the task. For example, the robot starts operating to deliver food along a set route.

[0071] Step 6:

[0072] The terminal (robot) constantly monitors the progress of the task and the patient's reaction. For example, it uses the robot's sensors to check the patient's satisfaction by checking the state of the meal.

[0073] Step 7:

[0074] The terminal (robot) reports the task completion status and ongoing status to the server in real time. Specifically, when a task is completed, the information is sent to the server.

[0075] Step 8:

[0076] The server records the received data in a database, including the timestamp of task completion and the patient's response.

[0077] Step 9:

[0078] The server uses the recorded data to perform analysis and predict the next action, for example, determining the best time to serve meals based on the data of multiple patients.

[0079] Step 10:

[0080] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user confirms this and prepares to send the next instruction to the robot.

[0081] Example 1

[0082] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0083] Pediatric patients' hospital stays place a heavy burden on medical staff and accompanying family members, and they often require assistance with health management and daily life. However, there is a lack of means to provide appropriate health management and support to patients while reducing these burdens. Therefore, there is a need for a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and families.

[0084] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0085] In this invention, the server includes an interface means for a user to input patient information, a server means for storing the patient information input via the interface means in a database, and a server means for acquiring the patient information stored in the server means and sending instructions to the robot, thereby enabling the robot to assist pediatric patients by providing meals, guiding them to the toilet, reading to them, assisting them with online classes, or performing other tasks.

[0086] The "interface means for users to input patient information" refers to a device or software that allows doctors and medical staff to input basic patient information and store it in the system.

[0087] The "server means" is a central control device for storing patient information in a database and sending instructions to the robot.

[0088] A "database" is an information storage device for centrally managing a patient's basic information, medical history, allergy information, health status, etc.

[0089] The "robot means" is a mechanical device that provides assistance to pediatric patients based on instructions from the server.

[0090] "Patient information" refers to data necessary for health management, including the patient's name, age, medical history, allergy information, etc.

[0091] "Work status" refers to information that indicates the progress of the task being performed by the robot and the results of that task.

[0092] "Health status" refers to information about the patient's current health, including vital signs and dietary intake.

[0093] "Analysis means" refers to a device or software that analyzes the reported data from the robot and predicts and plans the next task.

[0094] "Predicting the next action" refers to planning the next assistance task that the robot should perform based on the data obtained by the analysis means.

[0095] This invention is a robot system for supporting pediatric patients in hospital. This system includes a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, and an analysis means for monitoring and analyzing the working status and health condition of the patient.

[0096] Server Means

[0097] The server is the central component of this system and fulfills several roles. First, it stores patient information entered by users (doctors and medical staff) in a database. The stored information is retrieved through an API (Application Program Interface) and used to send instructions to the robot.

[0098] Specifically, the user inputs and saves patient information through a dedicated interface, including the patient's name, age, medical history, allergy information, etc. The server stores this information in a database and sends instructions to the robot when necessary.

[0099] Robotic Means

[0100] The robotic means performs specific tasks for the pediatric patient based on instructions received from the server, such as providing meals, guiding the patient to the toilet, reading aloud, and supporting online classes.

[0101] The robot follows instructions received from the server and performs the specified task. For example, if the instruction is "serve lunch for patient A," the robot delivers the meal to the patient and provides support. Once the task is completed, the robot reports the work status and the patient's health condition to the server.

[0102] analytical means

[0103] The server receives the data reported by the robots and records it in a database. It then uses analytical tools to analyze this data and plan the next tasks, ensuring that the entire system always functions optimally and that the pediatric patient's hospital stay goes smoothly.

[0104] Specific examples

[0105] Examples of meal assistance include:

[0106] The user (doctor) enters Patient A's information and saves it in a database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in a database and uses analytical tools to plan the next task.

[0107] Prompt for the generative AI model:

[0108] "Please explain the specific steps involved in a robot delivering lunch to pediatric patients."

[0109] Examples of toilet guidance:

[0110] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0111] Prompt for the generative AI model:

[0112] "Please explain the specific processing steps of the toilet guidance robot."

[0113] As described above, the present invention provides a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and their families through cooperation between components.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] The user enters patient information.

[0117] Users (doctors and medical staff) use a dedicated interface to enter basic patient information (such as name, age, medical history, allergies, etc.) The data is then stored on the front end and prepared for transmission to the server, where it is correctly formatted and sent to the server.

[0118] Step 2:

[0119] The server stores the patient information in a database.

[0120] The server receives patient information submitted by the user. The received data goes through a data validation process before being stored in the database. If validation is successful, the server saves the data to the database, specifically by adding it as a record to the appropriate table in the database.

[0121] Step 3:

[0122] The user gives instructions to the robot.

[0123] Through the interface, the user sends instructions to the server specifying tasks for a specific patient (e.g., providing food, guiding the patient to the toilet, etc.). This instruction includes detailed information about the task to be performed. The server accepts this instruction and prepares it to be sent to the corresponding robot.

[0124] Step 4:

[0125] The server sends instructions to the robot.

[0126] The server receives instructions from the user, converts them into the appropriate format, and sends them to the robot's API endpoint. These instructions contain details of the task to be performed and information about the patient. The data sent becomes instructions that the robot can interpret and execute the task.

[0127] Step 5:

[0128] The robot receives instructions and performs the task.

[0129] The robot analyzes instructions received from the server and carries out the specified task. For example, if the instruction is "serve lunch for patient A," the robot prepares the meal and delivers it to the patient's room. The robot uses sensors to monitor its progress and the patient's response.

[0130] Step 6:

[0131] The robot reports its work status and health status to the server.

[0132] The robot monitors the progress of the tasks it is performing and the patient's health status in real time and reports the information to the server. The reported data includes the task completion status and the patient's vital signs. This data is sent through the server's API.

[0133] Step 7:

[0134] The server records the report in a database.

[0135] The server validates the reported data received from the robot and stores it in a database. The stored information serves as the patient's health history. The storage procedure includes verifying the accuracy of the data and adding records to the appropriate tables.

[0136] Step 8:

[0137] The server plans the next task.

[0138] The server analyzes the information recorded in the database and plans the next task to be executed. Using analytical means, it determines the optimal next task based on past data and the current situation. The planned task is again notified to the user or robot, and preparations for execution are made.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] In modern logistics centers, tasks such as product picking, packing, inventory management, and transportation still require a large labor force. While high efficiency is required, the workload of workers is a major issue. Real-time information management is also essential to ensure the accuracy and speed of operations. However, conventional systems have difficulty resolving these issues efficiently and comprehensively. The present invention aims to solve these issues, improve the efficiency of operations within logistics centers, and reduce the labor burden.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes database means for storing patient information, server means for acquiring the patient information stored in the database means and sending instructions to the robot, robot means for assisting pediatric patients, analysis means for monitoring the working status of the robot means and the health condition of the patient and analyzing it to predict the next action, database means for applying the system to a logistics center and storing product information, server means for acquiring the product information stored in the database means and sending instructions to the robot, and robot means for picking and packing products. This enables efficient and accurate picking, packing, inventory management, and transportation of products in the logistics center.

[0144] Definition of Terms

[0145] "Pediatric patient" refers to a child between the ages of birth and 18 years who requires appropriate medical care.

[0146] "Hospitalization" refers to the period of receiving medical treatment and care in a hospital or medical facility, which can last from short to long periods of time.

[0147] A "robotic system" refers to a system that includes a mechanical device for automatically performing a specific task and the infrastructure for controlling and communicating with it.

[0148] "Patient information" refers to a collective term for data such as personal information, medical history, health status, and treatment plans of pediatric patients.

[0149] "Database means" refers to a system for electronically collecting, storing and managing information.

[0150] "Server Means" refers to a system that includes a central processing unit for accessing Database Means and communicating with other devices and systems.

[0151] "Instructions" refer to specific instructions for performing a particular task.

[0152] "Robotic Means" refers to an automatically controlled mechanical device for performing tasks based on instructions received from Server Means.

[0153] "Work Status" refers to the progress or status reports of a robotic means as it performs a task.

[0154] "Health status" refers to the overall physical and mental state of a patient.

[0155] "Analysis means" refers to tools and systems that have the functionality to analyze collected data and predict next actions.

[0156] "Logistics center" refers to a facility that stores, manages, and distributes goods and materials.

[0157] "Product information" refers to data such as the type, quantity, location, and condition of products within a logistics center.

[0158] "Picking" refers to the task of removing specified items from a specific location in the warehouse.

[0159] "Packing" refers to the process of placing goods into suitable packaging or packaging materials.

[0160] "Inventory control" refers to the process of monitoring and controlling the quantity and condition of stored goods.

[0161] "Transportation" refers to the act of moving goods from one place to another.

[0162] MODE FOR CARRYING OUT THE INVENTION

[0163] System Overview

[0164] This article describes a robotic system that automates the picking, packing, inventory management, and transportation of goods in a distribution center. This system includes the following main components:

[0165] Database Means

[0166] Server Means

[0167] Robotic Means

[0168] analytical means

[0169] Explanation of program processing

[0170] Hardware and software used

[0171] The implementation of this system uses the following hardware and software:

[0172] Hardware: Transport robots (e.g., automated transport equipment and robotic arms)

[0173] Software: Central server (providing API), database management system, robot control program (implemented in Python, etc.)

[0174] Data processing and calculation

[0175] 1. Product information management

[0176] The server stores product information in a database, which includes product type, quantity, location, condition, etc.

[0177] 2. Obtaining and Sending Instructions

[0178] The server means retrieves the product information stored in the database means and sends instructions to the robot means, including the location from which a specific product should be picked, the packing item, and the delivery location.

[0179] 3. Execute the task

[0180] The robotic means executes tasks based on instructions from the server. For example, in the case of picking, the robot picks up a specified item from a specific location in the warehouse and moves it to an area for packing.

[0181] 4. Work Status Report

[0182] The robot means reports the work status and inventory status to the server in real time. The server records the information received in real time in the database means, analyzes the data using the analysis means as needed, and predicts the next action.

[0183] Specific examples

[0184] For example, if a user sends a picking instruction for product ID "123456" to the server, the server sends this information to the robot. The robot picks product ID "123456" from the specified location in the warehouse, and after completing the task, reports to the server that picking of "123456" is complete. The server then instructs the robot on the next task based on that report.

[0185] Example prompts for generative AI models

[0186] Server: Please send the current warehouse status and product picking instructions in the following format.

[0187] User: Please pick the following item ID 123456 from Aisle 3, Shelf 5.

[0188] This system enables efficient and accurate picking, packing, inventory management, and transportation of products within the logistics center, ensuring speed and accuracy of work while also reducing the labor burden.

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Specific flow of program processing

[0191] Processing Step Description

[0192] Step 1: Store product information in a database

[0193] Input: The user enters product information (product ID, type, quantity, location, and condition).

[0194] Specific operations: The server receives the product information and stores the information in the database means.

[0195] Output: Product details saved in database.

[0196] Step 2: Sending instructions from the server to the robot

[0197] Input: The server receives picking and packing instructions from the user (product ID, location, detailed work instructions).

[0198] Specific operation: The server accesses the database means to obtain the relevant product information and instructs the robot to perform a specific task (e.g., picking, packing, transporting).

[0199] Output: A task is sent to the robot.

[0200] Step 3: Robot picking the items

[0201] Input: The robot receives a picking instruction from the server (e.g., pick product ID "123456" from Aisle 3, Shelf 5).

[0202] Specific operation: The robot moves to the specified location in the warehouse and picks the corresponding item.

[0203] Output: The picking task is completed and the result is stored in temporary memory.

[0204] Step 4: Robot packs the products

[0205] Input: The robot next receives packing instructions.

[0206] Specific operation: The robot moves the picked items to the packing area and packs the items using appropriate packaging materials.

[0207] Output: The packing task is completed and the result is stored in temporary memory.

[0208] Step 5: Reporting work status and inventory status

[0209] Input: The robot reports the results of the completed picking or packing task to the server, specifically, which items were moved to where, and whether packing is complete.

[0210] Specific operation: The robot sends details of the work status and inventory data to the server in real time.

[0211] Output: The server records the received information in a database and updates the real-time inventory status.

[0212] Step 6: Plan and instruct next steps

[0213] Input: The server receives the latest inventory information and work status recorded in the database.

[0214] What it does: The server uses analytics to analyze the data, plan the next best task, and then send new instructions to the robot.

[0215] Output: A new task is sent to the robot and it starts its next task.

[0216] These processing steps enable efficient and accurate picking, packing, inventory management, and transportation of goods within the logistics center.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] This invention relates to a robot system that supports pediatric patients in hospital. This system is composed of a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, an analysis means for monitoring, analyzing, and predicting the work status and the patient's health condition, and an emotion engine for recognizing the user's emotions.

[0219] server

[0220] The server is a central computer system that manages patient information and communicates with the robot and emotion engine. The server maintains a database and stores patient information entered by the user (doctor). It also receives input from the emotion engine and adjusts instructions to the robot.

[0221] The server provides an API (Application Program Interface) and accepts requests from robots and emotion engines, allowing them to obtain information, send instructions, and report on their work status.

[0222] Terminal (robot)

[0223] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from the server, the robot performs tasks such as serving meals, guiding them to the toilet, reading to them, and supporting them in online classes. The robot can also adjust its assistance methods based on emotional data from an emotion engine. For example, if a patient is feeling stressed, the robot will respond by speaking to them gently.

[0224] The added emotion engine analyzes the facial expressions, voice, and speech patterns of users and patients via the robot's sensors to identify their emotions. The identified emotion data is immediately sent to the server and used to adjust the next instructions.

[0225] User

[0226] The user, a doctor or medical staff member, inputs patient information through the server and instructs the robot on tasks. The server can also check the patient's health condition, the robot's operating status, and even emotion data from the emotion engine, and issue new instructions as needed.

[0227] Detailed explanation of program processing

[0228] server

[0229] The server first stores patient information in a database. This information is obtained through an API when the user issues commands to the robot. The server also receives emotion data sent from the emotion engine and stores it in the database.

[0230] Terminal (robot)

[0231] The robot carries out tasks when it receives instructions from the server. For example, if it is instructed to serve a meal, the robot will carry the meal and serve it to the patient. The robot also monitors the progress of the task and the patient's reactions, and continuously sends this data to the server. If there is input from the emotion engine, it adjusts the assistance it provides based on that input.

[0232] Emotion Engine

[0233] The emotion engine recognizes emotions by analyzing the facial expressions, tone of voice, and language patterns of the user and pediatric patient. For example, if it identifies emotions such as stress or anxiety, it sends the data to the server. The server stores the received emotion data in a database and adjusts the next task or assistance method based on the data.

[0234] Specific examples

[0235] Example 1: Meal assistance

[0236] The user (doctor) inputs new patient information and saves it in the database. The user then sends meal preparation instructions for a specific pediatric patient to the server, which forwards the instructions to the robot. The robot uses an emotion engine to analyze the patient's facial expressions and tone of voice, ensuring that the patient is relaxed while preparing the meal. After completing the task, the robot reports its progress and emotion data to the server, which records the information in the database.

[0237] Example 2: Toilet Guidance

[0238] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server then forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides the necessary support. During this process, if the emotion engine detects anxiety or stress in the patient, the robot will respond by offering comforting words. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[0239] This invention provides a concrete means to support pediatric patients' hospital stays and reduce the burden on accompanying family members and medical staff. In addition, by combining it with an emotion engine, it is possible to provide detailed assistance according to the patient's emotional state, improving the quality of hospital stays.

[0240] The processing flow will be explained below.

[0241] Step 1:

[0242] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[0243] Step 2:

[0244] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[0245] Step 3:

[0246] The user sends an instruction to the server to perform a specific task (e.g., serving a meal), and the server associates the patient information with the task information.

[0247] Step 4:

[0248] The server sends task instructions to the robot. Specifically, it communicates the instruction "Provide food for patient ID 1" to the robot's terminal.

[0249] Step 5:

[0250] The terminal (robot) receives instructions from the server and prepares to perform the specified task (serving food).

[0251] Step 6:

[0252] The terminal (robot) activates an emotion engine that analyzes the facial expressions, voice, and speech patterns of the pediatric patient. The emotion engine identifies the patient's emotions and transmits the data to the server in real time.

[0253] Step 7:

[0254] The device (robot) adapts the way it performs tasks based on the identified emotional data. For example, if a patient is nervous, the robot can calm them down by speaking to them gently while providing a meal.

[0255] Step 8:

[0256] The terminal (robot) constantly monitors the progress of the task and the patient's reaction (emotional data), and reports the situation to the server as needed.

[0257] Step 9:

[0258] The server records the received task progress and emotion data in a database, specifically, saving the task completion timestamp including data from the emotion engine.

[0259] Step 10:

[0260] The server analyzes the information in the database and predicts the next task or assistance needed. For example, if it determines that the patient is relaxed while eating, it uses the same approach for the next meal.

[0261] Step 11:

[0262] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user checks this and prepares to send new instructions to the robot if necessary.

[0263] In this way, we support pediatric patients in their hospital stay, reduce the burden on accompanying family members and medical staff, and provide detailed care that is tailored to the patient's emotional state.

[0264] Example 2

[0265] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0266] Conventional robotic systems have not been able to fully realize the attentive care required to improve the quality of life for pediatric patients in hospital. In particular, the lack of a system that can recognize the patient's emotional state in real time and respond accordingly makes it difficult to reduce the patient's mental burden. Furthermore, there is a lack of a means to centrally manage work status and emotional data, which is one of the reasons for the increased burden on medical staff.

[0267] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a database means for storing patient information, a server means for acquiring the patient information stored in the database means and sending instructions to the robot, a robot means for providing care to the pediatric patient based on instructions from the server means, an analysis means for monitoring and analyzing the robot means' work status and the patient's health condition to predict the robot's next action, and an emotion recognition engine for acquiring emotion data and adjusting the care method based on the data. This allows the system to recognize the patient's emotional state in real time and provide appropriate responses, thereby reducing the mental and physical burden on pediatric patients and improving their quality of life during hospitalization. Furthermore, centralized management of work status and emotion data reduces the burden on medical staff and enables more efficient care.

[0268] "Patient information" refers to detailed data about a patient, such as basic personal information, dietary restrictions, allergy information, and special care needs.

[0269] "Database Means" refers to an electronic record device that stores patient information and allows it to be retrieved when needed.

[0270] "Server means" is a central computer system for obtaining patient information and sending instructions to the robot.

[0271] The "robot means" is a mechanical device that provides care to a pediatric patient based on instructions from the server means.

[0272] The "analysis means" is a function for monitoring the working status of the robot means and the health condition of the patient, analyzing this data, and predicting the next action.

[0273] An "emotion recognition engine" is an algorithm or device that analyzes data obtained through sensors in a robotic means to recognize and identify the emotional state of a patient.

[0274] "API" is an abbreviation for Application Program Interface, which allows the server means to accept requests from the robot means and emotion recognition engine, and to obtain information and send instructions.

[0275] An "assistance method" is a specific method of support provided by a robotic means for a specific patient need.

[0276] A "sensor" is a device that allows a robotic means to sense external information (for example, facial expressions or voice) and record it as data.

[0277] "Real-time" refers to data collection and processing occurring almost instantly, with minimal delay.

[0278] "Health status" refers to the overall state of a patient, including their physical condition and medical condition.

[0279] The present invention relates to a robotic system that supports pediatric patients during their hospital stay. This system operates by combining multiple hardware and software components, aiming to provide efficient and effective patient care.

[0280] Hardware

[0281] Server: A central computer system that interfaces with the database, APIs, analytics, and emotion recognition engine.

[0282] Terminal (Robot): A mechanical device that provides direct care to pediatric patients. It is equipped with sensors and communicates with an emotion recognition engine.

[0283] Database: An electronic record for storing patient information and affective data.

[0284] software

[0285] API: An interface through which the server accepts requests from the robot and emotion recognition engine, obtains information, and sends instructions.

[0286] Emotion Recognition Engine: Contains algorithms that analyze facial expressions, tone of voice, and language patterns to identify emotions.

[0287] Analysis means: Has the ability to monitor the robot's working status and the patient's health condition and predict its next actions.

[0288] Overall system processing flow

[0289] The user, a doctor or medical staff member, inputs patient information into the server using a dedicated terminal. This information is sent to the server and stored in a database. The user then sends instructions for specific tasks (e.g., serving meals or guiding patients to the toilet) through the server.

[0290] The server then forwards the instructions to the robot via an API. The robot uses sensors to collect real-time patient data and analyzes the emotional data through an emotion recognition engine. This emotional data is then sent back to the server to adjust the next instructions as needed.

[0291] Specific examples

[0292] Example 1: Meal assistance

[0293] 1. The user (doctor) enters new patient information and saves it in the database.

[0294] 2. The user sends meal delivery instructions to the server for a specific pediatric patient.

[0295] 3. The server forwards the instructions to the robot.

[0296] 4. The robot uses an emotion recognition engine to analyze the patient's facial expressions and tone of voice, ensuring they are relaxed while serving food.

[0297] 5. After completing the task, the robot reports its progress and emotional data to the server, which records the information in a database.

[0298] Example prompt:

[0299] "You've entered new patient information into a database and sent an API request to instruct the robot to serve the meal. Now explain how the system works so the robot receives the instruction and begins working."

[0300] Example 2: Toilet Guidance

[0301] 1. The user sends toileting assistance instructions for a specific pediatric patient to the server.

[0302] 2. The server forwards the instructions to the robot.

[0303] 3. The robot follows the instructions, safely guides the patient to the toilet and provides any necessary assistance.

[0304] 4. During this process, if the emotion recognition engine detects the patient's anxiety or stress, the robot will respond by offering kind words of encouragement.

[0305] 5. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[0306] Example prompt:

[0307] "The robot has been instructed to serve a meal. Please explain the specific flow of how to provide assistance while checking the patient's condition using the emotion recognition engine."

[0308] This invention provides a concrete means for improving the quality of hospitalized pediatric patients' lives and reducing the burden on medical staff. By combining it with an emotion recognition engine, it is possible to provide detailed assistance according to the patient's emotional state, making hospitalization more comfortable.

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Step 1:

[0311] Entering and saving patient information

[0312] The user enters patient information into the server from a dedicated terminal. This information includes basic personal information, dietary restrictions, allergies, and special care needs. The server stores the entered patient information in a database. The entered data is validated to ensure that the information is properly formatted. If it is not, an error message is returned.

[0313] Input: Patient information (personal information, dietary restrictions, allergy information, special care details)

[0314] Output: Patient information correctly saved in the database

[0315] Step 2:

[0316] Start of meal serving task

[0317] The user sends instructions for serving food to the server, which then forwards the instructions to the robot via API.

[0318] The server generates an API request containing the order code and associated patient ID and sends it to the robot.

[0319] Input: Meal instructions from user, associated patient ID

[0320] Output: API request to the robot

[0321] Step 3:

[0322] Executing meal serving tasks

[0323] The terminal (robot) receives instructions from the server. Based on the received instructions, the robot delivers meals from the kitchen to the patient's room. Sensors are used to collect the patient's facial expressions and tone of voice in real time.

[0324] Input: Meal provision instructions from the server, patient ID

[0325] Output: Started meal serving task, collected sensor data

[0326] Step 4:

[0327] Real-time monitoring and data reporting

[0328] The terminal (robot) monitors the patient's reactions while serving the meal, using sensors to collect the patient's facial expressions and tone of voice, and sends the data to a server in real time.

[0329] The server receives the emotion data sent in real time and stores it in a database, while also monitoring the robot's work status in real time.

[0330] Input: Sensor data sent from the robot

[0331] Output: Emotion data stored in the database, robot work status

[0332] Step 5:

[0333] Emotional data analysis and response

[0334] The emotion recognition engine analyzes data from the robot's sensors, analyzing facial expressions, tone of voice, and language patterns to identify emotions, and sends the results to a server.

[0335] The server determines whether further instructions are needed based on the emotional data and sends new instructions to the robot if necessary.

[0336] Input: Sensor data analysis results by emotion recognition engine

[0337] Output: Analysis results and new instructions sent to the server

[0338] Step 6:

[0339] Task completion reporting and data recording

[0340] The terminal (robot) reports to the server that the meal has been served, and the server stores the received report and emotion data in a database.

[0341] Input: Task completion report from the robot, emotion data

[0342] Output: Task completion information and emotion data recorded in a database

[0343] (Application example 2)

[0344] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0345] The present invention relates to a robot system that supports pediatric patients' hospital stays and a food delivery support system that takes into account customer emotions. Traditionally, pediatric patients' hospital stays have placed a heavy burden on medical staff and their families, and the patients themselves have often experienced anxiety and stress. Food delivery services have also been problematic in that customers experience discomfort and stress when receiving their food. In light of these circumstances, there is a need for a system that can improve the quality of hospital stays and increase customer satisfaction with delivery services.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0347] In this invention, the server includes a database means for storing and processing patient information, a server means for acquiring the patient information and sending instructions to the robot, and an analysis means for predicting the next action by monitoring and analyzing the work status and the patient's health condition received from the robot means, thereby enabling support for the patient's hospital stay and customer care during delivery services.

[0348] "Pediatric patient" refers to a patient admitted to a hospital during childhood.

[0349] "Hospitalization" refers to the period of time spent in a hospital room while receiving medical and nursing services provided by the hospital.

[0350] "Patient Information" refers to information about a patient, including their medical history, current health status, allergy information, preferences, and medical needs.

[0351] "Database means" refers to a system or device for storing, updating, and managing information.

[0352] "Server means" refers to a computer system that communicates with multiple terminals via a network and processes instructions and data within the server.

[0353] "Robotic means" refers to a mechanical device controlled by a program and designed to automatically perform designated tasks.

[0354] "Analysis means" refers to a device or system for analyzing collected data and predicting future actions.

[0355] "Emotion recognition means" refers to a device or program for identifying emotions from a user's facial expressions and voice.

[0356] "Reporting means" refers to a device or program that collects and transmits data to a server.

[0357] "Food delivery" refers to a service that delivers food and drinks to customers who order them.

[0358] "Customer preferences" refers to individual preference information such as the customer's preferred dishes, ingredients, and allergy information.

[0359] "Appropriate action" refers to taking the most appropriate action in a given situation to improve patient or customer satisfaction.

[0360] This invention provides a robot system that supports pediatric patients in hospital and takes into account the emotions of customers in food delivery services. This system is composed of a database means for storing patient and customer information, a server means for sending instructions for assistance and delivery to the robot, a robot means for providing assistance and delivery based on the instructions, an analysis means for monitoring and analyzing the work situation and emotional state, an emotion recognition means, and a reporting means.

[0361] The server first stores patient and customer information in a database and then obtains necessary information via the database means. This information is obtained through the API when issuing instructions to the robot. The server also receives emotion data sent from the emotion recognition means and stores it in the database.

[0362] The robot carries out a task when it receives an instruction from the server. For example, if it receives an instruction to serve a meal to a pediatric patient, the robot carries out the task, delivers the meal, and serves it to the patient. Similarly, in a food delivery service, the robot delivers the meal to a customer's home. The robot monitors the progress of the task and the customer's reaction, and continuously sends this data to the server.

[0363] The emotion recognition means analyzes the facial expressions, voice, and speech patterns of the user or customer to identify their emotions. For example, if a customer is smiling, it can identify "happy," and if they are feeling stressed, it can identify "sad." The identified emotion data is immediately sent to the server and used to adjust the next task or assistance method.

[0364] As a concrete example, consider the following scenario:

[0365] Update new customer information

[0366] Doctors and medical staff input new patient and customer information and store it in a database. This updated information allows the robot to provide appropriate assistance and delivery.

[0367] Example prompt sentence:

[0368] "Please update new customer information. Customer ID is 'new_customer_1' and preferences are 'gluten-free' and 'no dairy'."

[0369] Performing sentiment analysis

[0370] When the robot interacts with patients or customers, it analyzes the voice input text using emotion recognition to identify emotions, which is important for responding appropriately to the patient or customer's emotions.

[0371] Example prompt sentence:

[0372] "Analyze the sentiment for customer ID 'customer_id_1'. Input text is 'I'm so happy to receive my meal quickly!'"

[0373] Delivery food delivery

[0374] When a food delivery robot delivers a meal to a customer's home, it can take the customer's emotions into account and respond appropriately. For example, if the customer is feeling stressed, the robot will provide an encouraging message such as, "I hope this meal cheers you up!"

[0375] Example prompt sentence:

[0376] "Please execute a delivery response based on the sentiment of customer ID 'customer_id_1'."

[0377] With these functions, the present invention not only supports pediatric patients' hospital stays and reduces the burden on medical staff and their families, but also improves customer satisfaction in food delivery services.

[0378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0379] Step 1:

[0380] A user enters new patient or customer information into the server.

[0381] Input: Patient or customer information (e.g., patient ID, name, preferences, allergy information, etc.)

[0382] Operation: The server receives the information entered by the user and stores it in a database means.

[0383] Output: Updated patient and customer information in the database.

[0384] Step 2:

[0385] The server sends instructions to the robot based on the specified task.

[0386] Input: Type of task (e.g., meal service, toilet assistance, food delivery, etc.) and relevant patient or customer information.

[0387] Operation: The server sends instructions to the robotic means through the API, providing details of the corresponding tasks.

[0388] Output: Task instructions sent to the robot.

[0389] Step 3:

[0390] The robot receives instructions from the server and performs the specified tasks.

[0391] Input: Task instructions from the server.

[0392] Action: Based on the specifics of the task, the robot will initiate a movement, for example, bringing and serving food to the patient, guiding them to the restroom, or delivering food to a client's home.

[0393] Output: Execution of the specified task.

[0394] Step 4:

[0395] The robot monitors the progress of the task and the reactions of the patient / customer.

[0396] Input: Data from the robot's sensors (e.g., location information, operating status, facial expressions and voice of the patient / customer, etc.).

[0397] Operation: The robot sends its progress and collected sensor data to a server in real time.

[0398] Output: Progress reports and sensor data sent to the server.

[0399] Step 5:

[0400] Emotion recognition means analyzes the emotions of patients and customers.

[0401] Input: Patient / customer facial, voice, and speech patterns.

[0402] How it works: The emotion recognizer uses a generative AI model to analyze these inputs and identify emotions (e.g., "happy," "sad," "neutral," etc.).

[0403] Output: Identified emotion data.

[0404] Step 6:

[0405] The server adjusts its next instructions based on the received emotion data.

[0406] Input: Identified emotion data and progress reports.

[0407] How it works: References past data stored in the database and generates appropriate next instructions, such as instructing the robot to provide an encouraging message if the customer is identified as "sad."

[0408] Output: Adjusted next instruction.

[0409] Step 7:

[0410] The robot performs the next action based on the adjusted instructions.

[0411] Input: Coordinated instructions from the server.

[0412] Action: The robot follows new instructions and performs the required action. For example, if the customer is identified as "happy," the robot will say something like, "Enjoy your meal!"

[0413] Output: The next action that was performed.

[0414] This system will not only support hospital stays, but will also enable food delivery services to be tailored to the customer's emotions.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0416] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0421] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0423] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0425] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0426] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0427] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0428] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0430] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0431] This invention relates to a robot system that supports pediatric patients in hospital. This system includes a database for storing patient information, a server for acquiring patient information and sending instructions to the robot, a robot that provides assistance based on the instructions, and an analysis unit for monitoring and analyzing the working status and health condition and predicting the next action.

[0432] server

[0433] The server is a central computer system that manages patient information and communicates with the robot. The server maintains a database and stores patient information entered by the user (doctor). The server also provides an API (Application Program Interface) and receives requests from the robot and user terminals. This API enables the robot to obtain information, send instructions, and report work status.

[0434] Terminal (robot)

[0435] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from a server, the robot performs tasks such as serving meals, guiding them to the toilet, reading aloud, and supporting online classes. The robot also reports its own work status and the patient's health status to the server in real time, and these are recorded in a database.

[0436] User

[0437] The users, doctors and medical staff, input patient information through the server and instruct the robot on tasks. They can also check the patient's health condition and the robot's operating status from the server and issue new instructions as needed.

[0438] Detailed explanation of program processing

[0439] The server first stores patient information in a database, and the stored information is retrieved through an API when a user issues instructions to the robot. For example, if a user issues an instruction to feed a pediatric patient, the server sends the instruction to the robot.

[0440] When the robot receives the instruction, it starts the specified task (e.g., serving food). The robot constantly monitors the progress of the task and reports the results to the server in real time. The server records the received information in a database and, if necessary, analyzes the data using analytical means to predict the next action.

[0441] Specific examples

[0442] Example 1: Meal assistance

[0443] The user (doctor) enters new patient information and saves it in the database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in the database and uses analytical tools to plan the next task.

[0444] Example 2: Toilet Guidance

[0445] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0446] As described above, this invention provides a concrete means to support pediatric patients during hospitalization and reduce the burden on accompanying family members and medical staff. Furthermore, this system can monitor the patient's health status and task progress in real time, allowing for efficient continuous support.

[0447] The processing flow will be explained below.

[0448] Step 1:

[0449] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[0450] Step 2:

[0451] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[0452] Step 3:

[0453] Once the patient information is saved on the server, the user can call the server API to instruct the robot to perform a specific task (e.g., serving a meal). The server stores the patient ID and task information and prepares it to be sent to the robot.

[0454] Step 4:

[0455] The server sends instructions to the robot, specifically, the necessary task information (e.g., "Provide food for patient ID 1") to the terminal to which the robot is connected.

[0456] Step 5:

[0457] The terminal (robot) receives instructions from the server and starts executing the task. For example, the robot starts operating to deliver food along a set route.

[0458] Step 6:

[0459] The terminal (robot) constantly monitors the progress of the task and the patient's reaction. For example, it uses the robot's sensors to check the patient's satisfaction by checking the state of the meal.

[0460] Step 7:

[0461] The terminal (robot) reports the task completion status and ongoing status to the server in real time. Specifically, when a task is completed, the information is sent to the server.

[0462] Step 8:

[0463] The server records the received data in a database, including the timestamp of task completion and the patient's response.

[0464] Step 9:

[0465] The server uses the recorded data to perform analysis and predict the next action, for example, determining the best time to serve meals based on the data of multiple patients.

[0466] Step 10:

[0467] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user confirms this and prepares to send the next instruction to the robot.

[0468] Example 1

[0469] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0470] Pediatric patients' hospital stays place a heavy burden on medical staff and accompanying family members, and they often require assistance with health management and daily life. However, there is a lack of means to provide appropriate health management and support to patients while reducing these burdens. Therefore, there is a need for a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and families.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0472] In this invention, the server includes an interface means for a user to input patient information, a server means for storing the patient information input via the interface means in a database, and a server means for acquiring the patient information stored in the server means and sending instructions to the robot, thereby enabling the robot to assist pediatric patients by providing meals, guiding them to the toilet, reading to them, assisting them with online classes, or performing other tasks.

[0473] The "interface means for users to input patient information" refers to a device or software that allows doctors and medical staff to input basic patient information and store it in the system.

[0474] The "server means" is a central control device for storing patient information in a database and sending instructions to the robot.

[0475] A "database" is an information storage device for centrally managing a patient's basic information, medical history, allergy information, health status, etc.

[0476] The "robot means" is a mechanical device that provides assistance to pediatric patients based on instructions from the server.

[0477] "Patient information" refers to data necessary for health management, including the patient's name, age, medical history, allergy information, etc.

[0478] "Work status" refers to information that indicates the progress of the task being performed by the robot and the results of that task.

[0479] "Health status" refers to information about the patient's current health, including vital signs and dietary intake.

[0480] "Analysis means" refers to a device or software that analyzes the reported data from the robot and predicts and plans the next task.

[0481] "Predicting the next action" refers to planning the next assistance task that the robot should perform based on the data obtained by the analysis means.

[0482] This invention is a robot system for supporting pediatric patients in hospital. This system includes a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, and an analysis means for monitoring and analyzing the working status and health condition of the patient.

[0483] Server Means

[0484] The server is the central component of this system and fulfills several roles. First, it stores patient information entered by users (doctors and medical staff) in a database. The stored information is retrieved through an API (Application Program Interface) and used to send instructions to the robot.

[0485] Specifically, the user inputs and saves patient information through a dedicated interface, including the patient's name, age, medical history, allergy information, etc. The server stores this information in a database and sends instructions to the robot when necessary.

[0486] Robotic Means

[0487] The robotic means performs specific tasks for the pediatric patient based on instructions received from the server, such as providing meals, guiding the patient to the toilet, reading aloud, and supporting online classes.

[0488] The robot follows instructions received from the server and performs the specified task. For example, if the instruction is "serve lunch for patient A," the robot delivers the meal to the patient and provides support. Once the task is completed, the robot reports the work status and the patient's health condition to the server.

[0489] analytical means

[0490] The server receives the data reported by the robots and records it in a database. It then uses analytical tools to analyze this data and plan the next tasks, ensuring that the entire system always functions optimally and that the pediatric patient's hospital stay goes smoothly.

[0491] Specific examples

[0492] Examples of meal assistance include:

[0493] The user (doctor) enters Patient A's information and saves it in a database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in a database and uses analytical tools to plan the next task.

[0494] Prompt for the generative AI model:

[0495] "Please explain the specific steps involved in a robot delivering lunch to pediatric patients."

[0496] Examples of toilet guidance:

[0497] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0498] Prompt for the generative AI model:

[0499] "Please explain the specific processing steps of the toilet guidance robot."

[0500] As described above, the present invention provides a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and their families through cooperation between components.

[0501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] The user enters patient information.

[0504] Users (doctors and medical staff) use a dedicated interface to enter basic patient information (such as name, age, medical history, allergies, etc.) The data is then stored on the front end and prepared for transmission to the server, where it is correctly formatted and sent to the server.

[0505] Step 2:

[0506] The server stores the patient information in a database.

[0507] The server receives patient information submitted by the user. The received data goes through a data validation process before being stored in the database. If validation is successful, the server saves the data to the database, specifically by adding it as a record to the appropriate table in the database.

[0508] Step 3:

[0509] The user gives instructions to the robot.

[0510] Through the interface, the user sends instructions to the server specifying tasks for a specific patient (e.g., providing food, guiding the patient to the toilet, etc.). This instruction includes detailed information about the task to be performed. The server accepts this instruction and prepares it to be sent to the corresponding robot.

[0511] Step 4:

[0512] The server sends instructions to the robot.

[0513] The server receives instructions from the user, converts them into the appropriate format, and sends them to the robot's API endpoint. These instructions contain details of the task to be performed and information about the patient. The data sent becomes instructions that the robot can interpret and execute the task.

[0514] Step 5:

[0515] The robot receives instructions and performs the task.

[0516] The robot analyzes instructions received from the server and carries out the specified task. For example, if the instruction is "serve lunch for patient A," the robot prepares the meal and delivers it to the patient's room. The robot uses sensors to monitor its progress and the patient's response.

[0517] Step 6:

[0518] The robot reports its work status and health status to the server.

[0519] The robot monitors the progress of the tasks it is performing and the patient's health status in real time and reports the information to the server. The reported data includes the task completion status and the patient's vital signs. This data is sent through the server's API.

[0520] Step 7:

[0521] The server records the report in a database.

[0522] The server validates the reported data received from the robot and stores it in a database. The stored information serves as the patient's health history. The storage procedure includes verifying the accuracy of the data and adding records to the appropriate tables.

[0523] Step 8:

[0524] The server plans the next task.

[0525] The server analyzes the information recorded in the database and plans the next task to be executed. Using analytical means, it determines the optimal next task based on past data and the current situation. The planned task is again notified to the user or robot, and preparations for execution are made.

[0526] (Application example 1)

[0527] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] In modern logistics centers, tasks such as product picking, packing, inventory management, and transportation still require a large labor force. While high efficiency is required, the workload of workers is a major issue. Real-time information management is also essential to ensure the accuracy and speed of operations. However, conventional systems have difficulty resolving these issues efficiently and comprehensively. The present invention aims to solve these issues, improve the efficiency of operations within logistics centers, and reduce the labor burden.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0530] In this invention, the server includes database means for storing patient information, server means for acquiring the patient information stored in the database means and sending instructions to the robot, robot means for assisting pediatric patients, analysis means for monitoring the working status of the robot means and the health condition of the patient and analyzing it to predict the next action, database means for applying the system to a logistics center and storing product information, server means for acquiring the product information stored in the database means and sending instructions to the robot, and robot means for picking and packing products. This enables efficient and accurate picking, packing, inventory management, and transportation of products in the logistics center.

[0531] Definition of Terms

[0532] "Pediatric patient" refers to a child between the ages of birth and 18 years who requires appropriate medical care.

[0533] "Hospitalization" refers to the period of receiving medical treatment and care in a hospital or medical facility, which can last from short to long periods of time.

[0534] A "robotic system" refers to a system that includes a mechanical device for automatically performing a specific task and the infrastructure for controlling and communicating with it.

[0535] "Patient information" refers to a collective term for data such as personal information, medical history, health status, and treatment plans of pediatric patients.

[0536] "Database means" refers to a system for electronically collecting, storing and managing information.

[0537] "Server Means" refers to a system that includes a central processing unit for accessing Database Means and communicating with other devices and systems.

[0538] "Instructions" refer to specific instructions for performing a particular task.

[0539] "Robotic Means" refers to an automatically controlled mechanical device for performing tasks based on instructions received from Server Means.

[0540] "Work Status" refers to the progress or status reports of a robotic means as it performs a task.

[0541] "Health status" refers to the overall physical and mental state of a patient.

[0542] "Analysis means" refers to tools and systems that have the functionality to analyze collected data and predict next actions.

[0543] "Logistics center" refers to a facility that stores, manages, and distributes goods and materials.

[0544] "Product information" refers to data such as the type, quantity, location, and condition of products within a logistics center.

[0545] "Picking" refers to the task of removing specified items from a specific location in the warehouse.

[0546] "Packing" refers to the process of placing goods into suitable packaging or packaging materials.

[0547] "Inventory control" refers to the process of monitoring and controlling the quantity and condition of stored goods.

[0548] "Transportation" refers to the act of moving goods from one place to another.

[0549] MODE FOR CARRYING OUT THE INVENTION

[0550] System Overview

[0551] This article describes a robotic system that automates the picking, packing, inventory management, and transportation of goods in a distribution center. This system includes the following main components:

[0552] Database Means

[0553] Server Means

[0554] Robotic Means

[0555] analytical means

[0556] Explanation of program processing

[0557] Hardware and software used

[0558] The implementation of this system uses the following hardware and software:

[0559] Hardware: Transport robots (e.g., automated transport equipment and robotic arms)

[0560] Software: Central server (providing API), database management system, robot control program (implemented in Python, etc.)

[0561] Data processing and calculation

[0562] 1. Product information management

[0563] The server stores product information in a database, which includes product type, quantity, location, condition, etc.

[0564] 2. Obtaining and Sending Instructions

[0565] The server means retrieves the product information stored in the database means and sends instructions to the robot means, including the location from which a specific product should be picked, the packing item, and the delivery location.

[0566] 3. Execute the task

[0567] The robotic means executes tasks based on instructions from the server. For example, in the case of picking, the robot picks up a specified item from a specific location in the warehouse and moves it to an area for packing.

[0568] 4. Work Status Report

[0569] The robot means reports the work status and inventory status to the server in real time. The server records the information received in real time in the database means, analyzes the data using the analysis means as needed, and predicts the next action.

[0570] Specific examples

[0571] For example, if a user sends a picking instruction for product ID "123456" to the server, the server sends this information to the robot. The robot picks product ID "123456" from the specified location in the warehouse, and after completing the task, reports to the server that picking of "123456" is complete. The server then instructs the robot on the next task based on that report.

[0572] Example prompts for generative AI models

[0573] Server: Please send the current warehouse status and product picking instructions in the following format.

[0574] User: Please pick the following item ID 123456 from Aisle 3, Shelf 5.

[0575] This system enables efficient and accurate picking, packing, inventory management, and transportation of products within the logistics center, ensuring speed and accuracy of work while also reducing the labor burden.

[0576] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0577] Specific flow of program processing

[0578] Processing Step Description

[0579] Step 1: Store product information in a database

[0580] Input: The user enters product information (product ID, type, quantity, location, and condition).

[0581] Specific operations: The server receives the product information and stores the information in the database means.

[0582] Output: Product details saved in database.

[0583] Step 2: Sending instructions from the server to the robot

[0584] Input: The server receives picking and packing instructions from the user (product ID, location, detailed work instructions).

[0585] Specific operation: The server accesses the database means to obtain the relevant product information and instructs the robot to perform a specific task (e.g., picking, packing, transporting).

[0586] Output: A task is sent to the robot.

[0587] Step 3: Robot picking the items

[0588] Input: The robot receives a picking instruction from the server (e.g., pick product ID "123456" from Aisle 3, Shelf 5).

[0589] Specific operation: The robot moves to the specified location in the warehouse and picks the corresponding item.

[0590] Output: The picking task is completed and the result is stored in temporary memory.

[0591] Step 4: Robot packs the products

[0592] Input: The robot next receives packing instructions.

[0593] Specific operation: The robot moves the picked items to the packing area and packs the items using appropriate packaging materials.

[0594] Output: The packing task is completed and the result is stored in temporary memory.

[0595] Step 5: Reporting work status and inventory status

[0596] Input: The robot reports the results of the completed picking or packing task to the server, specifically, which items were moved to where, and whether packing is complete.

[0597] Specific operation: The robot sends details of the work status and inventory data to the server in real time.

[0598] Output: The server records the received information in a database and updates the real-time inventory status.

[0599] Step 6: Plan and instruct next steps

[0600] Input: The server receives the latest inventory information and work status recorded in the database.

[0601] What it does: The server uses analytics to analyze the data, plan the next best task, and then send new instructions to the robot.

[0602] Output: A new task is sent to the robot and it starts its next task.

[0603] These processing steps enable efficient and accurate picking, packing, inventory management, and transportation of goods within the logistics center.

[0604] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0605] This invention relates to a robot system that supports pediatric patients in hospital. This system is composed of a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, an analysis means for monitoring, analyzing, and predicting the work status and the patient's health condition, and an emotion engine for recognizing the user's emotions.

[0606] server

[0607] The server is a central computer system that manages patient information and communicates with the robot and emotion engine. The server maintains a database and stores patient information entered by the user (doctor). It also receives input from the emotion engine and adjusts instructions to the robot.

[0608] The server provides an API (Application Program Interface) and accepts requests from robots and emotion engines, allowing them to obtain information, send instructions, and report on their work status.

[0609] Terminal (robot)

[0610] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from the server, the robot performs tasks such as serving meals, guiding them to the toilet, reading to them, and supporting them in online classes. The robot can also adjust its assistance methods based on emotional data from an emotion engine. For example, if a patient is feeling stressed, the robot will respond by speaking to them gently.

[0611] The added emotion engine analyzes the facial expressions, voice, and speech patterns of users and patients via the robot's sensors to identify their emotions. The identified emotion data is immediately sent to the server and used to adjust the next instructions.

[0612] User

[0613] The user, a doctor or medical staff member, inputs patient information through the server and instructs the robot on tasks. The server can also check the patient's health condition, the robot's operating status, and even emotion data from the emotion engine, and issue new instructions as needed.

[0614] Detailed explanation of program processing

[0615] server

[0616] The server first stores patient information in a database. This information is obtained through an API when the user issues commands to the robot. The server also receives emotion data sent from the emotion engine and stores it in the database.

[0617] Terminal (robot)

[0618] The robot carries out tasks when it receives instructions from the server. For example, if it is instructed to serve a meal, the robot will carry the meal and serve it to the patient. The robot also monitors the progress of the task and the patient's reactions, and continuously sends this data to the server. If there is input from the emotion engine, it adjusts the assistance it provides based on that input.

[0619] Emotion Engine

[0620] The emotion engine recognizes emotions by analyzing the facial expressions, tone of voice, and language patterns of the user and pediatric patient. For example, if it identifies emotions such as stress or anxiety, it sends the data to the server. The server stores the received emotion data in a database and adjusts the next task or assistance method based on the data.

[0621] Specific examples

[0622] Example 1: Meal assistance

[0623] The user (doctor) inputs new patient information and saves it in the database. The user then sends meal preparation instructions for a specific pediatric patient to the server, which forwards the instructions to the robot. The robot uses an emotion engine to analyze the patient's facial expressions and tone of voice, ensuring that the patient is relaxed while preparing the meal. After completing the task, the robot reports its progress and emotion data to the server, which records the information in the database.

[0624] Example 2: Toilet Guidance

[0625] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server then forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides the necessary support. During this process, if the emotion engine detects anxiety or stress in the patient, the robot will respond by offering comforting words. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[0626] This invention provides a concrete means to support pediatric patients' hospital stays and reduce the burden on accompanying family members and medical staff. In addition, by combining it with an emotion engine, it is possible to provide detailed assistance according to the patient's emotional state, improving the quality of hospital stays.

[0627] The processing flow will be explained below.

[0628] Step 1:

[0629] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[0630] Step 2:

[0631] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[0632] Step 3:

[0633] The user sends an instruction to the server to perform a specific task (e.g., serving a meal), and the server associates the patient information with the task information.

[0634] Step 4:

[0635] The server sends task instructions to the robot. Specifically, it communicates the instruction "Provide food for patient ID 1" to the robot's terminal.

[0636] Step 5:

[0637] The terminal (robot) receives instructions from the server and prepares to perform the specified task (serving food).

[0638] Step 6:

[0639] The terminal (robot) activates an emotion engine that analyzes the facial expressions, voice, and speech patterns of the pediatric patient. The emotion engine identifies the patient's emotions and transmits the data to the server in real time.

[0640] Step 7:

[0641] The device (robot) adapts the way it performs tasks based on the identified emotional data. For example, if a patient is nervous, the robot can calm them down by speaking to them gently while providing a meal.

[0642] Step 8:

[0643] The terminal (robot) constantly monitors the progress of the task and the patient's reaction (emotional data), and reports the situation to the server as needed.

[0644] Step 9:

[0645] The server records the received task progress and emotion data in a database, specifically, saving the task completion timestamp including data from the emotion engine.

[0646] Step 10:

[0647] The server analyzes the information in the database and predicts the next task or assistance needed. For example, if it determines that the patient is relaxed while eating, it uses the same approach for the next meal.

[0648] Step 11:

[0649] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user checks this and prepares to send new instructions to the robot if necessary.

[0650] In this way, we support pediatric patients in their hospital stay, reduce the burden on accompanying family members and medical staff, and provide detailed care that is tailored to the patient's emotional state.

[0651] Example 2

[0652] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] Conventional robotic systems have not been able to fully realize the attentive care required to improve the quality of life for pediatric patients in hospital. In particular, the lack of a system that can recognize the patient's emotional state in real time and respond accordingly makes it difficult to reduce the patient's mental burden. Furthermore, there is a lack of a means to centrally manage work status and emotional data, which is one of the reasons for the increased burden on medical staff.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a database means for storing patient information, a server means for acquiring the patient information stored in the database means and sending instructions to the robot, a robot means for providing care to the pediatric patient based on instructions from the server means, an analysis means for monitoring and analyzing the robot means' work status and the patient's health condition to predict the robot's next action, and an emotion recognition engine for acquiring emotion data and adjusting the care method based on the data. This allows the system to recognize the patient's emotional state in real time and provide appropriate responses, thereby reducing the mental and physical burden on pediatric patients and improving their quality of life during hospitalization. Furthermore, centralized management of work status and emotion data reduces the burden on medical staff and enables more efficient care.

[0655] "Patient information" refers to detailed data about a patient, such as basic personal information, dietary restrictions, allergy information, and special care needs.

[0656] "Database Means" refers to an electronic record device that stores patient information and allows it to be retrieved when needed.

[0657] "Server means" is a central computer system for obtaining patient information and sending instructions to the robot.

[0658] The "robot means" is a mechanical device that provides care to a pediatric patient based on instructions from the server means.

[0659] The "analysis means" is a function for monitoring the working status of the robot means and the health condition of the patient, analyzing this data, and predicting the next action.

[0660] An "emotion recognition engine" is an algorithm or device that analyzes data obtained through sensors in a robotic means to recognize and identify the emotional state of a patient.

[0661] "API" is an abbreviation for Application Program Interface, which allows the server means to accept requests from the robot means and emotion recognition engine, and to obtain information and send instructions.

[0662] An "assistance method" is a specific method of support provided by a robotic means for a specific patient need.

[0663] A "sensor" is a device that allows a robotic means to sense external information (for example, facial expressions or voice) and record it as data.

[0664] "Real-time" refers to data collection and processing occurring almost instantly, with minimal delay.

[0665] "Health status" refers to the overall state of a patient, including their physical condition and medical condition.

[0666] The present invention relates to a robotic system that supports pediatric patients during their hospital stay. This system operates by combining multiple hardware and software components, aiming to provide efficient and effective patient care.

[0667] Hardware

[0668] Server: A central computer system that interfaces with the database, APIs, analytics, and emotion recognition engine.

[0669] Terminal (Robot): A mechanical device that provides direct care to pediatric patients. It is equipped with sensors and communicates with an emotion recognition engine.

[0670] Database: An electronic record for storing patient information and affective data.

[0671] software

[0672] API: An interface through which the server accepts requests from the robot and emotion recognition engine, obtains information, and sends instructions.

[0673] Emotion Recognition Engine: Contains algorithms that analyze facial expressions, tone of voice, and language patterns to identify emotions.

[0674] Analysis means: Has the ability to monitor the robot's working status and the patient's health condition and predict its next actions.

[0675] Overall system processing flow

[0676] The user, a doctor or medical staff member, inputs patient information into the server using a dedicated terminal. This information is sent to the server and stored in a database. The user then sends instructions for specific tasks (e.g., serving meals or guiding patients to the toilet) through the server.

[0677] The server then forwards the instructions to the robot via an API. The robot uses sensors to collect real-time patient data and analyzes the emotional data through an emotion recognition engine. This emotional data is then sent back to the server to adjust the next instructions as needed.

[0678] Specific examples

[0679] Example 1: Meal assistance

[0680] 1. The user (doctor) enters new patient information and saves it in the database.

[0681] 2. The user sends meal delivery instructions to the server for a specific pediatric patient.

[0682] 3. The server forwards the instructions to the robot.

[0683] 4. The robot uses an emotion recognition engine to analyze the patient's facial expressions and tone of voice, ensuring they are relaxed while serving food.

[0684] 5. After completing the task, the robot reports its progress and emotional data to the server, which records the information in a database.

[0685] Example prompt:

[0686] "You've entered new patient information into a database and sent an API request to instruct the robot to serve the meal. Now explain how the system works so the robot receives the instruction and begins working."

[0687] Example 2: Toilet Guidance

[0688] 1. The user sends toileting assistance instructions for a specific pediatric patient to the server.

[0689] 2. The server forwards the instructions to the robot.

[0690] 3. The robot follows the instructions, safely guides the patient to the toilet and provides any necessary assistance.

[0691] 4. During this process, if the emotion recognition engine detects the patient's anxiety or stress, the robot will respond by offering kind words of encouragement.

[0692] 5. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[0693] Example prompt:

[0694] "The robot has been instructed to serve a meal. Please explain the specific flow of how to provide assistance while checking the patient's condition using the emotion recognition engine."

[0695] This invention provides a concrete means for improving the quality of hospitalized pediatric patients' lives and reducing the burden on medical staff. By combining it with an emotion recognition engine, it is possible to provide detailed assistance according to the patient's emotional state, making hospitalization more comfortable.

[0696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0697] Step 1:

[0698] Entering and saving patient information

[0699] The user enters patient information into the server from a dedicated terminal. This information includes basic personal information, dietary restrictions, allergies, and special care needs. The server stores the entered patient information in a database. The entered data is validated to ensure that the information is properly formatted. If it is not, an error message is returned.

[0700] Input: Patient information (personal information, dietary restrictions, allergy information, special care details)

[0701] Output: Patient information correctly saved in the database

[0702] Step 2:

[0703] Start of meal serving task

[0704] The user sends instructions for serving food to the server, which then forwards the instructions to the robot via API.

[0705] The server generates an API request containing the order code and associated patient ID and sends it to the robot.

[0706] Input: Meal instructions from user, associated patient ID

[0707] Output: API request to the robot

[0708] Step 3:

[0709] Executing meal serving tasks

[0710] The terminal (robot) receives instructions from the server. Based on the received instructions, the robot delivers meals from the kitchen to the patient's room. Sensors are used to collect the patient's facial expressions and tone of voice in real time.

[0711] Input: Meal provision instructions from the server, patient ID

[0712] Output: Started meal serving task, collected sensor data

[0713] Step 4:

[0714] Real-time monitoring and data reporting

[0715] The terminal (robot) monitors the patient's reactions while serving the meal, using sensors to collect the patient's facial expressions and tone of voice, and sends the data to a server in real time.

[0716] The server receives the emotion data sent in real time and stores it in a database, while also monitoring the robot's work status in real time.

[0717] Input: Sensor data sent from the robot

[0718] Output: Emotion data stored in the database, robot work status

[0719] Step 5:

[0720] Emotional data analysis and response

[0721] The emotion recognition engine analyzes data from the robot's sensors, analyzing facial expressions, tone of voice, and language patterns to identify emotions, and sends the results to a server.

[0722] The server determines whether further instructions are needed based on the emotional data and sends new instructions to the robot if necessary.

[0723] Input: Sensor data analysis results by emotion recognition engine

[0724] Output: Analysis results and new instructions sent to the server

[0725] Step 6:

[0726] Task completion reporting and data recording

[0727] The terminal (robot) reports to the server that the meal has been served, and the server stores the received report and emotion data in a database.

[0728] Input: Task completion report from the robot, emotion data

[0729] Output: Task completion information and emotion data recorded in a database

[0730] (Application example 2)

[0731] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0732] The present invention relates to a robot system that supports pediatric patients' hospital stays and a food delivery support system that takes into account customer emotions. Traditionally, pediatric patients' hospital stays have placed a heavy burden on medical staff and their families, and the patients themselves have often experienced anxiety and stress. Food delivery services have also been problematic in that customers experience discomfort and stress when receiving their food. In light of these circumstances, there is a need for a system that can improve the quality of hospital stays and increase customer satisfaction with delivery services.

[0733] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0734] In this invention, the server includes a database means for storing and processing patient information, a server means for acquiring the patient information and sending instructions to the robot, and an analysis means for predicting the next action by monitoring and analyzing the work status and the patient's health condition received from the robot means, thereby enabling support for the patient's hospital stay and customer care during delivery services.

[0735] "Pediatric patient" refers to a patient admitted to a hospital during childhood.

[0736] "Hospitalization" refers to the period of time spent in a hospital room while receiving medical and nursing services provided by the hospital.

[0737] "Patient Information" refers to information about a patient, including their medical history, current health status, allergy information, preferences, and medical needs.

[0738] "Database means" refers to a system or device for storing, updating, and managing information.

[0739] "Server means" refers to a computer system that communicates with multiple terminals via a network and processes instructions and data within the server.

[0740] "Robotic means" refers to a mechanical device controlled by a program and designed to automatically perform designated tasks.

[0741] "Analysis means" refers to a device or system for analyzing collected data and predicting future actions.

[0742] "Emotion recognition means" refers to a device or program for identifying emotions from a user's facial expressions and voice.

[0743] "Reporting means" refers to a device or program that collects and transmits data to a server.

[0744] "Food delivery" refers to a service that delivers food and drinks to customers who order them.

[0745] "Customer preferences" refers to individual preference information such as the customer's preferred dishes, ingredients, and allergy information.

[0746] "Appropriate action" refers to taking the most appropriate action in a given situation to improve patient or customer satisfaction.

[0747] This invention provides a robot system that supports pediatric patients in hospital and takes into account the emotions of customers in food delivery services. This system is composed of a database means for storing patient and customer information, a server means for sending instructions for assistance and delivery to the robot, a robot means for providing assistance and delivery based on the instructions, an analysis means for monitoring and analyzing the work situation and emotional state, an emotion recognition means, and a reporting means.

[0748] The server first stores patient and customer information in a database and then obtains necessary information via the database means. This information is obtained through the API when issuing instructions to the robot. The server also receives emotion data sent from the emotion recognition means and stores it in the database.

[0749] The robot carries out a task when it receives an instruction from the server. For example, if it receives an instruction to serve a meal to a pediatric patient, the robot carries out the task, delivers the meal, and serves it to the patient. Similarly, in a food delivery service, the robot delivers the meal to a customer's home. The robot monitors the progress of the task and the customer's reaction, and continuously sends this data to the server.

[0750] The emotion recognition means analyzes the facial expressions, voice, and speech patterns of the user or customer to identify their emotions. For example, if a customer is smiling, it can identify "happy," and if they are feeling stressed, it can identify "sad." The identified emotion data is immediately sent to the server and used to adjust the next task or assistance method.

[0751] As a concrete example, consider the following scenario:

[0752] Update new customer information

[0753] Doctors and medical staff input new patient and customer information and store it in a database. This updated information allows the robot to provide appropriate assistance and delivery.

[0754] Example prompt sentence:

[0755] "Please update new customer information. Customer ID is 'new_customer_1' and preferences are 'gluten-free' and 'no dairy'."

[0756] Performing sentiment analysis

[0757] When the robot interacts with patients or customers, it analyzes the voice input text using emotion recognition to identify emotions, which is important for responding appropriately to the patient or customer's emotions.

[0758] Example prompt sentence:

[0759] "Analyze the sentiment for customer ID 'customer_id_1'. Input text is 'I'm so happy to receive my meal quickly!'"

[0760] Delivery food delivery

[0761] When a food delivery robot delivers a meal to a customer's home, it can take the customer's emotions into account and respond appropriately. For example, if the customer is feeling stressed, the robot will provide an encouraging message such as, "I hope this meal cheers you up!"

[0762] Example prompt sentence:

[0763] "Please execute a delivery response based on the sentiment of customer ID 'customer_id_1'."

[0764] With these functions, the present invention not only supports pediatric patients' hospital stays and reduces the burden on medical staff and their families, but also improves customer satisfaction in food delivery services.

[0765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0766] Step 1:

[0767] A user enters new patient or customer information into the server.

[0768] Input: Patient or customer information (e.g., patient ID, name, preferences, allergy information, etc.)

[0769] Operation: The server receives the information entered by the user and stores it in a database means.

[0770] Output: Updated patient and customer information in the database.

[0771] Step 2:

[0772] The server sends instructions to the robot based on the specified task.

[0773] Input: Type of task (e.g., meal service, toilet assistance, food delivery, etc.) and relevant patient or customer information.

[0774] Operation: The server sends instructions to the robotic means through the API, providing details of the corresponding tasks.

[0775] Output: Task instructions sent to the robot.

[0776] Step 3:

[0777] The robot receives instructions from the server and performs the specified tasks.

[0778] Input: Task instructions from the server.

[0779] Action: Based on the specifics of the task, the robot will initiate a movement, for example, bringing and serving food to the patient, guiding them to the restroom, or delivering food to a client's home.

[0780] Output: Execution of the specified task.

[0781] Step 4:

[0782] The robot monitors the progress of the task and the reactions of the patient / customer.

[0783] Input: Data from the robot's sensors (e.g., location information, operating status, facial expressions and voice of the patient / customer, etc.).

[0784] Operation: The robot sends its progress and collected sensor data to a server in real time.

[0785] Output: Progress reports and sensor data sent to the server.

[0786] Step 5:

[0787] Emotion recognition means analyzes the emotions of patients and customers.

[0788] Input: Patient / customer facial, voice, and speech patterns.

[0789] How it works: The emotion recognizer uses a generative AI model to analyze these inputs and identify emotions (e.g., "happy," "sad," "neutral," etc.).

[0790] Output: Identified emotion data.

[0791] Step 6:

[0792] The server adjusts its next instructions based on the received emotion data.

[0793] Input: Identified emotion data and progress reports.

[0794] How it works: References past data stored in the database and generates appropriate next instructions, such as instructing the robot to provide an encouraging message if the customer is identified as "sad."

[0795] Output: Adjusted next instruction.

[0796] Step 7:

[0797] The robot performs the next action based on the adjusted instructions.

[0798] Input: Coordinated instructions from the server.

[0799] Action: The robot follows new instructions and performs the required action. For example, if the customer is identified as "happy," the robot will say something like, "Enjoy your meal!"

[0800] Output: The next action that was performed.

[0801] This system will not only support hospital stays, but will also enable food delivery services to be tailored to the customer's emotions.

[0802] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0803] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0804] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0805] [Third embodiment]

[0806] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0807] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0808] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0809] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0810] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0811] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0812] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0813] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0814] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0815] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0816] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0817] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0818] This invention relates to a robot system that supports pediatric patients in hospital. This system includes a database for storing patient information, a server for acquiring patient information and sending instructions to the robot, a robot that provides assistance based on the instructions, and an analysis unit for monitoring and analyzing the working status and health condition and predicting the next action.

[0819] server

[0820] The server is a central computer system that manages patient information and communicates with the robot. The server maintains a database and stores patient information entered by the user (doctor). The server also provides an API (Application Program Interface) and receives requests from the robot and user terminals. This API enables the robot to obtain information, send instructions, and report work status.

[0821] Terminal (robot)

[0822] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from a server, the robot performs tasks such as serving meals, guiding them to the toilet, reading aloud, and supporting online classes. The robot also reports its own work status and the patient's health status to the server in real time, and these are recorded in a database.

[0823] User

[0824] The users, doctors and medical staff, input patient information through the server and instruct the robot on tasks. They can also check the patient's health condition and the robot's operating status from the server and issue new instructions as needed.

[0825] Detailed explanation of program processing

[0826] The server first stores patient information in a database, and the stored information is retrieved through an API when a user issues instructions to the robot. For example, if a user issues an instruction to feed a pediatric patient, the server sends the instruction to the robot.

[0827] When the robot receives the instruction, it starts the specified task (e.g., serving food). The robot constantly monitors the progress of the task and reports the results to the server in real time. The server records the received information in a database and, if necessary, analyzes the data using analytical means to predict the next action.

[0828] Specific examples

[0829] Example 1: Meal assistance

[0830] The user (doctor) enters new patient information and saves it in the database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in the database and uses analytical tools to plan the next task.

[0831] Example 2: Toilet Guidance

[0832] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0833] As described above, this invention provides a concrete means to support pediatric patients during hospitalization and reduce the burden on accompanying family members and medical staff. Furthermore, this system can monitor the patient's health status and task progress in real time, allowing for efficient continuous support.

[0834] The processing flow will be explained below.

[0835] Step 1:

[0836] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[0837] Step 2:

[0838] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[0839] Step 3:

[0840] Once the patient information is saved on the server, the user can call the server API to instruct the robot to perform a specific task (e.g., serving a meal). The server stores the patient ID and task information and prepares it to be sent to the robot.

[0841] Step 4:

[0842] The server sends instructions to the robot, specifically, the necessary task information (e.g., "Provide food for patient ID 1") to the terminal to which the robot is connected.

[0843] Step 5:

[0844] The terminal (robot) receives instructions from the server and starts executing the task. For example, the robot starts operating to deliver food along a set route.

[0845] Step 6:

[0846] The terminal (robot) constantly monitors the progress of the task and the patient's reaction. For example, it uses the robot's sensors to check the patient's satisfaction by checking the state of the meal.

[0847] Step 7:

[0848] The terminal (robot) reports the task completion status and ongoing status to the server in real time. Specifically, when a task is completed, the information is sent to the server.

[0849] Step 8:

[0850] The server records the received data in a database, including the timestamp of task completion and the patient's response.

[0851] Step 9:

[0852] The server uses the recorded data to perform analysis and predict the next action, for example, determining the best time to serve meals based on the data of multiple patients.

[0853] Step 10:

[0854] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user confirms this and prepares to send the next instruction to the robot.

[0855] Example 1

[0856] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0857] Pediatric patients' hospital stays place a heavy burden on medical staff and accompanying family members, and they often require assistance with health management and daily life. However, there is a lack of means to provide appropriate health management and support to patients while reducing these burdens. Therefore, there is a need for a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and families.

[0858] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0859] In this invention, the server includes an interface means for a user to input patient information, a server means for storing the patient information input via the interface means in a database, and a server means for acquiring the patient information stored in the server means and sending instructions to the robot, thereby enabling the robot to assist pediatric patients by providing meals, guiding them to the toilet, reading to them, assisting them with online classes, or performing other tasks.

[0860] The "interface means for users to input patient information" refers to a device or software that allows doctors and medical staff to input basic patient information and store it in the system.

[0861] The "server means" is a central control device for storing patient information in a database and sending instructions to the robot.

[0862] A "database" is an information storage device for centrally managing a patient's basic information, medical history, allergy information, health status, etc.

[0863] The "robot means" is a mechanical device that provides assistance to pediatric patients based on instructions from the server.

[0864] "Patient information" refers to data necessary for health management, including the patient's name, age, medical history, allergy information, etc.

[0865] "Work status" refers to information that indicates the progress of the task being performed by the robot and the results of that task.

[0866] "Health status" refers to information about the patient's current health, including vital signs and dietary intake.

[0867] "Analysis means" refers to a device or software that analyzes the reported data from the robot and predicts and plans the next task.

[0868] "Predicting the next action" refers to planning the next assistance task that the robot should perform based on the data obtained by the analysis means.

[0869] This invention is a robot system for supporting pediatric patients in hospital. This system includes a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, and an analysis means for monitoring and analyzing the working status and health condition of the patient.

[0870] Server Means

[0871] The server is the central component of this system and fulfills several roles. First, it stores patient information entered by users (doctors and medical staff) in a database. The stored information is retrieved through an API (Application Program Interface) and used to send instructions to the robot.

[0872] Specifically, the user inputs and saves patient information through a dedicated interface, including the patient's name, age, medical history, allergy information, etc. The server stores this information in a database and sends instructions to the robot when necessary.

[0873] Robotic Means

[0874] The robotic means performs specific tasks for the pediatric patient based on instructions received from the server, such as providing meals, guiding the patient to the toilet, reading aloud, and supporting online classes.

[0875] The robot follows instructions received from the server and performs the specified task. For example, if the instruction is "serve lunch for patient A," the robot delivers the meal to the patient and provides support. Once the task is completed, the robot reports the work status and the patient's health condition to the server.

[0876] analytical means

[0877] The server receives the data reported by the robots and records it in a database. It then uses analytical tools to analyze this data and plan the next tasks, ensuring that the entire system always functions optimally and that the pediatric patient's hospital stay goes smoothly.

[0878] Specific examples

[0879] Examples of meal assistance include:

[0880] The user (doctor) enters Patient A's information and saves it in a database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in a database and uses analytical tools to plan the next task.

[0881] Prompt for the generative AI model:

[0882] "Please explain the specific steps involved in a robot delivering lunch to pediatric patients."

[0883] Examples of toilet guidance:

[0884] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[0885] Prompt for the generative AI model:

[0886] "Please explain the specific processing steps of the toilet guidance robot."

[0887] As described above, the present invention provides a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and their families through cooperation between components.

[0888] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0889] Step 1:

[0890] The user enters patient information.

[0891] Users (doctors and medical staff) use a dedicated interface to enter basic patient information (such as name, age, medical history, allergies, etc.) The data is then stored on the front end and prepared for transmission to the server, where it is correctly formatted and sent to the server.

[0892] Step 2:

[0893] The server stores the patient information in a database.

[0894] The server receives patient information submitted by the user. The received data goes through a data validation process before being stored in the database. If validation is successful, the server saves the data to the database, specifically by adding it as a record to the appropriate table in the database.

[0895] Step 3:

[0896] The user gives instructions to the robot.

[0897] Through the interface, the user sends instructions to the server specifying tasks for a specific patient (e.g., providing food, guiding the patient to the toilet, etc.). This instruction includes detailed information about the task to be performed. The server accepts this instruction and prepares it to be sent to the corresponding robot.

[0898] Step 4:

[0899] The server sends instructions to the robot.

[0900] The server receives instructions from the user, converts them into the appropriate format, and sends them to the robot's API endpoint. These instructions contain details of the task to be performed and information about the patient. The data sent becomes instructions that the robot can interpret and execute the task.

[0901] Step 5:

[0902] The robot receives instructions and performs the task.

[0903] The robot analyzes instructions received from the server and carries out the specified task. For example, if the instruction is "serve lunch for patient A," the robot prepares the meal and delivers it to the patient's room. The robot uses sensors to monitor its progress and the patient's response.

[0904] Step 6:

[0905] The robot reports its work status and health status to the server.

[0906] The robot monitors the progress of the tasks it is performing and the patient's health status in real time and reports the information to the server. The reported data includes the task completion status and the patient's vital signs. This data is sent through the server's API.

[0907] Step 7:

[0908] The server records the report in a database.

[0909] The server validates the reported data received from the robot and stores it in a database. The stored information serves as the patient's health history. The storage procedure includes verifying the accuracy of the data and adding records to the appropriate tables.

[0910] Step 8:

[0911] The server plans the next task.

[0912] The server analyzes the information recorded in the database and plans the next task to be executed. Using analytical means, it determines the optimal next task based on past data and the current situation. The planned task is again notified to the user or robot, and preparations for execution are made.

[0913] (Application example 1)

[0914] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0915] In modern logistics centers, tasks such as product picking, packing, inventory management, and transportation still require a large labor force. While high efficiency is required, the workload of workers is a major issue. Real-time information management is also essential to ensure the accuracy and speed of operations. However, conventional systems have difficulty resolving these issues efficiently and comprehensively. The present invention aims to solve these issues, improve the efficiency of operations within logistics centers, and reduce the labor burden.

[0916] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0917] In this invention, the server includes database means for storing patient information, server means for acquiring the patient information stored in the database means and sending instructions to the robot, robot means for assisting pediatric patients, analysis means for monitoring the working status of the robot means and the health condition of the patient and analyzing it to predict the next action, database means for applying the system to a logistics center and storing product information, server means for acquiring the product information stored in the database means and sending instructions to the robot, and robot means for picking and packing products. This enables efficient and accurate picking, packing, inventory management, and transportation of products in the logistics center.

[0918] Definition of Terms

[0919] "Pediatric patient" refers to a child between the ages of birth and 18 years who requires appropriate medical care.

[0920] "Hospitalization" refers to the period of receiving medical treatment and care in a hospital or medical facility, which can last from short to long periods of time.

[0921] A "robotic system" refers to a system that includes a mechanical device for automatically performing a specific task and the infrastructure for controlling and communicating with it.

[0922] "Patient information" refers to a collective term for data such as personal information, medical history, health status, and treatment plans of pediatric patients.

[0923] "Database means" refers to a system for electronically collecting, storing and managing information.

[0924] "Server Means" refers to a system that includes a central processing unit for accessing Database Means and communicating with other devices and systems.

[0925] "Instructions" refer to specific instructions for performing a particular task.

[0926] "Robotic Means" refers to an automatically controlled mechanical device for performing tasks based on instructions received from Server Means.

[0927] "Work Status" refers to the progress or status reports of a robotic means as it performs a task.

[0928] "Health status" refers to the overall physical and mental state of a patient.

[0929] "Analysis means" refers to tools and systems that have the functionality to analyze collected data and predict next actions.

[0930] "Logistics center" refers to a facility that stores, manages, and distributes goods and materials.

[0931] "Product information" refers to data such as the type, quantity, location, and condition of products within a logistics center.

[0932] "Picking" refers to the task of removing specified items from a specific location in the warehouse.

[0933] "Packing" refers to the process of placing goods into suitable packaging or packaging materials.

[0934] "Inventory control" refers to the process of monitoring and controlling the quantity and condition of stored goods.

[0935] "Transportation" refers to the act of moving goods from one place to another.

[0936] MODE FOR CARRYING OUT THE INVENTION

[0937] System Overview

[0938] This article describes a robotic system that automates the picking, packing, inventory management, and transportation of goods in a distribution center. This system includes the following main components:

[0939] Database Means

[0940] Server Means

[0941] Robotic Means

[0942] analytical means

[0943] Explanation of program processing

[0944] Hardware and software used

[0945] The implementation of this system uses the following hardware and software:

[0946] Hardware: Transport robots (e.g., automated transport equipment and robotic arms)

[0947] Software: Central server (providing API), database management system, robot control program (implemented in Python, etc.)

[0948] Data processing and calculation

[0949] 1. Product information management

[0950] The server stores product information in a database, which includes product type, quantity, location, condition, etc.

[0951] 2. Obtaining and Sending Instructions

[0952] The server means retrieves the product information stored in the database means and sends instructions to the robot means, including the location from which a specific product should be picked, the packing item, and the delivery location.

[0953] 3. Execute the task

[0954] The robotic means executes tasks based on instructions from the server. For example, in the case of picking, the robot picks up a specified item from a specific location in the warehouse and moves it to an area for packing.

[0955] 4. Work Status Report

[0956] The robot means reports the work status and inventory status to the server in real time. The server records the information received in real time in the database means, analyzes the data using the analysis means as needed, and predicts the next action.

[0957] Specific examples

[0958] For example, if a user sends a picking instruction for product ID "123456" to the server, the server sends this information to the robot. The robot picks product ID "123456" from the specified location in the warehouse, and after completing the task, reports to the server that picking of "123456" is complete. The server then instructs the robot on the next task based on that report.

[0959] Example prompts for generative AI models

[0960] Server: Please send the current warehouse status and product picking instructions in the following format.

[0961] User: Please pick the following item ID 123456 from Aisle 3, Shelf 5.

[0962] This system enables efficient and accurate picking, packing, inventory management, and transportation of products within the logistics center, ensuring speed and accuracy of work while also reducing the labor burden.

[0963] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0964] Specific flow of program processing

[0965] Processing Step Description

[0966] Step 1: Store product information in a database

[0967] Input: The user enters product information (product ID, type, quantity, location, and condition).

[0968] Specific operations: The server receives the product information and stores the information in the database means.

[0969] Output: Product details saved in database.

[0970] Step 2: Sending instructions from the server to the robot

[0971] Input: The server receives picking and packing instructions from the user (product ID, location, detailed work instructions).

[0972] Specific operation: The server accesses the database means to obtain the relevant product information and instructs the robot to perform a specific task (e.g., picking, packing, transporting).

[0973] Output: A task is sent to the robot.

[0974] Step 3: Robot picking the items

[0975] Input: The robot receives a picking instruction from the server (e.g., pick product ID "123456" from Aisle 3, Shelf 5).

[0976] Specific operation: The robot moves to the specified location in the warehouse and picks the corresponding item.

[0977] Output: The picking task is completed and the result is stored in temporary memory.

[0978] Step 4: Robot packs the products

[0979] Input: The robot next receives packing instructions.

[0980] Specific operation: The robot moves the picked items to the packing area and packs the items using appropriate packaging materials.

[0981] Output: The packing task is completed and the result is stored in temporary memory.

[0982] Step 5: Reporting work status and inventory status

[0983] Input: The robot reports the results of the completed picking or packing task to the server, specifically, which items were moved to where, and whether packing is complete.

[0984] Specific operation: The robot sends details of the work status and inventory data to the server in real time.

[0985] Output: The server records the received information in a database and updates the real-time inventory status.

[0986] Step 6: Plan and instruct next steps

[0987] Input: The server receives the latest inventory information and work status recorded in the database.

[0988] What it does: The server uses analytics to analyze the data, plan the next best task, and then send new instructions to the robot.

[0989] Output: A new task is sent to the robot and it starts its next task.

[0990] These processing steps enable efficient and accurate picking, packing, inventory management, and transportation of goods within the logistics center.

[0991] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0992] This invention relates to a robot system that supports pediatric patients in hospital. This system is composed of a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, an analysis means for monitoring, analyzing, and predicting the work status and the patient's health condition, and an emotion engine for recognizing the user's emotions.

[0993] server

[0994] The server is a central computer system that manages patient information and communicates with the robot and emotion engine. The server maintains a database and stores patient information entered by the user (doctor). It also receives input from the emotion engine and adjusts instructions to the robot.

[0995] The server provides an API (Application Program Interface) and accepts requests from robots and emotion engines, allowing them to obtain information, send instructions, and report on their work status.

[0996] Terminal (robot)

[0997] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from the server, the robot performs tasks such as serving meals, guiding them to the toilet, reading to them, and supporting them in online classes. The robot can also adjust its assistance methods based on emotional data from an emotion engine. For example, if a patient is feeling stressed, the robot will respond by speaking to them gently.

[0998] The added emotion engine analyzes the facial expressions, voice, and speech patterns of users and patients via the robot's sensors to identify their emotions. The identified emotion data is immediately sent to the server and used to adjust the next instructions.

[0999] User

[1000] The user, a doctor or medical staff member, inputs patient information through the server and instructs the robot on tasks. The server can also check the patient's health condition, the robot's operating status, and even emotion data from the emotion engine, and issue new instructions as needed.

[1001] Detailed explanation of program processing

[1002] server

[1003] The server first stores patient information in a database. This information is obtained through an API when the user issues commands to the robot. The server also receives emotion data sent from the emotion engine and stores it in the database.

[1004] Terminal (robot)

[1005] The robot carries out tasks when it receives instructions from the server. For example, if it is instructed to serve a meal, the robot will carry the meal and serve it to the patient. The robot also monitors the progress of the task and the patient's reactions, and continuously sends this data to the server. If there is input from the emotion engine, it adjusts the assistance it provides based on that input.

[1006] Emotion Engine

[1007] The emotion engine recognizes emotions by analyzing the facial expressions, tone of voice, and language patterns of the user and pediatric patient. For example, if it identifies emotions such as stress or anxiety, it sends the data to the server. The server stores the received emotion data in a database and adjusts the next task or assistance method based on the data.

[1008] Specific examples

[1009] Example 1: Meal assistance

[1010] The user (doctor) inputs new patient information and saves it in the database. The user then sends meal preparation instructions for a specific pediatric patient to the server, which forwards the instructions to the robot. The robot uses an emotion engine to analyze the patient's facial expressions and tone of voice, ensuring that the patient is relaxed while preparing the meal. After completing the task, the robot reports its progress and emotion data to the server, which records the information in the database.

[1011] Example 2: Toilet Guidance

[1012] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server then forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides the necessary support. During this process, if the emotion engine detects anxiety or stress in the patient, the robot will respond by offering comforting words. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[1013] This invention provides a concrete means to support pediatric patients' hospital stays and reduce the burden on accompanying family members and medical staff. In addition, by combining it with an emotion engine, it is possible to provide detailed assistance according to the patient's emotional state, improving the quality of hospital stays.

[1014] The processing flow will be explained below.

[1015] Step 1:

[1016] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[1017] Step 2:

[1018] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[1019] Step 3:

[1020] The user sends an instruction to the server to perform a specific task (e.g., serving a meal), and the server associates the patient information with the task information.

[1021] Step 4:

[1022] The server sends task instructions to the robot. Specifically, it communicates the instruction "Provide food for patient ID 1" to the robot's terminal.

[1023] Step 5:

[1024] The terminal (robot) receives instructions from the server and prepares to perform the specified task (serving food).

[1025] Step 6:

[1026] The terminal (robot) activates an emotion engine that analyzes the facial expressions, voice, and speech patterns of the pediatric patient. The emotion engine identifies the patient's emotions and transmits the data to the server in real time.

[1027] Step 7:

[1028] The device (robot) adapts the way it performs tasks based on the identified emotional data. For example, if a patient is nervous, the robot can calm them down by speaking to them gently while providing a meal.

[1029] Step 8:

[1030] The terminal (robot) constantly monitors the progress of the task and the patient's reaction (emotional data), and reports the situation to the server as needed.

[1031] Step 9:

[1032] The server records the received task progress and emotion data in a database, specifically, saving the task completion timestamp including data from the emotion engine.

[1033] Step 10:

[1034] The server analyzes the information in the database and predicts the next task or assistance needed. For example, if it determines that the patient is relaxed while eating, it uses the same approach for the next meal.

[1035] Step 11:

[1036] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user checks this and prepares to send new instructions to the robot if necessary.

[1037] In this way, we support pediatric patients in their hospital stay, reduce the burden on accompanying family members and medical staff, and provide detailed care that is tailored to the patient's emotional state.

[1038] Example 2

[1039] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1040] Conventional robotic systems have not been able to fully realize the attentive care required to improve the quality of life for pediatric patients in hospital. In particular, the lack of a system that can recognize the patient's emotional state in real time and respond accordingly makes it difficult to reduce the patient's mental burden. Furthermore, there is a lack of a means to centrally manage work status and emotional data, which is one of the reasons for the increased burden on medical staff.

[1041] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a database means for storing patient information, a server means for acquiring the patient information stored in the database means and sending instructions to the robot, a robot means for providing care to the pediatric patient based on instructions from the server means, an analysis means for monitoring and analyzing the robot means' work status and the patient's health condition to predict the robot's next action, and an emotion recognition engine for acquiring emotion data and adjusting the care method based on the data. This allows the system to recognize the patient's emotional state in real time and provide appropriate responses, thereby reducing the mental and physical burden on pediatric patients and improving their quality of life during hospitalization. Furthermore, centralized management of work status and emotion data reduces the burden on medical staff and enables more efficient care.

[1042] "Patient information" refers to detailed data about a patient, such as basic personal information, dietary restrictions, allergy information, and special care needs.

[1043] "Database Means" refers to an electronic record device that stores patient information and allows it to be retrieved when needed.

[1044] "Server means" is a central computer system for obtaining patient information and sending instructions to the robot.

[1045] The "robot means" is a mechanical device that provides care to a pediatric patient based on instructions from the server means.

[1046] The "analysis means" is a function for monitoring the working status of the robot means and the health condition of the patient, analyzing this data, and predicting the next action.

[1047] An "emotion recognition engine" is an algorithm or device that analyzes data obtained through sensors in a robotic means to recognize and identify the emotional state of a patient.

[1048] "API" is an abbreviation for Application Program Interface, which allows the server means to accept requests from the robot means and emotion recognition engine, and to obtain information and send instructions.

[1049] An "assistance method" is a specific method of support provided by a robotic means for a specific patient need.

[1050] A "sensor" is a device that allows a robotic means to sense external information (for example, facial expressions or voice) and record it as data.

[1051] "Real-time" refers to data collection and processing occurring almost instantly, with minimal delay.

[1052] "Health status" refers to the overall state of a patient, including their physical condition and medical condition.

[1053] The present invention relates to a robotic system that supports pediatric patients during their hospital stay. This system operates by combining multiple hardware and software components, aiming to provide efficient and effective patient care.

[1054] Hardware

[1055] Server: A central computer system that interfaces with the database, APIs, analytics, and emotion recognition engine.

[1056] Terminal (Robot): A mechanical device that provides direct care to pediatric patients. It is equipped with sensors and communicates with an emotion recognition engine.

[1057] Database: An electronic record for storing patient information and affective data.

[1058] software

[1059] API: An interface through which the server accepts requests from the robot and emotion recognition engine, obtains information, and sends instructions.

[1060] Emotion Recognition Engine: Contains algorithms that analyze facial expressions, tone of voice, and language patterns to identify emotions.

[1061] Analysis means: Has the ability to monitor the robot's working status and the patient's health condition and predict its next actions.

[1062] Overall system processing flow

[1063] The user, a doctor or medical staff member, inputs patient information into the server using a dedicated terminal. This information is sent to the server and stored in a database. The user then sends instructions for specific tasks (e.g., serving meals or guiding patients to the toilet) through the server.

[1064] The server then forwards the instructions to the robot via an API. The robot uses sensors to collect real-time patient data and analyzes the emotional data through an emotion recognition engine. This emotional data is then sent back to the server to adjust the next instructions as needed.

[1065] Specific examples

[1066] Example 1: Meal assistance

[1067] 1. The user (doctor) enters new patient information and saves it in the database.

[1068] 2. The user sends meal delivery instructions to the server for a specific pediatric patient.

[1069] 3. The server forwards the instructions to the robot.

[1070] 4. The robot uses an emotion recognition engine to analyze the patient's facial expressions and tone of voice, ensuring they are relaxed while serving food.

[1071] 5. After completing the task, the robot reports its progress and emotional data to the server, which records the information in a database.

[1072] Example prompt:

[1073] "You've entered new patient information into a database and sent an API request to instruct the robot to serve the meal. Now explain how the system works so the robot receives the instruction and begins working."

[1074] Example 2: Toilet Guidance

[1075] 1. The user sends toileting assistance instructions for a specific pediatric patient to the server.

[1076] 2. The server forwards the instructions to the robot.

[1077] 3. The robot follows the instructions, safely guides the patient to the toilet and provides any necessary assistance.

[1078] 4. During this process, if the emotion recognition engine detects the patient's anxiety or stress, the robot will respond by offering kind words of encouragement.

[1079] 5. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[1080] Example prompt:

[1081] "The robot has been instructed to serve a meal. Please explain the specific flow of how to provide assistance while checking the patient's condition using the emotion recognition engine."

[1082] This invention provides a concrete means for improving the quality of hospitalized pediatric patients' lives and reducing the burden on medical staff. By combining it with an emotion recognition engine, it is possible to provide detailed assistance according to the patient's emotional state, making hospitalization more comfortable.

[1083] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] Entering and saving patient information

[1086] The user enters patient information into the server from a dedicated terminal. This information includes basic personal information, dietary restrictions, allergies, and special care needs. The server stores the entered patient information in a database. The entered data is validated to ensure that the information is properly formatted. If it is not, an error message is returned.

[1087] Input: Patient information (personal information, dietary restrictions, allergy information, special care details)

[1088] Output: Patient information correctly saved in the database

[1089] Step 2:

[1090] Start of meal serving task

[1091] The user sends instructions for serving food to the server, which then forwards the instructions to the robot via API.

[1092] The server generates an API request containing the order code and associated patient ID and sends it to the robot.

[1093] Input: Meal instructions from user, associated patient ID

[1094] Output: API request to the robot

[1095] Step 3:

[1096] Executing meal serving tasks

[1097] The terminal (robot) receives instructions from the server. Based on the received instructions, the robot delivers meals from the kitchen to the patient's room. Sensors are used to collect the patient's facial expressions and tone of voice in real time.

[1098] Input: Meal provision instructions from the server, patient ID

[1099] Output: Started meal serving task, collected sensor data

[1100] Step 4:

[1101] Real-time monitoring and data reporting

[1102] The terminal (robot) monitors the patient's reactions while serving the meal, using sensors to collect the patient's facial expressions and tone of voice, and sends the data to a server in real time.

[1103] The server receives the emotion data sent in real time and stores it in a database, while also monitoring the robot's work status in real time.

[1104] Input: Sensor data sent from the robot

[1105] Output: Emotion data stored in the database, robot work status

[1106] Step 5:

[1107] Emotional data analysis and response

[1108] The emotion recognition engine analyzes data from the robot's sensors, analyzing facial expressions, tone of voice, and language patterns to identify emotions, and sends the results to a server.

[1109] The server determines whether further instructions are needed based on the emotional data and sends new instructions to the robot if necessary.

[1110] Input: Sensor data analysis results by emotion recognition engine

[1111] Output: Analysis results and new instructions sent to the server

[1112] Step 6:

[1113] Task completion reporting and data recording

[1114] The terminal (robot) reports to the server that the meal has been served, and the server stores the received report and emotion data in a database.

[1115] Input: Task completion report from the robot, emotion data

[1116] Output: Task completion information and emotion data recorded in a database

[1117] (Application example 2)

[1118] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] The present invention relates to a robot system that supports pediatric patients' hospital stays and a food delivery support system that takes into account customer emotions. Traditionally, pediatric patients' hospital stays have placed a heavy burden on medical staff and their families, and the patients themselves have often experienced anxiety and stress. Food delivery services have also been problematic in that customers experience discomfort and stress when receiving their food. In light of these circumstances, there is a need for a system that can improve the quality of hospital stays and increase customer satisfaction with delivery services.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1121] In this invention, the server includes a database means for storing and processing patient information, a server means for acquiring the patient information and sending instructions to the robot, and an analysis means for predicting the next action by monitoring and analyzing the work status and the patient's health condition received from the robot means, thereby enabling support for the patient's hospital stay and customer care during delivery services.

[1122] "Pediatric patient" refers to a patient admitted to a hospital during childhood.

[1123] "Hospitalization" refers to the period of time spent in a hospital room while receiving medical and nursing services provided by the hospital.

[1124] "Patient Information" refers to information about a patient, including their medical history, current health status, allergy information, preferences, and medical needs.

[1125] "Database means" refers to a system or device for storing, updating, and managing information.

[1126] "Server means" refers to a computer system that communicates with multiple terminals via a network and processes instructions and data within the server.

[1127] "Robotic means" refers to a mechanical device controlled by a program and designed to automatically perform designated tasks.

[1128] "Analysis means" refers to a device or system for analyzing collected data and predicting future actions.

[1129] "Emotion recognition means" refers to a device or program for identifying emotions from a user's facial expressions and voice.

[1130] "Reporting means" refers to a device or program that collects and transmits data to a server.

[1131] "Food delivery" refers to a service that delivers food and drinks to customers who order them.

[1132] "Customer preferences" refers to individual preference information such as the customer's preferred dishes, ingredients, and allergy information.

[1133] "Appropriate action" refers to taking the most appropriate action in a given situation to improve patient or customer satisfaction.

[1134] This invention provides a robot system that supports pediatric patients in hospital and takes into account the emotions of customers in food delivery services. This system is composed of a database means for storing patient and customer information, a server means for sending instructions for assistance and delivery to the robot, a robot means for providing assistance and delivery based on the instructions, an analysis means for monitoring and analyzing the work situation and emotional state, an emotion recognition means, and a reporting means.

[1135] The server first stores patient and customer information in a database and then obtains necessary information via the database means. This information is obtained through the API when issuing instructions to the robot. The server also receives emotion data sent from the emotion recognition means and stores it in the database.

[1136] The robot carries out a task when it receives an instruction from the server. For example, if it receives an instruction to serve a meal to a pediatric patient, the robot carries out the task, delivers the meal, and serves it to the patient. Similarly, in a food delivery service, the robot delivers the meal to a customer's home. The robot monitors the progress of the task and the customer's reaction, and continuously sends this data to the server.

[1137] The emotion recognition means analyzes the facial expressions, voice, and speech patterns of the user or customer to identify their emotions. For example, if a customer is smiling, it can identify "happy," and if they are feeling stressed, it can identify "sad." The identified emotion data is immediately sent to the server and used to adjust the next task or assistance method.

[1138] As a concrete example, consider the following scenario:

[1139] Update new customer information

[1140] Doctors and medical staff input new patient and customer information and store it in a database. This updated information allows the robot to provide appropriate assistance and delivery.

[1141] Example prompt sentence:

[1142] "Please update new customer information. Customer ID is 'new_customer_1' and preferences are 'gluten-free' and 'no dairy'."

[1143] Performing sentiment analysis

[1144] When the robot interacts with patients or customers, it analyzes the voice input text using emotion recognition to identify emotions, which is important for responding appropriately to the patient or customer's emotions.

[1145] Example prompt sentence:

[1146] "Analyze the sentiment for customer ID 'customer_id_1'. Input text is 'I'm so happy to receive my meal quickly!'"

[1147] Delivery food delivery

[1148] When a food delivery robot delivers a meal to a customer's home, it can take the customer's emotions into account and respond appropriately. For example, if the customer is feeling stressed, the robot will provide an encouraging message such as, "I hope this meal cheers you up!"

[1149] Example prompt sentence:

[1150] "Please execute a delivery response based on the sentiment of customer ID 'customer_id_1'."

[1151] With these functions, the present invention not only supports pediatric patients' hospital stays and reduces the burden on medical staff and their families, but also improves customer satisfaction in food delivery services.

[1152] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1153] Step 1:

[1154] A user enters new patient or customer information into the server.

[1155] Input: Patient or customer information (e.g., patient ID, name, preferences, allergy information, etc.)

[1156] Operation: The server receives the information entered by the user and stores it in a database means.

[1157] Output: Updated patient and customer information in the database.

[1158] Step 2:

[1159] The server sends instructions to the robot based on the specified task.

[1160] Input: Type of task (e.g., meal service, toilet assistance, food delivery, etc.) and relevant patient or customer information.

[1161] Operation: The server sends instructions to the robotic means through the API, providing details of the corresponding tasks.

[1162] Output: Task instructions sent to the robot.

[1163] Step 3:

[1164] The robot receives instructions from the server and performs the specified tasks.

[1165] Input: Task instructions from the server.

[1166] Action: Based on the specifics of the task, the robot will initiate a movement, for example, bringing and serving food to the patient, guiding them to the restroom, or delivering food to a client's home.

[1167] Output: Execution of the specified task.

[1168] Step 4:

[1169] The robot monitors the progress of the task and the reactions of the patient / customer.

[1170] Input: Data from the robot's sensors (e.g., location information, operating status, facial expressions and voice of the patient / customer, etc.).

[1171] Operation: The robot sends its progress and collected sensor data to a server in real time.

[1172] Output: Progress reports and sensor data sent to the server.

[1173] Step 5:

[1174] Emotion recognition means analyzes the emotions of patients and customers.

[1175] Input: Patient / customer facial, voice, and speech patterns.

[1176] How it works: The emotion recognizer uses a generative AI model to analyze these inputs and identify emotions (e.g., "happy," "sad," "neutral," etc.).

[1177] Output: Identified emotion data.

[1178] Step 6:

[1179] The server adjusts its next instructions based on the received emotion data.

[1180] Input: Identified emotion data and progress reports.

[1181] How it works: References past data stored in the database and generates appropriate next instructions, such as instructing the robot to provide an encouraging message if the customer is identified as "sad."

[1182] Output: Adjusted next instruction.

[1183] Step 7:

[1184] The robot performs the next action based on the adjusted instructions.

[1185] Input: Coordinated instructions from the server.

[1186] Action: The robot follows new instructions and performs the required action. For example, if the customer is identified as "happy," the robot will say something like, "Enjoy your meal!"

[1187] Output: The next action that was performed.

[1188] This system will not only support hospital stays, but will also enable food delivery services to be tailored to the customer's emotions.

[1189] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1190] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1191] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1192] [Fourth embodiment]

[1193] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1194] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1195] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1196] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1197] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1198] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1199] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1200] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1201] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1202] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1203] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1204] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1205] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1206] This invention relates to a robot system that supports pediatric patients in hospital. This system includes a database for storing patient information, a server for acquiring patient information and sending instructions to the robot, a robot that provides assistance based on the instructions, and an analysis unit for monitoring and analyzing the working status and health condition and predicting the next action.

[1207] server

[1208] The server is a central computer system that manages patient information and communicates with the robot. The server maintains a database and stores patient information entered by the user (doctor). The server also provides an API (Application Program Interface) and receives requests from the robot and user terminals. This API enables the robot to obtain information, send instructions, and report work status.

[1209] Terminal (robot)

[1210] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from a server, the robot performs tasks such as serving meals, guiding them to the toilet, reading aloud, and supporting online classes. The robot also reports its own work status and the patient's health status to the server in real time, and these are recorded in a database.

[1211] User

[1212] The users, doctors and medical staff, input patient information through the server and instruct the robot on tasks. They can also check the patient's health condition and the robot's operating status from the server and issue new instructions as needed.

[1213] Detailed explanation of program processing

[1214] The server first stores patient information in a database, and the stored information is retrieved through an API when a user issues instructions to the robot. For example, if a user issues an instruction to feed a pediatric patient, the server sends the instruction to the robot.

[1215] When the robot receives the instruction, it starts the specified task (e.g., serving food). The robot constantly monitors the progress of the task and reports the results to the server in real time. The server records the received information in a database and, if necessary, analyzes the data using analytical means to predict the next action.

[1216] Specific examples

[1217] Example 1: Meal assistance

[1218] The user (doctor) enters new patient information and saves it in the database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in the database and uses analytical tools to plan the next task.

[1219] Example 2: Toilet Guidance

[1220] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[1221] As described above, this invention provides a concrete means to support pediatric patients during hospitalization and reduce the burden on accompanying family members and medical staff. Furthermore, this system can monitor the patient's health status and task progress in real time, allowing for efficient continuous support.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[1225] Step 2:

[1226] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[1227] Step 3:

[1228] Once the patient information is saved on the server, the user can call the server API to instruct the robot to perform a specific task (e.g., serving a meal). The server stores the patient ID and task information and prepares it to be sent to the robot.

[1229] Step 4:

[1230] The server sends instructions to the robot, specifically, the necessary task information (e.g., "Provide food for patient ID 1") to the terminal to which the robot is connected.

[1231] Step 5:

[1232] The terminal (robot) receives instructions from the server and starts executing the task. For example, the robot starts operating to deliver food along a set route.

[1233] Step 6:

[1234] The terminal (robot) constantly monitors the progress of the task and the patient's reaction. For example, it uses the robot's sensors to check the patient's satisfaction by checking the state of the meal.

[1235] Step 7:

[1236] The terminal (robot) reports the task completion status and ongoing status to the server in real time. Specifically, when a task is completed, the information is sent to the server.

[1237] Step 8:

[1238] The server records the received data in a database, including the timestamp of task completion and the patient's response.

[1239] Step 9:

[1240] The server uses the recorded data to perform analysis and predict the next action, for example, determining the best time to serve meals based on the data of multiple patients.

[1241] Step 10:

[1242] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user confirms this and prepares to send the next instruction to the robot.

[1243] Example 1

[1244] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1245] Pediatric patients' hospital stays place a heavy burden on medical staff and accompanying family members, and they often require assistance with health management and daily life. However, there is a lack of means to provide appropriate health management and support to patients while reducing these burdens. Therefore, there is a need for a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and families.

[1246] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1247] In this invention, the server includes an interface means for a user to input patient information, a server means for storing the patient information input via the interface means in a database, and a server means for acquiring the patient information stored in the server means and sending instructions to the robot, thereby enabling the robot to assist pediatric patients by providing meals, guiding them to the toilet, reading to them, assisting them with online classes, or performing other tasks.

[1248] The "interface means for users to input patient information" refers to a device or software that allows doctors and medical staff to input basic patient information and store it in the system.

[1249] The "server means" is a central control device for storing patient information in a database and sending instructions to the robot.

[1250] A "database" is an information storage device for centrally managing a patient's basic information, medical history, allergy information, health status, etc.

[1251] The "robot means" is a mechanical device that provides assistance to pediatric patients based on instructions from the server.

[1252] "Patient information" refers to data necessary for health management, including the patient's name, age, medical history, allergy information, etc.

[1253] "Work status" refers to information that indicates the progress of the task being performed by the robot and the results of that task.

[1254] "Health status" refers to information about the patient's current health, including vital signs and dietary intake.

[1255] "Analysis means" refers to a device or software that analyzes the reported data from the robot and predicts and plans the next task.

[1256] "Predicting the next action" refers to planning the next assistance task that the robot should perform based on the data obtained by the analysis means.

[1257] This invention is a robot system for supporting pediatric patients in hospital. This system includes a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, and an analysis means for monitoring and analyzing the working status and health condition of the patient.

[1258] Server Means

[1259] The server is the central component of this system and fulfills several roles. First, it stores patient information entered by users (doctors and medical staff) in a database. The stored information is retrieved through an API (Application Program Interface) and used to send instructions to the robot.

[1260] Specifically, the user inputs and saves patient information through a dedicated interface, including the patient's name, age, medical history, allergy information, etc. The server stores this information in a database and sends instructions to the robot when necessary.

[1261] Robotic Means

[1262] The robotic means performs specific tasks for the pediatric patient based on instructions received from the server, such as providing meals, guiding the patient to the toilet, reading aloud, and supporting online classes.

[1263] The robot follows instructions received from the server and performs the specified task. For example, if the instruction is "serve lunch for patient A," the robot delivers the meal to the patient and provides support. Once the task is completed, the robot reports the work status and the patient's health condition to the server.

[1264] analytical means

[1265] The server receives the data reported by the robots and records it in a database. It then uses analytical tools to analyze this data and plan the next tasks, ensuring that the entire system always functions optimally and that the pediatric patient's hospital stay goes smoothly.

[1266] Specific examples

[1267] Examples of meal assistance include:

[1268] The user (doctor) enters Patient A's information and saves it in a database. The server sends the information to the robot via API. The robot receives instructions for meal assistance, serves the patient, and provides appropriate support. The robot then reports the task completion status to the server. The server records the information in a database and uses analytical tools to plan the next task.

[1269] Prompt for the generative AI model:

[1270] "Please explain the specific steps involved in a robot delivering lunch to pediatric patients."

[1271] Examples of toilet guidance:

[1272] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides any necessary support. After completing the task, the robot reports its progress to the server, which records it in a database. The server uses this information to predict and plan the next task.

[1273] Prompt for the generative AI model:

[1274] "Please explain the specific processing steps of the toilet guidance robot."

[1275] As described above, the present invention provides a system that supports pediatric patients' hospital stays and reduces the burden on medical staff and their families through cooperation between components.

[1276] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1277] Step 1:

[1278] The user enters patient information.

[1279] Users (doctors and medical staff) use a dedicated interface to enter basic patient information (such as name, age, medical history, allergies, etc.) The data is then stored on the front end and prepared for transmission to the server, where it is correctly formatted and sent to the server.

[1280] Step 2:

[1281] The server stores the patient information in a database.

[1282] The server receives patient information submitted by the user. The received data goes through a data validation process before being stored in the database. If validation is successful, the server saves the data to the database, specifically by adding it as a record to the appropriate table in the database.

[1283] Step 3:

[1284] The user gives instructions to the robot.

[1285] Through the interface, the user sends instructions to the server specifying tasks for a specific patient (e.g., providing food, guiding the patient to the toilet, etc.). This instruction includes detailed information about the task to be performed. The server accepts this instruction and prepares it to be sent to the corresponding robot.

[1286] Step 4:

[1287] The server sends instructions to the robot.

[1288] The server receives instructions from the user, converts them into the appropriate format, and sends them to the robot's API endpoint. These instructions contain details of the task to be performed and information about the patient. The data sent becomes instructions that the robot can interpret and execute the task.

[1289] Step 5:

[1290] The robot receives instructions and performs the task.

[1291] The robot analyzes instructions received from the server and carries out the specified task. For example, if the instruction is "serve lunch for patient A," the robot prepares the meal and delivers it to the patient's room. The robot uses sensors to monitor its progress and the patient's response.

[1292] Step 6:

[1293] The robot reports its work status and health status to the server.

[1294] The robot monitors the progress of the tasks it is performing and the patient's health status in real time and reports the information to the server. The reported data includes the task completion status and the patient's vital signs. This data is sent through the server's API.

[1295] Step 7:

[1296] The server records the report in a database.

[1297] The server validates the reported data received from the robot and stores it in a database. The stored information serves as the patient's health history. The storage procedure includes verifying the accuracy of the data and adding records to the appropriate tables.

[1298] Step 8:

[1299] The server plans the next task.

[1300] The server analyzes the information recorded in the database and plans the next task to be executed. Using analytical means, it determines the optimal next task based on past data and the current situation. The planned task is again notified to the user or robot, and preparations for execution are made.

[1301] (Application example 1)

[1302] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1303] In modern logistics centers, tasks such as product picking, packing, inventory management, and transportation still require a large labor force. While high efficiency is required, the workload of workers is a major issue. Real-time information management is also essential to ensure the accuracy and speed of operations. However, conventional systems have difficulty resolving these issues efficiently and comprehensively. The present invention aims to solve these issues, improve the efficiency of operations within logistics centers, and reduce the labor burden.

[1304] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1305] In this invention, the server includes database means for storing patient information, server means for acquiring the patient information stored in the database means and sending instructions to the robot, robot means for assisting pediatric patients, analysis means for monitoring the working status of the robot means and the health condition of the patient and analyzing it to predict the next action, database means for applying the system to a logistics center and storing product information, server means for acquiring the product information stored in the database means and sending instructions to the robot, and robot means for picking and packing products. This enables efficient and accurate picking, packing, inventory management, and transportation of products in the logistics center.

[1306] Definition of Terms

[1307] "Pediatric patient" refers to a child between the ages of birth and 18 years who requires appropriate medical care.

[1308] "Hospitalization" refers to the period of receiving medical treatment and care in a hospital or medical facility, which can last from short to long periods of time.

[1309] A "robotic system" refers to a system that includes a mechanical device for automatically performing a specific task and the infrastructure for controlling and communicating with it.

[1310] "Patient information" refers to a collective term for data such as personal information, medical history, health status, and treatment plans of pediatric patients.

[1311] "Database means" refers to a system for electronically collecting, storing and managing information.

[1312] "Server Means" refers to a system that includes a central processing unit for accessing Database Means and communicating with other devices and systems.

[1313] "Instructions" refer to specific instructions for performing a particular task.

[1314] "Robotic Means" refers to an automatically controlled mechanical device for performing tasks based on instructions received from Server Means.

[1315] "Work Status" refers to the progress or status reports of a robotic means as it performs a task.

[1316] "Health status" refers to the overall physical and mental state of a patient.

[1317] "Analysis means" refers to tools and systems that have the functionality to analyze collected data and predict next actions.

[1318] "Logistics center" refers to a facility that stores, manages, and distributes goods and materials.

[1319] "Product information" refers to data such as the type, quantity, location, and condition of products within a logistics center.

[1320] "Picking" refers to the task of removing specified items from a specific location in the warehouse.

[1321] "Packing" refers to the process of placing goods into suitable packaging or packaging materials.

[1322] "Inventory control" refers to the process of monitoring and controlling the quantity and condition of stored goods.

[1323] "Transportation" refers to the act of moving goods from one place to another.

[1324] MODE FOR CARRYING OUT THE INVENTION

[1325] System Overview

[1326] This article describes a robotic system that automates the picking, packing, inventory management, and transportation of goods in a distribution center. This system includes the following main components:

[1327] Database Means

[1328] Server Means

[1329] Robotic Means

[1330] analytical means

[1331] Explanation of program processing

[1332] Hardware and software used

[1333] The implementation of this system uses the following hardware and software:

[1334] Hardware: Transport robots (e.g., automated transport equipment and robotic arms)

[1335] Software: Central server (providing API), database management system, robot control program (implemented in Python, etc.)

[1336] Data processing and calculation

[1337] 1. Product information management

[1338] The server stores product information in a database, which includes product type, quantity, location, condition, etc.

[1339] 2. Obtaining and Sending Instructions

[1340] The server means retrieves the product information stored in the database means and sends instructions to the robot means, including the location from which a specific product should be picked, the packing item, and the delivery location.

[1341] 3. Execute the task

[1342] The robotic means executes tasks based on instructions from the server. For example, in the case of picking, the robot picks up a specified item from a specific location in the warehouse and moves it to an area for packing.

[1343] 4. Work Status Report

[1344] The robot means reports the work status and inventory status to the server in real time. The server records the information received in real time in the database means, analyzes the data using the analysis means as needed, and predicts the next action.

[1345] Specific examples

[1346] For example, if a user sends a picking instruction for product ID "123456" to the server, the server sends this information to the robot. The robot picks product ID "123456" from the specified location in the warehouse, and after completing the task, reports to the server that picking of "123456" is complete. The server then instructs the robot on the next task based on that report.

[1347] Example prompts for generative AI models

[1348] Server: Please send the current warehouse status and product picking instructions in the following format.

[1349] User: Please pick the following item ID 123456 from Aisle 3, Shelf 5.

[1350] This system enables efficient and accurate picking, packing, inventory management, and transportation of products within the logistics center, ensuring speed and accuracy of work while also reducing the labor burden.

[1351] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1352] Specific flow of program processing

[1353] Processing Step Description

[1354] Step 1: Store product information in a database

[1355] Input: The user enters product information (product ID, type, quantity, location, and condition).

[1356] Specific operations: The server receives the product information and stores the information in the database means.

[1357] Output: Product details saved in database.

[1358] Step 2: Sending instructions from the server to the robot

[1359] Input: The server receives picking and packing instructions from the user (product ID, location, detailed work instructions).

[1360] Specific operation: The server accesses the database means to obtain the relevant product information and instructs the robot to perform a specific task (e.g., picking, packing, transporting).

[1361] Output: A task is sent to the robot.

[1362] Step 3: Robot picking the items

[1363] Input: The robot receives a picking instruction from the server (e.g., pick product ID "123456" from Aisle 3, Shelf 5).

[1364] Specific operation: The robot moves to the specified location in the warehouse and picks the corresponding item.

[1365] Output: The picking task is completed and the result is stored in temporary memory.

[1366] Step 4: Robot packs the products

[1367] Input: The robot next receives packing instructions.

[1368] Specific operation: The robot moves the picked items to the packing area and packs the items using appropriate packaging materials.

[1369] Output: The packing task is completed and the result is stored in temporary memory.

[1370] Step 5: Reporting work status and inventory status

[1371] Input: The robot reports the results of the completed picking or packing task to the server, specifically, which items were moved to where, and whether packing is complete.

[1372] Specific operation: The robot sends details of the work status and inventory data to the server in real time.

[1373] Output: The server records the received information in a database and updates the real-time inventory status.

[1374] Step 6: Plan and instruct next steps

[1375] Input: The server receives the latest inventory information and work status recorded in the database.

[1376] What it does: The server uses analytics to analyze the data, plan the next best task, and then send new instructions to the robot.

[1377] Output: A new task is sent to the robot and it starts its next task.

[1378] These processing steps enable efficient and accurate picking, packing, inventory management, and transportation of goods within the logistics center.

[1379] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1380] This invention relates to a robot system that supports pediatric patients in hospital. This system is composed of a database means for storing patient information, a server means for acquiring patient information and sending instructions to the robot, a robot means for providing assistance based on the instructions, an analysis means for monitoring, analyzing, and predicting the work status and the patient's health condition, and an emotion engine for recognizing the user's emotions.

[1381] server

[1382] The server is a central computer system that manages patient information and communicates with the robot and emotion engine. The server maintains a database and stores patient information entered by the user (doctor). It also receives input from the emotion engine and adjusts instructions to the robot.

[1383] The server provides an API (Application Program Interface) and accepts requests from robots and emotion engines, allowing them to obtain information, send instructions, and report on their work status.

[1384] Terminal (robot)

[1385] The robot is a mechanical device that provides direct assistance to pediatric patients. Based on instructions from the server, the robot performs tasks such as serving meals, guiding them to the toilet, reading to them, and supporting them in online classes. The robot can also adjust its assistance methods based on emotional data from an emotion engine. For example, if a patient is feeling stressed, the robot will respond by speaking to them gently.

[1386] The added emotion engine analyzes the facial expressions, voice, and speech patterns of users and patients via the robot's sensors to identify their emotions. The identified emotion data is immediately sent to the server and used to adjust the next instructions.

[1387] User

[1388] The user, a doctor or medical staff member, inputs patient information through the server and instructs the robot on tasks. The server can also check the patient's health condition, the robot's operating status, and even emotion data from the emotion engine, and issue new instructions as needed.

[1389] Detailed explanation of program processing

[1390] server

[1391] The server first stores patient information in a database. This information is obtained through an API when the user issues commands to the robot. The server also receives emotion data sent from the emotion engine and stores it in the database.

[1392] Terminal (robot)

[1393] The robot carries out tasks when it receives instructions from the server. For example, if it is instructed to serve a meal, the robot will carry the meal and serve it to the patient. The robot also monitors the progress of the task and the patient's reactions, and continuously sends this data to the server. If there is input from the emotion engine, it adjusts the assistance it provides based on that input.

[1394] Emotion Engine

[1395] The emotion engine recognizes emotions by analyzing the facial expressions, tone of voice, and language patterns of the user and pediatric patient. For example, if it identifies emotions such as stress or anxiety, it sends the data to the server. The server stores the received emotion data in a database and adjusts the next task or assistance method based on the data.

[1396] Specific examples

[1397] Example 1: Meal assistance

[1398] The user (doctor) inputs new patient information and saves it in the database. The user then sends meal preparation instructions for a specific pediatric patient to the server, which forwards the instructions to the robot. The robot uses an emotion engine to analyze the patient's facial expressions and tone of voice, ensuring that the patient is relaxed while preparing the meal. After completing the task, the robot reports its progress and emotion data to the server, which records the information in the database.

[1399] Example 2: Toilet Guidance

[1400] The user sends instructions for toileting assistance for a specific pediatric patient to the server. The server then forwards the instructions to the robot, which follows the instructions, safely guides the patient to the toilet, and provides the necessary support. During this process, if the emotion engine detects anxiety or stress in the patient, the robot will respond by offering comforting words. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[1401] This invention provides a concrete means to support pediatric patients' hospital stays and reduce the burden on accompanying family members and medical staff. In addition, by combining it with an emotion engine, it is possible to provide detailed assistance according to the patient's emotional state, improving the quality of hospital stays.

[1402] The processing flow will be explained below.

[1403] Step 1:

[1404] The user (doctor) enters patient information using a PC or mobile device. Specifically, the user enters the patient's name, age, diagnosis, and treatment details into an input form.

[1405] Step 2:

[1406] The server receives the patient information sent by the user and stores it in the database. Specifically, it receives a POST request via the API and creates a new entry in the database.

[1407] Step 3:

[1408] The user sends an instruction to the server to perform a specific task (e.g., serving a meal), and the server associates the patient information with the task information.

[1409] Step 4:

[1410] The server sends task instructions to the robot. Specifically, it communicates the instruction "Provide food for patient ID 1" to the robot's terminal.

[1411] Step 5:

[1412] The terminal (robot) receives instructions from the server and prepares to perform the specified task (serving food).

[1413] Step 6:

[1414] The terminal (robot) activates an emotion engine that analyzes the facial expressions, voice, and speech patterns of the pediatric patient. The emotion engine identifies the patient's emotions and transmits the data to the server in real time.

[1415] Step 7:

[1416] The device (robot) adapts the way it performs tasks based on the identified emotional data. For example, if a patient is nervous, the robot can calm them down by speaking to them gently while providing a meal.

[1417] Step 8:

[1418] The terminal (robot) constantly monitors the progress of the task and the patient's reaction (emotional data), and reports the situation to the server as needed.

[1419] Step 9:

[1420] The server records the received task progress and emotion data in a database, specifically, saving the task completion timestamp including data from the emotion engine.

[1421] Step 10:

[1422] The server analyzes the information in the database and predicts the next task or assistance needed. For example, if it determines that the patient is relaxed while eating, it uses the same approach for the next meal.

[1423] Step 11:

[1424] Based on the analysis results, the server notifies the user (doctor) of the next recommended task and the latest status of the patient. The user checks this and prepares to send new instructions to the robot if necessary.

[1425] In this way, we support pediatric patients in their hospital stay, reduce the burden on accompanying family members and medical staff, and provide detailed care that is tailored to the patient's emotional state.

[1426] Example 2

[1427] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1428] Conventional robotic systems have not been able to fully realize the attentive care required to improve the quality of life for pediatric patients in hospital. In particular, the lack of a system that can recognize the patient's emotional state in real time and respond accordingly makes it difficult to reduce the patient's mental burden. Furthermore, there is a lack of a means to centrally manage work status and emotional data, which is one of the reasons for the increased burden on medical staff.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a database means for storing patient information, a server means for acquiring the patient information stored in the database means and sending instructions to the robot, a robot means for providing care to the pediatric patient based on instructions from the server means, an analysis means for monitoring and analyzing the robot means' work status and the patient's health condition to predict the robot's next action, and an emotion recognition engine for acquiring emotion data and adjusting the care method based on the data. This allows the system to recognize the patient's emotional state in real time and provide appropriate responses, thereby reducing the mental and physical burden on pediatric patients and improving their quality of life during hospitalization. Furthermore, centralized management of work status and emotion data reduces the burden on medical staff and enables more efficient care.

[1430] "Patient information" refers to detailed data about a patient, such as basic personal information, dietary restrictions, allergy information, and special care needs.

[1431] "Database Means" refers to an electronic record device that stores patient information and allows it to be retrieved when needed.

[1432] "Server means" is a central computer system for obtaining patient information and sending instructions to the robot.

[1433] The "robot means" is a mechanical device that provides care to a pediatric patient based on instructions from the server means.

[1434] The "analysis means" is a function for monitoring the working status of the robot means and the health condition of the patient, analyzing this data, and predicting the next action.

[1435] An "emotion recognition engine" is an algorithm or device that analyzes data obtained through sensors in a robotic means to recognize and identify the emotional state of a patient.

[1436] "API" is an abbreviation for Application Program Interface, which allows the server means to accept requests from the robot means and emotion recognition engine, and to obtain information and send instructions.

[1437] An "assistance method" is a specific method of support provided by a robotic means for a specific patient need.

[1438] A "sensor" is a device that allows a robotic means to sense external information (for example, facial expressions or voice) and record it as data.

[1439] "Real-time" refers to data collection and processing occurring almost instantly, with minimal delay.

[1440] "Health status" refers to the overall state of a patient, including their physical condition and medical condition.

[1441] The present invention relates to a robotic system that supports pediatric patients during their hospital stay. This system operates by combining multiple hardware and software components, aiming to provide efficient and effective patient care.

[1442] Hardware

[1443] Server: A central computer system that interfaces with the database, APIs, analytics, and emotion recognition engine.

[1444] Terminal (Robot): A mechanical device that provides direct care to pediatric patients. It is equipped with sensors and communicates with an emotion recognition engine.

[1445] Database: An electronic record for storing patient information and affective data.

[1446] software

[1447] API: An interface through which the server accepts requests from the robot and emotion recognition engine, obtains information, and sends instructions.

[1448] Emotion Recognition Engine: Contains algorithms that analyze facial expressions, tone of voice, and language patterns to identify emotions.

[1449] Analysis means: Has the ability to monitor the robot's working status and the patient's health condition and predict its next actions.

[1450] Overall system processing flow

[1451] The user, a doctor or medical staff member, inputs patient information into the server using a dedicated terminal. This information is sent to the server and stored in a database. The user then sends instructions for specific tasks (e.g., serving meals or guiding patients to the toilet) through the server.

[1452] The server then forwards the instructions to the robot via an API. The robot uses sensors to collect real-time patient data and analyzes the emotional data through an emotion recognition engine. This emotional data is then sent back to the server to adjust the next instructions as needed.

[1453] Specific examples

[1454] Example 1: Meal assistance

[1455] 1. The user (doctor) enters new patient information and saves it in the database.

[1456] 2. The user sends meal delivery instructions to the server for a specific pediatric patient.

[1457] 3. The server forwards the instructions to the robot.

[1458] 4. The robot uses an emotion recognition engine to analyze the patient's facial expressions and tone of voice, ensuring they are relaxed while serving food.

[1459] 5. After completing the task, the robot reports its progress and emotional data to the server, which records the information in a database.

[1460] Example prompt:

[1461] "You've entered new patient information into a database and sent an API request to instruct the robot to serve the meal. Now explain how the system works so the robot receives the instruction and begins working."

[1462] Example 2: Toilet Guidance

[1463] 1. The user sends toileting assistance instructions for a specific pediatric patient to the server.

[1464] 2. The server forwards the instructions to the robot.

[1465] 3. The robot follows the instructions, safely guides the patient to the toilet and provides any necessary assistance.

[1466] 4. During this process, if the emotion recognition engine detects the patient's anxiety or stress, the robot will respond by offering kind words of encouragement.

[1467] 5. After completing the task, the robot reports its work status and emotional data to the server, which records the information in a database.

[1468] Example prompt:

[1469] "The robot has been instructed to serve a meal. Please explain the specific flow of how to provide assistance while checking the patient's condition using the emotion recognition engine."

[1470] This invention provides a concrete means for improving the quality of hospitalized pediatric patients' lives and reducing the burden on medical staff. By combining it with an emotion recognition engine, it is possible to provide detailed assistance according to the patient's emotional state, making hospitalization more comfortable.

[1471] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1472] Step 1:

[1473] Entering and saving patient information

[1474] The user enters patient information into the server from a dedicated terminal. This information includes basic personal information, dietary restrictions, allergies, and special care needs. The server stores the entered patient information in a database. The entered data is validated to ensure that the information is properly formatted. If it is not, an error message is returned.

[1475] Input: Patient information (personal information, dietary restrictions, allergy information, special care details)

[1476] Output: Patient information correctly saved in the database

[1477] Step 2:

[1478] Start of meal serving task

[1479] The user sends instructions for serving food to the server, which then forwards the instructions to the robot via API.

[1480] The server generates an API request containing the order code and associated patient ID and sends it to the robot.

[1481] Input: Meal instructions from user, associated patient ID

[1482] Output: API request to the robot

[1483] Step 3:

[1484] Executing meal serving tasks

[1485] The terminal (robot) receives instructions from the server. Based on the received instructions, the robot delivers meals from the kitchen to the patient's room. Sensors are used to collect the patient's facial expressions and tone of voice in real time.

[1486] Input: Meal provision instructions from the server, patient ID

[1487] Output: Started meal serving task, collected sensor data

[1488] Step 4:

[1489] Real-time monitoring and data reporting

[1490] The terminal (robot) monitors the patient's reactions while serving the meal, using sensors to collect the patient's facial expressions and tone of voice, and sends the data to a server in real time.

[1491] The server receives the emotion data sent in real time and stores it in a database, while also monitoring the robot's work status in real time.

[1492] Input: Sensor data sent from the robot

[1493] Output: Emotion data stored in the database, robot work status

[1494] Step 5:

[1495] Emotional data analysis and response

[1496] The emotion recognition engine analyzes data from the robot's sensors, analyzing facial expressions, tone of voice, and language patterns to identify emotions, and sends the results to a server.

[1497] The server determines whether further instructions are needed based on the emotional data and sends new instructions to the robot if necessary.

[1498] Input: Sensor data analysis results by emotion recognition engine

[1499] Output: Analysis results and new instructions sent to the server

[1500] Step 6:

[1501] Task completion reporting and data recording

[1502] The terminal (robot) reports to the server that the meal has been served, and the server stores the received report and emotion data in a database.

[1503] Input: Task completion report from the robot, emotion data

[1504] Output: Task completion information and emotion data recorded in a database

[1505] (Application example 2)

[1506] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1507] The present invention relates to a robot system that supports pediatric patients' hospital stays and a food delivery support system that takes into account customer emotions. Traditionally, pediatric patients' hospital stays have placed a heavy burden on medical staff and their families, and the patients themselves have often experienced anxiety and stress. Food delivery services have also been problematic in that customers experience discomfort and stress when receiving their food. In light of these circumstances, there is a need for a system that can improve the quality of hospital stays and increase customer satisfaction with delivery services.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1509] In this invention, the server includes a database means for storing and processing patient information, a server means for acquiring the patient information and sending instructions to the robot, and an analysis means for predicting the next action by monitoring and analyzing the work status and the patient's health condition received from the robot means, thereby enabling support for the patient's hospital stay and customer care during delivery services.

[1510] "Pediatric patient" refers to a patient admitted to a hospital during childhood.

[1511] "Hospitalization" refers to the period of time spent in a hospital room while receiving medical and nursing services provided by the hospital.

[1512] "Patient Information" refers to information about a patient, including their medical history, current health status, allergy information, preferences, and medical needs.

[1513] "Database means" refers to a system or device for storing, updating, and managing information.

[1514] "Server means" refers to a computer system that communicates with multiple terminals via a network and processes instructions and data within the server.

[1515] "Robotic means" refers to a mechanical device controlled by a program and designed to automatically perform designated tasks.

[1516] "Analysis means" refers to a device or system for analyzing collected data and predicting future actions.

[1517] "Emotion recognition means" refers to a device or program for identifying emotions from a user's facial expressions and voice.

[1518] "Reporting means" refers to a device or program that collects and transmits data to a server.

[1519] "Food delivery" refers to a service that delivers food and drinks to customers who order them.

[1520] "Customer preferences" refers to individual preference information such as the customer's preferred dishes, ingredients, and allergy information.

[1521] "Appropriate action" refers to taking the most appropriate action in a given situation to improve patient or customer satisfaction.

[1522] This invention provides a robot system that supports pediatric patients in hospital and takes into account the emotions of customers in food delivery services. This system is composed of a database means for storing patient and customer information, a server means for sending instructions for assistance and delivery to the robot, a robot means for providing assistance and delivery based on the instructions, an analysis means for monitoring and analyzing the work situation and emotional state, an emotion recognition means, and a reporting means.

[1523] The server first stores patient and customer information in a database and then obtains necessary information via the database means. This information is obtained through the API when issuing instructions to the robot. The server also receives emotion data sent from the emotion recognition means and stores it in the database.

[1524] The robot carries out a task when it receives an instruction from the server. For example, if it receives an instruction to serve a meal to a pediatric patient, the robot carries out the task, delivers the meal, and serves it to the patient. Similarly, in a food delivery service, the robot delivers the meal to a customer's home. The robot monitors the progress of the task and the customer's reaction, and continuously sends this data to the server.

[1525] The emotion recognition means analyzes the facial expressions, voice, and speech patterns of the user or customer to identify their emotions. For example, if a customer is smiling, it can identify "happy," and if they are feeling stressed, it can identify "sad." The identified emotion data is immediately sent to the server and used to adjust the next task or assistance method.

[1526] As a concrete example, consider the following scenario:

[1527] Update new customer information

[1528] Doctors and medical staff input new patient and customer information and store it in a database. This updated information allows the robot to provide appropriate assistance and delivery.

[1529] Example prompt sentence:

[1530] "Please update new customer information. Customer ID is 'new_customer_1' and preferences are 'gluten-free' and 'no dairy'."

[1531] Performing sentiment analysis

[1532] When the robot interacts with patients or customers, it analyzes the voice input text using emotion recognition to identify emotions, which is important for responding appropriately to the patient or customer's emotions.

[1533] Example prompt sentence:

[1534] "Analyze the sentiment for customer ID 'customer_id_1'. Input text is 'I'm so happy to receive my meal quickly!'"

[1535] Delivery food delivery

[1536] When a food delivery robot delivers a meal to a customer's home, it can take the customer's emotions into account and respond appropriately. For example, if the customer is feeling stressed, the robot will provide an encouraging message such as, "I hope this meal cheers you up!"

[1537] Example prompt sentence:

[1538] "Please execute a delivery response based on the sentiment of customer ID 'customer_id_1'."

[1539] With these functions, the present invention not only supports pediatric patients' hospital stays and reduces the burden on medical staff and their families, but also improves customer satisfaction in food delivery services.

[1540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1541] Step 1:

[1542] A user enters new patient or customer information into the server.

[1543] Input: Patient or customer information (e.g., patient ID, name, preferences, allergy information, etc.)

[1544] Operation: The server receives the information entered by the user and stores it in a database means.

[1545] Output: Updated patient and customer information in the database.

[1546] Step 2:

[1547] The server sends instructions to the robot based on the specified task.

[1548] Input: Type of task (e.g., meal service, toilet assistance, food delivery, etc.) and relevant patient or customer information.

[1549] Operation: The server sends instructions to the robotic means through the API, providing details of the corresponding tasks.

[1550] Output: Task instructions sent to the robot.

[1551] Step 3:

[1552] The robot receives instructions from the server and performs the specified tasks.

[1553] Input: Task instructions from the server.

[1554] Action: Based on the specifics of the task, the robot will initiate a movement, for example, bringing and serving food to the patient, guiding them to the restroom, or delivering food to a client's home.

[1555] Output: Execution of the specified task.

[1556] Step 4:

[1557] The robot monitors the progress of the task and the reactions of the patient / customer.

[1558] Input: Data from the robot's sensors (e.g., location information, operating status, facial expressions and voice of the patient / customer, etc.).

[1559] Operation: The robot sends its progress and collected sensor data to a server in real time.

[1560] Output: Progress reports and sensor data sent to the server.

[1561] Step 5:

[1562] Emotion recognition means analyzes the emotions of patients and customers.

[1563] Input: Patient / customer facial, voice, and speech patterns.

[1564] How it works: The emotion recognizer uses a generative AI model to analyze these inputs and identify emotions (e.g., "happy," "sad," "neutral," etc.).

[1565] Output: Identified emotion data.

[1566] Step 6:

[1567] The server adjusts its next instructions based on the received emotion data.

[1568] Input: Identified emotion data and progress reports.

[1569] How it works: References past data stored in the database and generates appropriate next instructions, such as instructing the robot to provide an encouraging message if the customer is identified as "sad."

[1570] Output: Adjusted next instruction.

[1571] Step 7:

[1572] The robot performs the next action based on the adjusted instructions.

[1573] Input: Coordinated instructions from the server.

[1574] Action: The robot follows new instructions and performs the required action. For example, if the customer is identified as "happy," the robot will say something like, "Enjoy your meal!"

[1575] Output: The next action that was performed.

[1576] This system will not only support hospital stays, but will also enable food delivery services to be tailored to the customer's emotions.

[1577] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1578] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1579] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1580] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1581] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1582] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1583] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1584] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1585] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1586] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1587] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1588] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1589] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1590] 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.

[1591] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1592] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1593] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1594] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1595] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1596] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1597] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1598] The following is further disclosed regarding the above embodiment.

[1599] (Claim 1)

[1600] A robotic system for supporting pediatric patients in hospital,

[1601] database means for storing patient information;

[1602] a server means for acquiring patient information stored in the database means and transmitting instructions to the robot;

[1603] a robot means for providing care to a pediatric patient based on instructions from the server means;

[1604] an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient;

[1605] A system including:

[1606] (Claim 2)

[1607] The system according to claim 1, characterized in that it provides assistance to pediatric patients by providing meals, guiding them to the toilet, reading to them, and supporting them with online classes.

[1608] (Claim 3)

[1609] 2. The system according to claim 1, wherein the robot means reports the operation status and the patient's health condition to the server means in real time, and the server means records the information in the database means.

[1610] "Example 1"

[1611] (Claim 1)

[1612] an interface means for a user to input patient information;

[1613] a server means for storing the patient information inputted via the interface means in a database;

[1614] a server means for acquiring patient information stored in the server means and transmitting instructions to the robot;

[1615] a robot means for providing care to a pediatric patient based on instructions from the server means;

[1616] an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient;

[1617] A system including:

[1618] (Claim 2)

[1619] The system of claim 1, characterized in that it assists pediatric patients by providing meals, guiding them to the toilet, reading to them, assisting them with online classes, or performing other tasks.

[1620] (Claim 3)

[1621] 2. The system according to claim 1, wherein the robot means reports the work status and the patient's health condition to the server means in real time, the server means records the information in a database, and the server means uses the analysis means to plan the next task.

[1622] "Application Example 1"

[1623] New Claims

[1624] (Claim 1)

[1625] A robotic system for supporting pediatric patients in hospital,

[1626] database means for storing patient information;

[1627] a server means for acquiring patient information stored in the database means and transmitting instructions to the robot;

[1628] a robot means for providing care to a pediatric patient based on instructions from the server means;

[1629] an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient;

[1630] A database means for applying the system to a logistics center and storing product information;

[1631] a server means for acquiring the product information stored in the database means and transmitting instructions to the robot;

[1632] a robot means for picking and packing products based on instructions from the server means;

[1633] A system including:

[1634] (Claim 2)

[1635] The system according to claim 1, characterized in that it provides assistance to pediatric patients by providing meals, guiding them to the toilet, reading to them, and supporting them with online classes, and the system according to claim 1, characterized in that it performs picking and packing of goods, inventory management, and transportation work within a logistics center.

[1636] (Claim 3)

[1637] The system according to claim 1, characterized in that the robot means reports the work status and the patient's health condition to the server means in real time, and the server means records the information in the database means; and the system according to claim 1, characterized in that the robot means reports the work status and the inventory status to the server means in real time, and the server means records the information in the database means.

[1638] "Example 2: Combining Emotion Engines"

[1639] (Claim 1)

[1640] A robotic system for supporting pediatric patients in hospital,

[1641] database means for storing patient information;

[1642] a server means for acquiring patient information stored in the database means and transmitting instructions to the robot;

[1643] a robot means for providing care to a pediatric patient based on instructions from the server means;

[1644] an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient;

[1645] an emotion recognition engine that acquires emotion data and adjusts assistance methods based on the data;

[1646] A system including:

[1647] (Claim 2)

[1648] The system according to claim 1, characterized in that it provides assistance to pediatric patients by providing meals, guiding them to the toilet, reading to them, and supporting them with online classes.

[1649] (Claim 3)

[1650] 2. The system according to claim 1, wherein said robot means reports the work status and the patient's health and emotional state to said server means in real time, and said server means records the information in said database means.

[1651] "Application example 2 when combining emotion engines"

[1652] (Claim 1)

[1653] A robotic system for supporting pediatric patients in hospital,

[1654] database means for storing patient information;

[1655] a server means for acquiring patient information stored in the database means and transmitting instructions to the robot;

[1656] a robot means for providing care to a pediatric patient based on instructions from the server means;

[1657] an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient;

[1658] database means for storing and processing customer preference information;

[1659] an emotion recognition means for analyzing the facial expression and tone of voice of the customer at the time of receipt to identify their emotion;

[1660] a robot means for performing an appropriate action according to the emotion of the customer using the emotion recognition means;

[1661] reporting means for reporting the progress of said robotic means in real time and recording it in a database;

[1662] A system including:

[1663] (Claim 2)

[1664] The system described in claim 1 provides assistance to pediatric patients by providing meals, guiding them to the toilet, reading to them, and supporting them with online classes, and also provides a food delivery service to customers by providing meals based on the customer's preferences and responding using emotion recognition.

[1665] (Claim 3)

[1666] 2. The system of claim 1, wherein the robot means reports the work status and the patient's health condition to the server means in real time, the server means records the information in the database means, and the robot means reports the emotional state of the customer and adapts the way of responding to the customer based thereon. [Explanation of symbols]

[1667] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A robotic system for supporting pediatric patients in hospital, database means for storing patient information; a server means for acquiring patient information stored in the database means and transmitting instructions to the robot; a robot means for providing care to a pediatric patient based on instructions from the server means; an analysis means for predicting the next action by monitoring and analyzing the working status of the robot means and the health condition of the patient; A system including:

2. The system according to claim 1, characterized in that it provides assistance to pediatric patients by providing meals, guiding them to the toilet, reading to them, and supporting them with online classes.

3. 2. The system according to claim 1, wherein said robot means reports the operation status and the health condition of the patient to said server means in real time, and said server means records the information in said database means.

Images