system

Abstract

We provide the system. [Solution] A data processing means for integrating and managing patient-related information, Information generation means for automatically generating visit records and treatment plans using an artificial intelligence model, A dialogue tool that provides conversational functions to facilitate communication among experts, Information security management measures to ensure the protection of information, A transmission means that provides an interface for efficiently transmitting information entered by users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since multiple experts engaged in home care manage patient information individually, there is a problem that information sharing and cooperation in treatment plans are insufficient. In addition, there are differences in expertise among experts, and there are situations where communication discrepancies and information misunderstandings are likely to occur, which is also an issue. These problems have led to a decline in the quality of services provided to patients and have become factors preventing the prompt provision of appropriate treatment and care.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that includes data processing means for sharing and managing integrated information among multiple experts in a ring, information generation means for automatically generating visit records and treatment plans using a generative model, dialogue means for providing a chat function that allows experts to easily exchange information, and information security management means for ensuring the safety of information. Furthermore, by providing information distribution means for delivering generated information to experts in real time, it enables the provision of prompt and appropriate care services. In addition, by providing means for analyzing data accumulated on the platform and obtaining new insights in the field of care, it also contributes to the long-term development of the care business.

[0006] "Data processing means" refers to a device or method that provides a function for comprehensively aggregating and effectively managing patient information.

[0007] "Information generation means" refers to a device or method equipped with the function of automatically creating visit records and treatment plans using a generation model.

[0008] "Dialogue means" refers to a device or method that provides chat or messaging functions to facilitate information sharing among experts.

[0009] "Information security management means" refers to devices or methods that provide the necessary security functions to maintain the safety of patient information and professional data.

[0010] "Information distribution means" refers to a device or method that provides information such as generated visit records and treatment plans to relevant professionals in real time.

[0011] "Data analysis means" refers to a device or method that has the function of discovering and extracting new insights related to the field of nursing care using data accumulated on a platform. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a comprehensive system to support home care settings. The system is designed as a platform usable by multiple professionals, each with access via a terminal. At its core, the system implements functions for integrated information management, enabling automated information generation and secure information sharing.

[0034] The server aggregates patient information sent from various specialists, checks its integrity, and then securely stores it in a database. This storage process involves data encryption to protect patient privacy. Based on the stored information, the server utilizes a generative model to automatically generate visit records and treatment plan summaries. This generated information is distributed in real time to all relevant specialists. The distributed information is immediately displayed on the user's device, allowing them to refer to and make decisions as needed.

[0035] The terminal provides an interface for efficiently transmitting user-inputted information to the server. This allows specialists to immediately input patient data after a visit and prepare for the next visit. The terminal also includes a chat function to facilitate communication among specialists. This enables users to quickly communicate with other specialists and share their expertise.

[0036] As a concrete example, suppose a nurse enters the patient's condition into a terminal after a home visit. The entered data is sent to a server, and a summary of the visit record is immediately generated. This summary is distributed to other doctors and caregivers and can be referenced during the next visit. Furthermore, if it is necessary to exchange opinions among professionals regarding important treatment plans, they can easily contact other professionals using the chat function and obtain the necessary information in a short time.

[0037] This system prioritizes information security, utilizing a secure domestic data center for server-side data management. Furthermore, the data accumulated in the system will be used for long-term data analysis to gain new insights into the field of elderly care. The knowledge gained from this analysis will contribute to the further development of the elderly care business.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user uses a terminal to enter patient data after the visit. This information includes vital signs, observed symptoms, and procedures performed.

[0041] Step 2:

[0042] The terminal formats the input data into a predetermined format and sends it to the server using a secure communication protocol.

[0043] Step 3:

[0044] The server analyzes the data received from the terminal to verify its integrity and accuracy. If there are any problems with the data at this stage, an error message is sent to the terminal.

[0045] Step 4:

[0046] The server stores data that has been verified for integrity in the database. During the storage process, the data is encrypted to protect privacy.

[0047] Step 5:

[0048] The server uses a generative model to automatically generate a summary of visit records and a treatment plan from the received data. This process references past data and guidelines.

[0049] Step 6:

[0050] The server distributes the generated summary and treatment plan to the terminals of all relevant professionals, allowing them to receive information in real time.

[0051] Step 7:

[0052] The device receives the delivered information and displays it to the user. The user can then use this information to prepare for their next visit.

[0053] Step 8:

[0054] Users can use the device's chat function as needed to share information and exchange opinions with other experts. This chat function also saves the history of the conversations.

[0055] Step 9:

[0056] The server regularly backs up all data, including chat logs, and stores it in a secure data center within the country. This protects against data loss.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In home care settings, the involvement of multiple specialists complicates the aggregation and management of patient information, requiring rapid and accurate information sharing while ensuring data consistency and security. Furthermore, there is a lack of automation for visit records and treatment plans, as well as mechanisms to facilitate smooth communication among specialists, which needs to be addressed.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes data management means for aggregating and verifying the integrity of data input from terminals, information security management means for encrypting and securely transmitting and receiving data using a set protocol, and information generation means for automatically generating visit records and treatment plan summaries using a generation AI model. This enables more efficient information sharing among multiple experts, ensures the security of information, reduces workload through automation, and facilitates smooth communication among experts.

[0062] A "terminal" is a device that provides an interface for users to input information and send that information to a server.

[0063] A "data management system" is a function that collects data entered from terminals, verifies data integrity, and stores it in a database.

[0064] "Information security management measures" refer to functions that encrypt data using a configured protocol and send and receive it securely.

[0065] A "generative AI model" is an artificial intelligence technology that automatically generates visit records and treatment plan summaries based on input data.

[0066] The "information generation means" refers to a function that uses a generation AI model to automatically generate visit records and treatment plan summaries based on input data.

[0067] "Information distribution means" refers to a function for distributing generated summaries in real time among relevant experts.

[0068] "Dialogue support tools" refer to functions that provide chat capabilities and other features to facilitate communication between experts.

[0069] "Planning support tools" refer to functions for formulating optimized visit plans based on the patient's condition.

[0070] A "data storage method" is a function for accumulating collected data over the long term.

[0071] "Data analysis tools" are functions that analyze accumulated data to discover new insights.

[0072] This embodiment of the invention is a system for efficiently managing patient information and promoting information sharing among professionals in home care settings. This system includes terminals and a server, and provides various functions for each professional to input and utilize information.

[0073] The terminal provides an interface for users to input the patient's condition after their visit. This user interface is configured using hardware such as tablets or personal computers. The entered data is accurately and quickly transmitted to the server using text boxes and dropdown menus.

[0074] The server receives data sent from the terminal and verifies its integrity. Data security is ensured by using TLS (Transport Layer Security) encryption technology. The verified data is stored in a database, and an AI model automatically generates visit records and treatment plan summaries. This AI model utilizes natural language processing technology to extract meaning from the data and generate summaries in an easy-to-understand format.

[0075] The generated information is shared in real time among experts through information distribution channels. This allows experts to respond quickly and accurately based on the latest patient information. The terminal also features a chat function, allowing experts to exchange opinions with other experts as needed. This function is highly effective in situations requiring immediacy, facilitating information sharing and smooth communication.

[0076] As a concrete example, consider a scenario where a nurse inputs the patient's blood pressure and temperature measurements into a terminal. Once this information is sent to the server, the AI ​​model generates a summary such as, "The patient's blood pressure is stable. Please proceed with the next visit as scheduled." This summary is then distributed to the terminals of other relevant doctors and caregivers and used to plan the next care session.

[0077] Examples of prompts for the generating AI model include, "Automatically generate a visit record based on the patient's health status today." In this way, this system supports the efficiency and quality improvement of home care.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user uses a terminal to input the patient's condition after providing home care. The input includes the patient's vital data (blood pressure, temperature, heart rate, etc.) and observations. This information is formatted as digital data based on the input form on the terminal. The final formatted data is then prepared as a dataset for the next processing step.

[0081] Step 2:

[0082] The terminal encrypts the data entered by the user and sends it to the server. This encryption uses the TLS protocol, ensuring secure transmission of data over the network. The input consists of formatted patient data, which is then encrypted and output as encrypted data. Once this encrypted data is sent to the server, the process proceeds to the next step.

[0083] Step 3:

[0084] The server decrypts the received data and checks its integrity before saving it to the database. The checking process verifies that the data items are accurate and complete. It receives encrypted data as input, decrypts it, and outputs it as clean, integrity-verified data. This output is then stored in a secure database.

[0085] Step 4:

[0086] The server uses stored data to leverage a generative AI model to generate visit records and treatment plan summaries. Decoded patient data is used as input, and the AI ​​generates summary text using natural language processing based on this data. This summary text is used to support rapid decision-making by experts.

[0087] Step 5:

[0088] The server distributes the generated summary in real time to the terminals of relevant experts using an information distribution system. In this step, the summary text output by the AI ​​model is received as input and output directly to the terminals of other experts. This allows all stakeholders to immediately grasp the latest patient information.

[0089] Step 6:

[0090] The terminal displays the distributed summary information to the user. Based on the displayed summary, the user checks the patient's condition and makes decisions regarding the next visit plan and treatment policy. The terminal also has a chat function for professionals, allowing the user to exchange information with other professionals as needed. The input is a summary text from the server, which is then visualized and output on the display screen.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In home care settings, the involvement of multiple specialists can lead to delays in sharing patient information and determining treatment plans, sometimes resulting in inconsistencies in information. Furthermore, information security must be considered, necessitating the protection of privacy. Additionally, there is a challenge to improve the quality of care by utilizing data related to caregiving operations over the long term.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes data processing means for integrating and managing patient-related information, information generation means for automatically generating visit records and treatment plans using an artificial intelligence model, and dialogue means for providing conversational functions to facilitate communication among professionals. This enables rapid information sharing even after home care visits, and allows professionals to be notified of the patient's condition in real time. Furthermore, it enables efficient data processing while ensuring the protection of information, and allows for the use of data to improve the quality of long-term care.

[0096] "Patient-related information" refers to data that indicates the health status and living conditions of individuals who are important from a medical and nursing care perspective.

[0097] A "data processing system for integrated management" is a mechanism for centrally aggregating multiple data sets and efficiently storing and managing them.

[0098] An "artificial intelligence model" is an algorithm that learns patterns and rules using large amounts of data and has the ability to automatically extract insights from that data.

[0099] "Automatic generation of visit records" refers to creating records based on information collected during a visit, minimizing manual input.

[0100] "Information generation means for automatically generating treatment plans" refers to a function that proposes the optimal treatment plan based on the health status of the subject.

[0101] A "dialogue function to facilitate communication among experts" refers to a means of dialogue established to enable multiple experts to share information with each other and make decisions collaboratively.

[0102] "Information security management measures to ensure the protection of information" refer to technical and organizational measures to prevent unauthorized access to and leakage of data.

[0103] An "interface for efficiently transmitting user-inputted information" is a user interface that allows users to send data to a server concisely and quickly.

[0104] A "specialist" refers to someone who possesses advanced knowledge and skills in a specific field and performs work in that field.

[0105] In the system for implementing this invention, a server, terminals, and users function as the central components. The server manages real-time aggregated patient-related information and uses an artificial intelligence model to automatically generate visit records and treatment plans based on that information. Patient information is sent to the server, and after the data is protected by advanced security management measures, the generated information is immediately delivered to the specialist's terminal.

[0106] The terminal provides an interface that makes it easy for specialists to input new patient information immediately after a visit. By utilizing the terminal's built-in conversation function, specialists can communicate efficiently and share their knowledge with each other. The conversation function enables real-time information sharing using Socket.IO.

[0107] Users access each device using their smartphones or tablets and enter the patient's condition after the visit. The entered information is processed instantly on the server, and medical records are automatically summarized. This summarized information is quickly distributed to relevant professionals to inform future decisions and treatments.

[0108] The hardware used includes mobile devices such as smartphones and tablets. The software consists of an AI generation component built in Python, Firebase for data management, and React Native and Socket.IO for real-time communication.

[0109] As a concrete example, when a caregiver enters a patient's health data on a tablet during a home visit, that data is processed on a server, and a generating AI model summarizes the day's visit record. This summary is then quickly sent to the attending physician, who can use the data as a reference for future visits. An example of a prompt message would be, "Please summarize the patient's latest visit record and distribute it to the specialist."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The terminal receives data on the patient's health status and living conditions entered by the caregiver, who is the user. This terminal is equipped with a data input interface designed for intuitive information entry. The entered data is temporarily stored on the terminal.

[0113] Step 2:

[0114] The terminal encodes the input information and then transmits it to the server via the internet. The input includes patient vital data, symptoms, and observations during visits, generating data packets as output. The data is encrypted using JWT before transmission.

[0115] Step 3:

[0116] The server decodes the received data and stores it in a secure database. The data's integrity is checked, and any inconsistencies are verified before it is saved to the database. Here, the input is the data packet sent from the terminal, and the output is the storage of the encrypted data into the database.

[0117] Step 4:

[0118] The server uses an artificial intelligence model based on the stored data to generate a summary of visit records. The AI ​​model creates the summary by analyzing the input data and extracting important information. In this step, patient data from the database is used as input, and summary information is generated as output.

[0119] Step 5:

[0120] The server delivers the generated summary information to the terminals of relevant experts in real time. The input here is the summary information from the AI ​​model, and the output is the display of the summary on each expert's terminal. Information is notified instantly via Socket.IO.

[0121] Step 6:

[0122] The terminal displays the received information and, if necessary, uses a conversation function between experts to exchange further opinions. At this stage, the input is summarized information from the server, and the output is the information displayed on the terminal, along with user responses and additional information.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a system designed to support information sharing and decision-making in home care settings, and in particular, incorporates an emotion engine to enable communication that takes user emotions into consideration. This emotion engine has the function of recognizing emotions from the user's facial expressions, voice, text, etc., and providing personalized support.

[0125] The server integrates and securely stores patient information entered by each specialist. It automatically generates visit records and treatment plans using a generative model and delivers them in real time. This allows specialists to quickly decide on their next course of action. Furthermore, an emotion engine operates on the server, analyzing the user's emotional state. This analysis is shared with other specialists through dialogue tools, facilitating more appropriate communication.

[0126] The device not only provides an interface for sending user-inputted information to the server, but also conveys feedback from the emotion engine to the user. This allows the user to objectively understand their own state. The device also includes emotion-sensitive prompts and alerts, designed to reduce the user's psychological burden.

[0127] For example, when a nurse enters a patient's condition, the terminal monitors the user's emotional state in real time. If the nurse is stressed, the emotion engine detects this and reports it to the server. This information is then shared with other healthcare professionals, enabling them to provide prompt and appropriate support.

[0128] This system facilitates information sharing among experts and enables decision-making that takes emotional factors into account. Furthermore, it will include data analysis capabilities to analyze accumulated emotional data in the future and use that data to improve caregiving services, thereby contributing to the further development of the caregiving field.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user uses a terminal to enter patient information. This information includes the patient's symptoms, vital signs, and treatment details. The terminal formats the entered information and prepares it for transmission to the server.

[0132] Step 2:

[0133] The terminal generates a packet containing the input data and sends it to the server using an encryption protocol. During this transmission process, the integrity and confidentiality of the information are ensured.

[0134] Step 3:

[0135] The server analyzes the data received from the terminal and verifies its integrity before saving it to the database. This process ensures that the data is managed consistently and securely.

[0136] Step 4:

[0137] The server uses a generative model to automatically generate visit records and treatment plan summaries from stored data. This generated information is distributed in real time to relevant professionals, enabling rapid action.

[0138] Step 5:

[0139] The emotion engine analyzes the user's voice and facial expression data to recognize their emotional state. It uses data obtained from the device's camera and microphone to determine emotions.

[0140] Step 6:

[0141] The server analyzes the emotional information recognized by the emotion engine and shares it with other experts as needed. Based on this information, the experts consider approaches that take into account the user's mental and physical state.

[0142] Step 7:

[0143] The device provides feedback to the user based on recognized emotional information. For example, if it determines that the user is highly stressed, it will display a message encouraging relaxation.

[0144] Step 8:

[0145] Users can use the device's chat function to interact with other experts and ask questions or seek information if needed. This chat provides support that takes emotional nuances into consideration.

[0146] Step 9:

[0147] The server regularly backs up all collected data and stores it in a secure data center. This storage ensures the permanent protection of the information.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In home care settings, delays in information sharing among professionals and communication that disregards emotional aspects hinder appropriate decision-making. Furthermore, the inability to adequately assess the stress and emotional state of professionals themselves often leads to increased workload. These challenges need to be addressed.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes input means for receiving and transmitting user input information to the server, data analysis means for analyzing emotions from the user's facial expressions, voice, and text data, and information sharing means for notifying other users of the analysis results generated using the emotion engine. This enables real-time information sharing among experts, facilitating quick and appropriate decision-making that takes emotional aspects into consideration.

[0153] An "input means" is an interface for receiving information from a user and sending that information to a server.

[0154] A "data analysis method" is a mechanism that analyzes emotions based on the user's facial expressions, voice, and text data.

[0155] "Information sharing means" refers to a function that notifies other users of sentiment analysis results and facilitates communication among experts.

[0156] A "feedback mechanism" is a function that provides users with appropriate prompts or alerts based on the results of sentiment analysis.

[0157] An "information distribution means" is a mechanism for delivering information transmitted by a user to a server in real time.

[0158] A "data analysis tool" is an analytical mechanism that uses data accumulated over a long period of time to discover new insights.

[0159] This invention aims to construct a system for facilitating information sharing and emotion analysis in home care. The server includes a database for integrating and securely managing user input information. This database stores data such as patient symptoms and vital signs entered by professionals such as nurses via terminals.

[0160] The server also features a generative AI model that automatically generates visit records and treatment plans. This generative model analyzes the input data to calculate effective visit schedules and treatment plans. Furthermore, an emotion engine analyzes the user's emotional state based on received voice, facial expression, and text data. The analysis results are immediately shared with relevant parties to support appropriate decision-making.

[0161] The terminal has an interface that provides users with easy operation. This allows users to easily input information and send it to the server. The terminal also provides feedback to the user based on sentiment analysis. For example, if the user is feeling stressed, this will be displayed on the terminal, and appropriate alerts and prompts will be provided.

[0162] As a concrete example, a prompt message such as "Please enter the patient's condition" is used. Upon receiving this prompt, the user enters the information, and the server processes it, providing appropriate feedback and analysis results to relevant specialists. This enables rapid and emotionally conscious decision-making in home care settings.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The user uses a terminal to input patient information. Specifically, they enter the patient's symptoms and vital signs in a text field and click the submit button. The input data includes the patient's name, symptoms, measured heart rate, and body temperature. As output, the terminal sends this information to the server.

[0166] Step 2:

[0167] The server securely stores the received patient information. The input data is the patient information sent in step 1, which is written to the database. Specifically, the server stores the information through the database management system, applying redundancy and encryption. As output, the information is passed to the generating AI model.

[0168] Step 3:

[0169] The server automatically generates visit records and treatment plans using a generative AI model. The input data is the patient information saved in step 2. Based on this data, the model performs analysis and outputs an effective visit schedule and treatment plan. Specifically, the model performs inference based on the trained data.

[0170] Step 4:

[0171] The server uses an emotion engine to analyze the user's (expert's) emotional state. Input data consists of audio and facial expressions. The analysis engine analyzes this data and outputs the user's stress and fatigue levels. Specifically, the server uses voice analysis software and facial recognition technology.

[0172] Step 5:

[0173] The server notifies the terminal of the analysis results, facilitating information sharing among experts. The input data consists of the generated results and emotion analysis results obtained in steps 3 and 4. As output, the generated treatment plan and emotion information are distributed to other experts. Specifically, the server sends messages using a notification system.

[0174] Step 6:

[0175] The device provides the user with feedback based on emotion analysis. The input data is the emotion analysis results received from the server in step 5. The device displays prompts and alerts to the user to reduce stress. Specifically, a message encouraging relaxation is displayed on the screen.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Providing efficient care without considering emotional factors is a challenging task in information sharing and decision-making support within the home care setting. In particular, real-time emotional recognition and information sharing are essential to balance reducing the burden on care staff with providing emotional care to patients. Means to achieve this are needed.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes data processing means for integrating and managing patient information, information generation means for automatically generating visit records and treatment plans using a generative model, and emotion analysis means for analyzing emotional states using emotion recognition technology and providing feedback to the user. This makes it possible to provide care that takes emotional factors into consideration in real time in nursing care settings.

[0181] "Data processing means" refers to a function that provides a method for integrating and securely managing patient information.

[0182] "Information generation means" refers to a function that provides a method for automatically generating visit records and treatment plans using a generation model.

[0183] A "means of dialogue" refers to a function that provides methods to facilitate communication among experts and promote information exchange.

[0184] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions, voice, etc., to recognize their emotional state.

[0185] A "visual information presentation method" is a function that provides a way to display emotion-based suggestions to care staff in real time.

[0186] "Information management means" refers to a function that provides management methods to ensure the confidentiality and security of information.

[0187] The system for realizing this invention is constructed using various hardware and software. The server integrates and securely manages patient information using data processing means. By using a generative model, it automatically generates visit records and treatment plans, functioning as an information generation means. Furthermore, as an emotion analysis means, it analyzes the user's facial expressions and voice data to recognize their emotional state in real time. For this analysis, for example, general face recognition APIs and voice analysis APIs are used.

[0188] On the terminal side, emotionally-based suggestions are displayed to care staff through visual information presentation methods. This allows staff during visits to receive specific guidance to provide appropriate communication and care to patients. Information management systems ensure that this data is securely stored and facilitate information sharing among professionals.

[0189] As a concrete example, when a caregiver is visiting a patient with dementia, if the device recognizes the patient's face and detects that the patient is anxious, a suggestion such as "Please speak to the patient in a calm tone to reassure them" will be displayed on the device's screen. The AI ​​model will then receive the instruction "Patient XX is anxious. Please suggest ways to reassure them."

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server receives patient facial expression data transmitted from the terminal. This data is in the form of images or videos captured by a camera. The input data is preprocessed using image processing algorithms and converted into a format suitable for the face recognition model. This process extracts facial feature points.

[0193] Step 2:

[0194] The server uses a face recognition API to analyze the emotional state based on the extracted facial feature points. This analysis process passes the input feature points through an emotion classification model, classifying them into emotional categories such as joy or anxiety. The output provides a specific emotional state and its probability.

[0195] Step 3:

[0196] The server generates and sends prompts to the generative AI model based on the results of the emotion analysis. An example prompt that can be generated is, "Patient XX is feeling anxious. Please suggest ways to reassure them." Based on the emotional state provided as input, the generative AI model generates appropriate care responses.

[0197] Step 4:

[0198] The server sends suggestions generated by the AI ​​model to the terminal. These suggestions are in text or audio format. The terminal displays the received suggestions on its screen, providing specific instructions to the care staff on what actions to take next. This output enables the staff to provide appropriate care to the patient.

[0199] Step 5:

[0200] The terminal sends staff responses and additional inputs to the server. This information is stored as system learning data and used to improve caregiving operations. In this step, the entered data is saved to a database and used for the next analysis.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention provides a comprehensive system to support home care settings. The system is designed as a platform usable by multiple professionals, each with access via a terminal. At its core, the system implements functions for integrated information management, enabling automated information generation and secure information sharing.

[0218] The server aggregates patient information sent from various specialists, checks its integrity, and then securely stores it in a database. This storage process involves data encryption to protect patient privacy. Based on the stored information, the server utilizes a generative model to automatically generate visit records and treatment plan summaries. This generated information is distributed in real time to all relevant specialists. The distributed information is immediately displayed on the user's device, allowing them to refer to and make decisions as needed.

[0219] The terminal provides an interface for efficiently transmitting user-inputted information to the server. This allows specialists to immediately input patient data after a visit and prepare for the next visit. The terminal also includes a chat function to facilitate communication among specialists. This enables users to quickly communicate with other specialists and share their expertise.

[0220] As a concrete example, suppose a nurse enters the patient's condition into a terminal after a home visit. The entered data is sent to a server, and a summary of the visit record is immediately generated. This summary is distributed to other doctors and caregivers and can be referenced during the next visit. Furthermore, if it is necessary to exchange opinions among professionals regarding important treatment plans, they can easily contact other professionals using the chat function and obtain the necessary information in a short time.

[0221] This system prioritizes information security, utilizing a secure domestic data center for server-side data management. Furthermore, the data accumulated in the system will be used for long-term data analysis to gain new insights into the field of elderly care. The knowledge gained from this analysis will contribute to the further development of the elderly care business.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user uses a terminal to enter patient data after the visit. This information includes vital signs, observed symptoms, and procedures performed.

[0225] Step 2:

[0226] The terminal formats the input data into a predetermined format and sends it to the server using a secure communication protocol.

[0227] Step 3:

[0228] The server analyzes the data received from the terminal to verify its integrity and accuracy. If there are any problems with the data at this stage, an error message is sent to the terminal.

[0229] Step 4:

[0230] The server stores data that has been verified for integrity in the database. During the storage process, the data is encrypted to protect privacy.

[0231] Step 5:

[0232] The server uses a generative model to automatically generate a summary of visit records and a treatment plan from the received data. This process references past data and guidelines.

[0233] Step 6:

[0234] The server distributes the generated summary and treatment plan to the terminals of all relevant professionals, allowing them to receive information in real time.

[0235] Step 7:

[0236] The device receives the delivered information and displays it to the user. The user can then use this information to prepare for their next visit.

[0237] Step 8:

[0238] Users can use the device's chat function as needed to share information and exchange opinions with other experts. This chat function also saves the history of the conversations.

[0239] Step 9:

[0240] The server regularly backs up all data, including chat logs, and stores it in a secure data center within the country. This protects against data loss.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] In home care settings, the involvement of multiple specialists complicates the aggregation and management of patient information, requiring rapid and accurate information sharing while ensuring data consistency and security. Furthermore, there is a lack of automation for visit records and treatment plans, as well as mechanisms to facilitate smooth communication among specialists, which needs to be addressed.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes data management means for aggregating and verifying the integrity of data input from terminals, information security management means for encrypting and securely transmitting and receiving data using a set protocol, and information generation means for automatically generating visit records and treatment plan summaries using a generation AI model. This enables more efficient information sharing among multiple experts, ensures the security of information, reduces workload through automation, and facilitates smooth communication among experts.

[0246] A "terminal" is a device that provides an interface for users to input information and send that information to a server.

[0247] A "data management system" is a function that collects data entered from terminals, verifies data integrity, and stores it in a database.

[0248] "Information security management measures" refer to functions that encrypt data using a configured protocol and send and receive it securely.

[0249] A "generative AI model" is an artificial intelligence technology that automatically generates visit records and treatment plan summaries based on input data.

[0250] The "information generation means" refers to a function that uses a generation AI model to automatically generate visit records and treatment plan summaries based on input data.

[0251] "Information distribution means" refers to a function for distributing generated summaries in real time among relevant experts.

[0252] "Dialogue support tools" refer to functions that provide chat capabilities and other features to facilitate communication between experts.

[0253] "Planning support tools" refer to functions for formulating optimized visit plans based on the patient's condition.

[0254] A "data storage method" is a function for accumulating collected data over the long term.

[0255] "Data analysis tools" are functions that analyze accumulated data to discover new insights.

[0256] This embodiment of the invention is a system for efficiently managing patient information and promoting information sharing among professionals in home care settings. This system includes terminals and a server, and provides various functions for each professional to input and utilize information.

[0257] The terminal provides an interface for users to input the patient's condition after their visit. This user interface is configured using hardware such as tablets or personal computers. The entered data is accurately and quickly transmitted to the server using text boxes and dropdown menus.

[0258] The server receives data sent from the terminal and verifies its integrity. Data security is ensured by using TLS (Transport Layer Security) encryption technology. The verified data is stored in a database, and an AI model automatically generates visit records and treatment plan summaries. This AI model utilizes natural language processing technology to extract meaning from the data and generate summaries in an easy-to-understand format.

[0259] The generated information is shared in real time among experts through information distribution channels. This allows experts to respond quickly and accurately based on the latest patient information. The terminal also features a chat function, allowing experts to exchange opinions with other experts as needed. This function is highly effective in situations requiring immediacy, facilitating information sharing and smooth communication.

[0260] As a concrete example, consider a scenario where a nurse inputs the patient's blood pressure and temperature measurements into a terminal. Once this information is sent to the server, the AI ​​model generates a summary such as, "The patient's blood pressure is stable. Please proceed with the next visit as scheduled." This summary is then distributed to the terminals of other relevant doctors and caregivers and used to plan the next care session.

[0261] Examples of prompts for the generating AI model include, "Automatically generate a visit record based on the patient's health status today." In this way, this system supports the efficiency and quality improvement of home care.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user uses a terminal to input the patient's condition after providing home care. The input includes the patient's vital data (blood pressure, temperature, heart rate, etc.) and observations. This information is formatted as digital data based on the input form on the terminal. The final formatted data is then prepared as a dataset for the next processing step.

[0265] Step 2:

[0266] The terminal encrypts the data entered by the user and sends it to the server. This encryption uses the TLS protocol, ensuring secure transmission of data over the network. The input consists of formatted patient data, which is then encrypted and output as encrypted data. Once this encrypted data is sent to the server, the process proceeds to the next step.

[0267] Step 3:

[0268] The server decrypts the received data and checks its integrity before saving it to the database. The checking process verifies that the data items are accurate and complete. It receives encrypted data as input, decrypts it, and outputs it as clean, integrity-verified data. This output is then stored in a secure database.

[0269] Step 4:

[0270] The server uses stored data to leverage a generative AI model to generate visit records and treatment plan summaries. Decoded patient data is used as input, and the AI ​​generates summary text using natural language processing based on this data. This summary text is used to support rapid decision-making by experts.

[0271] Step 5:

[0272] The server distributes the generated summary in real time to the terminals of relevant experts using an information distribution system. In this step, the summary text output by the AI ​​model is received as input and output directly to the terminals of other experts. This allows all stakeholders to immediately grasp the latest patient information.

[0273] Step 6:

[0274] The terminal displays the distributed summary information to the user. Based on the displayed summary, the user checks the patient's condition and makes decisions regarding the next visit plan and treatment policy. The terminal also has a chat function for professionals, allowing the user to exchange information with other professionals as needed. The input is a summary text from the server, which is then visualized and output on the display screen.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In home care settings, the involvement of multiple specialists can lead to delays in sharing patient information and determining treatment plans, sometimes resulting in inconsistencies in information. Furthermore, information security must be considered, necessitating the protection of privacy. Additionally, there is a challenge to improve the quality of care by utilizing data related to caregiving operations over the long term.

[0278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes data processing means for integrating and managing information related to patients, information generation means for automatically generating visit records and automatically generating treatment plans using an artificial intelligence model, and dialogue means for providing a conversation function to facilitate communication among experts. As a result, information can be quickly shared even after home care visits, and the patient's condition can be notified to experts in real time. In addition, efficient data processing is possible while ensuring information protection, and data utilization for improving the quality of long-term care is also possible.

[0280] "Information related to patients" refers to data indicating the health status and living conditions of important subjects from the perspectives of medical care and nursing care.

[0281] "Data processing means for integrating and managing" is a mechanism for aggregating multiple data in a unified manner and efficiently storing and managing them.

[0282] "Artificial intelligence model" is an algorithm that has the ability to learn patterns and rules using a large amount of data and automatically extract insights from the data.

[0283] "Automatically generating visit records" refers to creating records with minimal manual input based on the information collected during a visit.

[0284] "Information generation means for automatically generating treatment plans" is a function for proposing an optimal treatment policy based on the health status of the subject.

[0285] "Conversation function for facilitating communication among experts" is a means of dialogue provided for multiple experts to share information with each other and cooperate in decision-making.

[0286] "Information security management means for ensuring information protection" is technical and organizational measures for preventing unauthorized access and leakage of data.

[0287] An "interface for efficiently transmitting user-inputted information" is a user interface that allows users to send data to a server concisely and quickly.

[0288] A "specialist" refers to someone who possesses advanced knowledge and skills in a specific field and performs work in that field.

[0289] In the system for implementing this invention, a server, terminals, and users function as the central components. The server manages real-time aggregated patient-related information and uses an artificial intelligence model to automatically generate visit records and treatment plans based on that information. Patient information is sent to the server, and after the data is protected by advanced security management measures, the generated information is immediately delivered to the specialist's terminal.

[0290] The terminal provides an interface that makes it easy for specialists to input new patient information immediately after a visit. By utilizing the terminal's built-in conversation function, specialists can communicate efficiently and share their knowledge with each other. The conversation function enables real-time information sharing using Socket.IO.

[0291] Users access each device using their smartphones or tablets and enter the patient's condition after the visit. The entered information is processed instantly on the server, and medical records are automatically summarized. This summarized information is quickly distributed to relevant professionals to inform future decisions and treatments.

[0292] The hardware used includes mobile devices such as smartphones and tablets. The software consists of an AI generation component built in Python, Firebase for data management, and React Native and Socket.IO for real-time communication.

[0293] As a concrete example, when a caregiver enters a patient's health data on a tablet during a home visit, that data is processed on a server, and a generating AI model summarizes the day's visit record. This summary is then quickly sent to the attending physician, who can use the data as a reference for future visits. An example of a prompt message would be, "Please summarize the patient's latest visit record and distribute it to the specialist."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The terminal receives data on the patient's health status and living conditions entered by the caregiver, who is the user. This terminal is equipped with a data input interface designed for intuitive information entry. The entered data is temporarily stored on the terminal.

[0297] Step 2:

[0298] The terminal encodes the input information and then transmits it to the server via the internet. The input includes patient vital data, symptoms, and observations during visits, generating data packets as output. The data is encrypted using JWT before transmission.

[0299] Step 3:

[0300] The server decodes the received data and stores it in a secure database. The data's integrity is checked, and any inconsistencies are verified before it is saved to the database. Here, the input is the data packet sent from the terminal, and the output is the storage of the encrypted data into the database.

[0301] Step 4:

[0302] The server generates a summary of the access records using an artificial intelligence model based on the stored data. The AI model creates a summary by analyzing the input data and extracting important information. In this step, patient data in the database is used as the input, and summary information is generated as the output.

[0303] Step 5:

[0304] The server distributes the generated summary information to the terminals of relevant experts in real time. The input here is the summary information from the AI model, and the output is the display of the summary on each expert's terminal. It is a mechanism where information is notified immediately through Socket.IO.

[0305] Step 6:

[0306] The terminal displays the received information and, if necessary, conducts further exchanges of opinions using the conversation function among experts. The input at this stage is the summary information from the server, and the output is the information displayed on the terminal and the responses and additional information from the user.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0308] This invention is a system for supporting information sharing and decision-making at the site of home care, and particularly incorporates an emotion engine for enabling communication considering the user's emotion. This emotion engine has the function of recognizing emotion from the user's expression, voice, text, etc. and providing personalized support.

[0309] The server integrates and securely stores patient information entered by each specialist. It automatically generates visit records and treatment plans using a generative model and delivers them in real time. This allows specialists to quickly decide on their next course of action. Furthermore, an emotion engine operates on the server, analyzing the user's emotional state. This analysis is shared with other specialists through dialogue tools, facilitating more appropriate communication.

[0310] The device not only provides an interface for sending user-inputted information to the server, but also conveys feedback from the emotion engine to the user. This allows the user to objectively understand their own state. The device also includes emotion-sensitive prompts and alerts, designed to reduce the user's psychological burden.

[0311] For example, when a nurse enters a patient's condition, the terminal monitors the user's emotional state in real time. If the nurse is stressed, the emotion engine detects this and reports it to the server. This information is then shared with other healthcare professionals, enabling them to provide prompt and appropriate support.

[0312] This system facilitates information sharing among experts and enables decision-making that takes emotional factors into account. Furthermore, it will include data analysis capabilities to analyze accumulated emotional data in the future and use that data to improve caregiving services, thereby contributing to the further development of the caregiving field.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The user uses a terminal to enter patient information. This information includes the patient's symptoms, vital signs, and treatment details. The terminal formats the entered information and prepares it for transmission to the server.

[0316] Step 2:

[0317] The terminal generates a packet containing the input data and sends it to the server using an encryption protocol. During this transmission process, the integrity and confidentiality of the information are ensured.

[0318] Step 3:

[0319] The server analyzes the data received from the terminal and verifies its integrity before saving it to the database. This process ensures that the data is managed consistently and securely.

[0320] Step 4:

[0321] The server uses a generative model to automatically generate visit records and treatment plan summaries from stored data. This generated information is distributed in real time to relevant professionals, enabling rapid action.

[0322] Step 5:

[0323] The emotion engine analyzes the user's voice and facial expression data to recognize their emotional state. It uses data obtained from the device's camera and microphone to determine emotions.

[0324] Step 6:

[0325] The server analyzes the emotional information recognized by the emotion engine and shares it with other experts as needed. Based on this information, the experts consider approaches that take into account the user's mental and physical state.

[0326] Step 7:

[0327] The device provides feedback to the user based on recognized emotional information. For example, if it determines that the user is highly stressed, it will display a message encouraging relaxation.

[0328] Step 8:

[0329] Users can use the device's chat function to interact with other experts and ask questions or seek information if needed. This chat provides support that takes emotional nuances into consideration.

[0330] Step 9:

[0331] The server regularly backs up all collected data and stores it in a secure data center. This storage ensures the permanent protection of the information.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0334] In home care settings, delays in information sharing among professionals and communication that disregards emotional aspects hinder appropriate decision-making. Furthermore, the inability to adequately assess the stress and emotional state of professionals themselves often leads to increased workload. These challenges need to be addressed.

[0335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0336] In this invention, the server includes input means for receiving and transmitting user input information to the server, data analysis means for analyzing emotions from the user's facial expressions, voice, and text data, and information sharing means for notifying other users of the analysis results generated using the emotion engine. This enables real-time information sharing among experts, facilitating quick and appropriate decision-making that takes emotional aspects into consideration.

[0337] An "input means" is an interface for receiving information from a user and sending that information to a server.

[0338] A "data analysis method" is a mechanism that analyzes emotions based on the user's facial expressions, voice, and text data.

[0339] "Information sharing means" refers to a function that notifies other users of sentiment analysis results and facilitates communication among experts.

[0340] A "feedback mechanism" is a function that provides users with appropriate prompts or alerts based on the results of sentiment analysis.

[0341] An "information distribution means" is a mechanism for delivering information transmitted by a user to a server in real time.

[0342] A "data analysis tool" is an analytical mechanism that uses data accumulated over a long period of time to discover new insights.

[0343] This invention aims to construct a system for facilitating information sharing and emotion analysis in home care. The server includes a database for integrating and securely managing user input information. This database stores data such as patient symptoms and vital signs entered by professionals such as nurses via terminals.

[0344] The server also features a generative AI model that automatically generates visit records and treatment plans. This generative model analyzes the input data to calculate effective visit schedules and treatment plans. Furthermore, an emotion engine analyzes the user's emotional state based on received voice, facial expression, and text data. The analysis results are immediately shared with relevant parties to support appropriate decision-making.

[0345] The terminal has an interface that provides users with easy operation. This allows users to easily input information and send it to the server. The terminal also provides feedback to the user based on sentiment analysis. For example, if the user is feeling stressed, this will be displayed on the terminal, and appropriate alerts and prompts will be provided.

[0346] As a concrete example, a prompt message such as "Please enter the patient's condition" is used. Upon receiving this prompt, the user enters the information, and the server processes it, providing appropriate feedback and analysis results to relevant specialists. This enables rapid and emotionally conscious decision-making in home care settings.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The user uses a terminal to input patient information. Specifically, they enter the patient's symptoms and vital signs in a text field and click the submit button. The input data includes the patient's name, symptoms, measured heart rate, and body temperature. As output, the terminal sends this information to the server.

[0350] Step 2:

[0351] The server securely stores the received patient information. The input data is the patient information sent in step 1, which is written to the database. Specifically, the server stores the information through the database management system, applying redundancy and encryption. As output, the information is passed to the generating AI model.

[0352] Step 3:

[0353] The server automatically generates visit records and treatment plans using a generative AI model. The input data is the patient information saved in step 2. Based on this data, the model performs analysis and outputs an effective visit schedule and treatment plan. Specifically, the model performs inference based on the trained data.

[0354] Step 4:

[0355] The server uses an emotion engine to analyze the user's (expert's) emotional state. Input data consists of audio and facial expressions. The analysis engine analyzes this data and outputs the user's stress and fatigue levels. Specifically, the server uses voice analysis software and facial recognition technology.

[0356] Step 5:

[0357] The server notifies the terminal of the analysis results, facilitating information sharing among experts. The input data consists of the generated results and emotion analysis results obtained in steps 3 and 4. As output, the generated treatment plan and emotion information are distributed to other experts. Specifically, the server sends messages using a notification system.

[0358] Step 6:

[0359] The device provides the user with feedback based on emotion analysis. The input data is the emotion analysis results received from the server in step 5. The device displays prompts and alerts to the user to reduce stress. Specifically, a message encouraging relaxation is displayed on the screen.

[0360] (Application Example 2)

[0361] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0362] Providing efficient care without considering emotional factors is a challenging task in information sharing and decision-making support within the home care setting. In particular, real-time emotional recognition and information sharing are essential to balance reducing the burden on care staff with providing emotional care to patients. Means to achieve this are needed.

[0363] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0364] In this invention, the server includes data processing means for integrating and managing patient information, information generation means for automatically generating visit records and treatment plans using a generative model, and emotion analysis means for analyzing emotional states using emotion recognition technology and providing feedback to the user. This makes it possible to provide care that takes emotional factors into consideration in real time in nursing care settings.

[0365] "Data processing means" refers to a function that provides a method for integrating and securely managing patient information.

[0366] "Information generation means" refers to a function that provides a method for automatically generating visit records and treatment plans using a generation model.

[0367] A "means of dialogue" refers to a function that provides methods to facilitate communication among experts and promote information exchange.

[0368] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions, voice, etc., to recognize their emotional state.

[0369] A "visual information presentation method" is a function that provides a way to display emotion-based suggestions to care staff in real time.

[0370] "Information management means" refers to a function that provides management methods to ensure the confidentiality and security of information.

[0371] The system for realizing this invention is constructed using various hardware and software. The server integrates and securely manages patient information using data processing means. By using a generative model, it automatically generates visit records and treatment plans, functioning as an information generation means. Furthermore, as an emotion analysis means, it analyzes the user's facial expressions and voice data to recognize their emotional state in real time. For this analysis, for example, general face recognition APIs and voice analysis APIs are used.

[0372] On the terminal side, emotionally-based suggestions are displayed to care staff through visual information presentation methods. This allows staff during visits to receive specific guidance to provide appropriate communication and care to patients. Information management systems ensure that this data is securely stored and facilitate information sharing among professionals.

[0373] As a concrete example, when a caregiver is visiting a patient with dementia, if the device recognizes the patient's face and detects that the patient is anxious, a suggestion such as "Please speak to the patient in a calm tone to reassure them" will be displayed on the device's screen. The AI ​​model will then receive the instruction "Patient XX is anxious. Please suggest ways to reassure them."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server receives patient facial expression data transmitted from the terminal. This data is in the form of images or videos captured by a camera. The input data is preprocessed using image processing algorithms and converted into a format suitable for the face recognition model. This process extracts facial feature points.

[0377] Step 2:

[0378] The server uses a face recognition API to analyze the emotional state based on the extracted facial feature points. This analysis process passes the input feature points through an emotion classification model, classifying them into emotional categories such as joy or anxiety. The output provides a specific emotional state and its probability.

[0379] Step 3:

[0380] The server generates and sends prompts to the generative AI model based on the results of the emotion analysis. An example prompt that can be generated is, "Patient XX is feeling anxious. Please suggest ways to reassure them." Based on the emotional state provided as input, the generative AI model generates appropriate care responses.

[0381] Step 4:

[0382] The server sends suggestions generated by the AI ​​model to the terminal. These suggestions are in text or audio format. The terminal displays the received suggestions on its screen, providing specific instructions to the care staff on what actions to take next. This output enables the staff to provide appropriate care to the patient.

[0383] Step 5:

[0384] The terminal sends staff responses and additional inputs to the server. This information is stored as system learning data and used to improve caregiving operations. In this step, the entered data is saved to a database and used for the next analysis.

[0385] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0387] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0391] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0392] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0393] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0394] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0395] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0396] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0397] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0398] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0399] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0400] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0401] This invention provides a comprehensive system to support home care settings. The system is designed as a platform usable by multiple professionals, each with access via a terminal. At its core, the system implements functions for integrated information management, enabling automated information generation and secure information sharing.

[0402] The server aggregates patient information sent from various specialists, checks its integrity, and then securely stores it in a database. This storage process involves data encryption to protect patient privacy. Based on the stored information, the server utilizes a generative model to automatically generate visit records and treatment plan summaries. This generated information is distributed in real time to all relevant specialists. The distributed information is immediately displayed on the user's device, allowing them to refer to and make decisions as needed.

[0403] The terminal provides an interface for efficiently transmitting user-inputted information to the server. This allows specialists to immediately input patient data after a visit and prepare for the next visit. The terminal also includes a chat function to facilitate communication among specialists. This enables users to quickly communicate with other specialists and share their expertise.

[0404] As a concrete example, suppose a nurse enters the patient's condition into a terminal after a home visit. The entered data is sent to a server, and a summary of the visit record is immediately generated. This summary is distributed to other doctors and caregivers and can be referenced during the next visit. Furthermore, if it is necessary to exchange opinions among professionals regarding important treatment plans, they can easily contact other professionals using the chat function and obtain the necessary information in a short time.

[0405] This system prioritizes information security, utilizing a secure domestic data center for server-side data management. Furthermore, the data accumulated in the system will be used for long-term data analysis to gain new insights into the field of elderly care. The knowledge gained from this analysis will contribute to the further development of the elderly care business.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The user uses a terminal to enter patient data after the visit. This information includes vital signs, observed symptoms, and procedures performed.

[0409] Step 2:

[0410] The terminal formats the input data into a predetermined format and sends it to the server using a secure communication protocol.

[0411] Step 3:

[0412] The server analyzes the data received from the terminal to verify its integrity and accuracy. If there are any problems with the data at this stage, an error message is sent to the terminal.

[0413] Step 4:

[0414] The server stores data that has been verified for integrity in the database. During the storage process, the data is encrypted to protect privacy.

[0415] Step 5:

[0416] The server uses a generative model to automatically generate a summary of visit records and a treatment plan from the received data. This process references past data and guidelines.

[0417] Step 6:

[0418] The server distributes the generated summary and treatment plan to the terminals of all relevant professionals, allowing them to receive information in real time.

[0419] Step 7:

[0420] The device receives the delivered information and displays it to the user. The user can then use this information to prepare for their next visit.

[0421] Step 8:

[0422] Users can use the device's chat function as needed to share information and exchange opinions with other experts. This chat function also saves the history of the conversations.

[0423] Step 9:

[0424] The server regularly backs up all data, including chat logs, and stores it in a secure data center within the country. This protects against data loss.

[0425] (Example 1)

[0426] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0427] In home care settings, the involvement of multiple specialists complicates the aggregation and management of patient information, requiring rapid and accurate information sharing while ensuring data consistency and security. Furthermore, there is a lack of automation for visit records and treatment plans, as well as mechanisms to facilitate smooth communication among specialists, which needs to be addressed.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes data management means for aggregating and verifying the integrity of data input from terminals, information security management means for encrypting and securely transmitting and receiving data using a set protocol, and information generation means for automatically generating visit records and treatment plan summaries using a generation AI model. This enables more efficient information sharing among multiple experts, ensures the security of information, reduces workload through automation, and facilitates smooth communication among experts.

[0430] A "terminal" is a device that provides an interface for users to input information and send that information to a server.

[0431] A "data management system" is a function that collects data entered from terminals, verifies data integrity, and stores it in a database.

[0432] "Information security management measures" refer to functions that encrypt data using a configured protocol and send and receive it securely.

[0433] A "generative AI model" is an artificial intelligence technology that automatically generates visit records and treatment plan summaries based on input data.

[0434] The "information generation means" refers to a function that uses a generation AI model to automatically generate visit records and treatment plan summaries based on input data.

[0435] "Information distribution means" refers to a function for distributing generated summaries in real time among relevant experts.

[0436] "Dialogue support tools" refer to functions that provide chat capabilities and other features to facilitate communication between experts.

[0437] "Planning support tools" refer to functions for formulating optimized visit plans based on the patient's condition.

[0438] A "data storage method" is a function for accumulating collected data over the long term.

[0439] "Data analysis tools" are functions that analyze accumulated data to discover new insights.

[0440] This embodiment of the invention is a system for efficiently managing patient information and promoting information sharing among professionals in home care settings. This system includes terminals and a server, and provides various functions for each professional to input and utilize information.

[0441] The terminal provides an interface for users to input the patient's condition after their visit. This user interface is configured using hardware such as tablets or personal computers. The entered data is accurately and quickly transmitted to the server using text boxes and dropdown menus.

[0442] The server receives data sent from the terminal and verifies its integrity. Data security is ensured by using TLS (Transport Layer Security) encryption technology. The verified data is stored in a database, and an AI model automatically generates visit records and treatment plan summaries. This AI model utilizes natural language processing technology to extract meaning from the data and generate summaries in an easy-to-understand format.

[0443] The generated information is shared in real time among experts through information distribution channels. This allows experts to respond quickly and accurately based on the latest patient information. The terminal also features a chat function, allowing experts to exchange opinions with other experts as needed. This function is highly effective in situations requiring immediacy, facilitating information sharing and smooth communication.

[0444] As a concrete example, consider a scenario where a nurse inputs the patient's blood pressure and temperature measurements into a terminal. Once this information is sent to the server, the AI ​​model generates a summary such as, "The patient's blood pressure is stable. Please proceed with the next visit as scheduled." This summary is then distributed to the terminals of other relevant doctors and caregivers and used to plan the next care session.

[0445] Examples of prompts for the generating AI model include, "Automatically generate a visit record based on the patient's health status today." In this way, this system supports the efficiency and quality improvement of home care.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] The user uses a terminal to input the patient's condition after providing home care. The input includes the patient's vital data (blood pressure, temperature, heart rate, etc.) and observations. This information is formatted as digital data based on the input form on the terminal. The final formatted data is then prepared as a dataset for the next processing step.

[0449] Step 2:

[0450] The terminal encrypts the data entered by the user and sends it to the server. This encryption uses the TLS protocol, ensuring secure transmission of data over the network. The input consists of formatted patient data, which is then encrypted and output as encrypted data. Once this encrypted data is sent to the server, the process proceeds to the next step.

[0451] Step 3:

[0452] The server decrypts the received data and checks its integrity before saving it to the database. The checking process verifies that the data items are accurate and complete. It receives encrypted data as input, decrypts it, and outputs it as clean, integrity-verified data. This output is then stored in a secure database.

[0453] Step 4:

[0454] The server uses stored data to leverage a generative AI model to generate visit records and treatment plan summaries. Decoded patient data is used as input, and the AI ​​generates summary text using natural language processing based on this data. This summary text is used to support rapid decision-making by experts.

[0455] Step 5:

[0456] The server distributes the generated summary in real time to the terminals of relevant experts using an information distribution system. In this step, the summary text output by the AI ​​model is received as input and output directly to the terminals of other experts. This allows all stakeholders to immediately grasp the latest patient information.

[0457] Step 6:

[0458] The terminal displays the distributed summary information to the user. Based on the displayed summary, the user checks the patient's condition and makes decisions regarding the next visit plan and treatment policy. The terminal also has a chat function for professionals, allowing the user to exchange information with other professionals as needed. The input is a summary text from the server, which is then visualized and output on the display screen.

[0459] (Application Example 1)

[0460] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] In home care settings, the involvement of multiple specialists can lead to delays in sharing patient information and determining treatment plans, sometimes resulting in inconsistencies in information. Furthermore, information security must be considered, necessitating the protection of privacy. Additionally, there is a challenge to improve the quality of care by utilizing data related to caregiving operations over the long term.

[0462] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0463] In this invention, the server includes data processing means for integrating and managing patient-related information, information generation means for automatically generating visit records and treatment plans using an artificial intelligence model, and dialogue means for providing conversational functions to facilitate communication among professionals. This enables rapid information sharing even after home care visits, and allows professionals to be notified of the patient's condition in real time. Furthermore, it enables efficient data processing while ensuring the protection of information, and allows for the use of data to improve the quality of long-term care.

[0464] "Patient-related information" refers to data that indicates the health status and living conditions of individuals who are important from a medical and nursing care perspective.

[0465] A "data processing system for integrated management" is a mechanism for centrally aggregating multiple data sets and efficiently storing and managing them.

[0466] An "artificial intelligence model" is an algorithm that learns patterns and rules using large amounts of data and has the ability to automatically extract insights from that data.

[0467] "Automatic generation of visit records" refers to creating records based on information collected during a visit, minimizing manual input.

[0468] "Information generation means for automatically generating treatment plans" refers to a function that proposes the optimal treatment plan based on the health status of the subject.

[0469] A "dialogue function to facilitate communication among experts" refers to a means of dialogue established to enable multiple experts to share information with each other and make decisions collaboratively.

[0470] "Information security management measures to ensure the protection of information" refer to technical and organizational measures to prevent unauthorized access to and leakage of data.

[0471] An "interface for efficiently transmitting user-inputted information" is a user interface that allows users to send data to a server concisely and quickly.

[0472] A "specialist" refers to someone who possesses advanced knowledge and skills in a specific field and performs work in that field.

[0473] In the system for implementing this invention, a server, terminals, and users function as the central components. The server manages real-time aggregated patient-related information and uses an artificial intelligence model to automatically generate visit records and treatment plans based on that information. Patient information is sent to the server, and after the data is protected by advanced security management measures, the generated information is immediately delivered to the specialist's terminal.

[0474] The terminal provides an interface that makes it easy for specialists to input new patient information immediately after a visit. By utilizing the terminal's built-in conversation function, specialists can communicate efficiently and share their knowledge with each other. The conversation function enables real-time information sharing using Socket.IO.

[0475] Users access each device using their smartphones or tablets and enter the patient's condition after the visit. The entered information is processed instantly on the server, and medical records are automatically summarized. This summarized information is quickly distributed to relevant professionals to inform future decisions and treatments.

[0476] The hardware used includes mobile devices such as smartphones and tablets. The software consists of an AI generation component built in Python, Firebase for data management, and React Native and Socket.IO for real-time communication.

[0477] As a concrete example, when a caregiver enters a patient's health data on a tablet during a home visit, that data is processed on a server, and a generating AI model summarizes the day's visit record. This summary is then quickly sent to the attending physician, who can use the data as a reference for future visits. An example of a prompt message would be, "Please summarize the patient's latest visit record and distribute it to the specialist."

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The terminal receives data on the patient's health status and living conditions entered by the caregiver, who is the user. This terminal is equipped with a data input interface designed for intuitive information entry. The entered data is temporarily stored on the terminal.

[0481] Step 2:

[0482] The terminal encodes the input information and then transmits it to the server via the internet. The input includes patient vital data, symptoms, and observations during visits, generating data packets as output. The data is encrypted using JWT before transmission.

[0483] Step 3:

[0484] The server decodes the received data and stores it in a secure database. The data's integrity is checked, and any inconsistencies are verified before it is saved to the database. Here, the input is the data packet sent from the terminal, and the output is the storage of the encrypted data into the database.

[0485] Step 4:

[0486] The server uses an artificial intelligence model based on the stored data to generate a summary of visit records. The AI ​​model creates the summary by analyzing the input data and extracting important information. In this step, patient data from the database is used as input, and summary information is generated as output.

[0487] Step 5:

[0488] The server delivers the generated summary information to the terminals of relevant experts in real time. The input here is the summary information from the AI ​​model, and the output is the display of the summary on each expert's terminal. Information is notified instantly via Socket.IO.

[0489] Step 6:

[0490] The terminal displays the received information and, if necessary, uses a conversation function between experts to exchange further opinions. At this stage, the input is summarized information from the server, and the output is the information displayed on the terminal, along with user responses and additional information.

[0491] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0492] This invention is a system designed to support information sharing and decision-making in home care settings, and in particular, incorporates an emotion engine to enable communication that takes user emotions into consideration. This emotion engine has the function of recognizing emotions from the user's facial expressions, voice, text, etc., and providing personalized support.

[0493] The server integrates and securely stores patient information entered by each specialist. It automatically generates visit records and treatment plans using a generative model and delivers them in real time. This allows specialists to quickly decide on their next course of action. Furthermore, an emotion engine operates on the server, analyzing the user's emotional state. This analysis is shared with other specialists through dialogue tools, facilitating more appropriate communication.

[0494] The device not only provides an interface for sending user-inputted information to the server, but also conveys feedback from the emotion engine to the user. This allows the user to objectively understand their own state. The device also includes emotion-sensitive prompts and alerts, designed to reduce the user's psychological burden.

[0495] For example, when a nurse enters a patient's condition, the terminal monitors the user's emotional state in real time. If the nurse is stressed, the emotion engine detects this and reports it to the server. This information is then shared with other healthcare professionals, enabling them to provide prompt and appropriate support.

[0496] This system facilitates information sharing among experts and enables decision-making that takes emotional factors into account. Furthermore, it will include data analysis capabilities to analyze accumulated emotional data in the future and use that data to improve caregiving services, thereby contributing to the further development of the caregiving field.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user uses a terminal to enter patient information. This information includes the patient's symptoms, vital signs, and treatment details. The terminal formats the entered information and prepares it for transmission to the server.

[0500] Step 2:

[0501] The terminal generates a packet containing the input data and sends it to the server using an encryption protocol. During this transmission process, the integrity and confidentiality of the information are ensured.

[0502] Step 3:

[0503] The server analyzes the data received from the terminal and verifies its integrity before saving it to the database. This process ensures that the data is managed consistently and securely.

[0504] Step 4:

[0505] The server uses a generative model to automatically generate visit records and treatment plan summaries from stored data. This generated information is distributed in real time to relevant professionals, enabling rapid action.

[0506] Step 5:

[0507] The emotion engine analyzes the user's voice and facial expression data to recognize their emotional state. It uses data obtained from the device's camera and microphone to determine emotions.

[0508] Step 6:

[0509] The server analyzes the emotional information recognized by the emotion engine and shares it with other experts as needed. Based on this information, the experts consider approaches that take into account the user's mental and physical state.

[0510] Step 7:

[0511] The device provides feedback to the user based on recognized emotional information. For example, if it determines that the user is highly stressed, it will display a message encouraging relaxation.

[0512] Step 8:

[0513] Users can use the device's chat function to interact with other experts and ask questions or seek information if needed. This chat provides support that takes emotional nuances into consideration.

[0514] Step 9:

[0515] The server regularly backs up all collected data and stores it in a secure data center. This storage ensures the permanent protection of the information.

[0516] (Example 2)

[0517] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In home care settings, delays in information sharing among professionals and communication that disregards emotional aspects hinder appropriate decision-making. Furthermore, the inability to adequately assess the stress and emotional state of professionals themselves often leads to increased workload. These challenges need to be addressed.

[0519] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0520] In this invention, the server includes input means for receiving and transmitting user input information to the server, data analysis means for analyzing emotions from the user's facial expressions, voice, and text data, and information sharing means for notifying other users of the analysis results generated using the emotion engine. This enables real-time information sharing among experts, facilitating quick and appropriate decision-making that takes emotional aspects into consideration.

[0521] An "input means" is an interface for receiving information from a user and sending that information to a server.

[0522] A "data analysis method" is a mechanism that analyzes emotions based on the user's facial expressions, voice, and text data.

[0523] "Information sharing means" refers to a function that notifies other users of sentiment analysis results and facilitates communication among experts.

[0524] A "feedback mechanism" is a function that provides users with appropriate prompts or alerts based on the results of sentiment analysis.

[0525] An "information distribution means" is a mechanism for delivering information transmitted by a user to a server in real time.

[0526] A "data analysis tool" is an analytical mechanism that uses data accumulated over a long period of time to discover new insights.

[0527] This invention aims to construct a system for facilitating information sharing and emotion analysis in home care. The server includes a database for integrating and securely managing user input information. This database stores data such as patient symptoms and vital signs entered by professionals such as nurses via terminals.

[0528] The server also features a generative AI model that automatically generates visit records and treatment plans. This generative model analyzes the input data to calculate effective visit schedules and treatment plans. Furthermore, an emotion engine analyzes the user's emotional state based on received voice, facial expression, and text data. The analysis results are immediately shared with relevant parties to support appropriate decision-making.

[0529] The terminal has an interface that provides users with easy operation. This allows users to easily input information and send it to the server. The terminal also provides feedback to the user based on sentiment analysis. For example, if the user is feeling stressed, this will be displayed on the terminal, and appropriate alerts and prompts will be provided.

[0530] As a concrete example, a prompt message such as "Please enter the patient's condition" is used. Upon receiving this prompt, the user enters the information, and the server processes it, providing appropriate feedback and analysis results to relevant specialists. This enables rapid and emotionally conscious decision-making in home care settings.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The user uses a terminal to input patient information. Specifically, they enter the patient's symptoms and vital signs in a text field and click the submit button. The input data includes the patient's name, symptoms, measured heart rate, and body temperature. As output, the terminal sends this information to the server.

[0534] Step 2:

[0535] The server securely stores the received patient information. The input data is the patient information sent in step 1, which is written to the database. Specifically, the server stores the information through the database management system, applying redundancy and encryption. As output, the information is passed to the generating AI model.

[0536] Step 3:

[0537] The server automatically generates visit records and treatment plans using a generative AI model. The input data is the patient information saved in step 2. Based on this data, the model performs analysis and outputs an effective visit schedule and treatment plan. Specifically, the model performs inference based on the trained data.

[0538] Step 4:

[0539] The server uses an emotion engine to analyze the user's (expert's) emotional state. Input data consists of audio and facial expressions. The analysis engine analyzes this data and outputs the user's stress and fatigue levels. Specifically, the server uses voice analysis software and facial recognition technology.

[0540] Step 5:

[0541] The server notifies the terminal of the analysis results, facilitating information sharing among experts. The input data consists of the generated results and emotion analysis results obtained in steps 3 and 4. As output, the generated treatment plan and emotion information are distributed to other experts. Specifically, the server sends messages using a notification system.

[0542] Step 6:

[0543] The device provides the user with feedback based on emotion analysis. The input data is the emotion analysis results received from the server in step 5. The device displays prompts and alerts to the user to reduce stress. Specifically, a message encouraging relaxation is displayed on the screen.

[0544] (Application Example 2)

[0545] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0546] Providing efficient care without considering emotional factors is a challenging task in information sharing and decision-making support within the home care setting. In particular, real-time emotional recognition and information sharing are essential to balance reducing the burden on care staff with providing emotional care to patients. Means to achieve this are needed.

[0547] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0548] In this invention, the server includes data processing means for integrating and managing patient information, information generation means for automatically generating visit records and treatment plans using a generative model, and emotion analysis means for analyzing emotional states using emotion recognition technology and providing feedback to the user. This makes it possible to provide care that takes emotional factors into consideration in real time in nursing care settings.

[0549] "Data processing means" refers to a function that provides a method for integrating and securely managing patient information.

[0550] "Information generation means" refers to a function that provides a method for automatically generating visit records and treatment plans using a generation model.

[0551] A "means of dialogue" refers to a function that provides methods to facilitate communication among experts and promote information exchange.

[0552] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions, voice, etc., to recognize their emotional state.

[0553] A "visual information presentation method" is a function that provides a way to display emotion-based suggestions to care staff in real time.

[0554] "Information management means" refers to a function that provides management methods to ensure the confidentiality and security of information.

[0555] The system for realizing this invention is constructed using various hardware and software. The server integrates and securely manages patient information using data processing means. By using a generative model, it automatically generates visit records and treatment plans, functioning as an information generation means. Furthermore, as an emotion analysis means, it analyzes the user's facial expressions and voice data to recognize their emotional state in real time. For this analysis, for example, general face recognition APIs and voice analysis APIs are used.

[0556] On the terminal side, emotionally-based suggestions are displayed to care staff through visual information presentation methods. This allows staff during visits to receive specific guidance to provide appropriate communication and care to patients. Information management systems ensure that this data is securely stored and facilitate information sharing among professionals.

[0557] As a concrete example, when a caregiver is visiting a patient with dementia, if the device recognizes the patient's face and detects that the patient is anxious, a suggestion such as "Please speak to the patient in a calm tone to reassure them" will be displayed on the device's screen. The AI ​​model will then receive the instruction "Patient XX is anxious. Please suggest ways to reassure them."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server receives patient facial expression data transmitted from the terminal. This data is in the form of images or videos captured by a camera. The input data is preprocessed using image processing algorithms and converted into a format suitable for the face recognition model. This process extracts facial feature points.

[0561] Step 2:

[0562] The server uses a face recognition API to analyze the emotional state based on the extracted facial feature points. This analysis process passes the input feature points through an emotion classification model, classifying them into emotional categories such as joy or anxiety. The output provides a specific emotional state and its probability.

[0563] Step 3:

[0564] The server generates and sends prompts to the generative AI model based on the results of the emotion analysis. An example prompt that can be generated is, "Patient XX is feeling anxious. Please suggest ways to reassure them." Based on the emotional state provided as input, the generative AI model generates appropriate care responses.

[0565] Step 4:

[0566] The server sends suggestions generated by the AI ​​model to the terminal. These suggestions are in text or audio format. The terminal displays the received suggestions on its screen, providing specific instructions to the care staff on what actions to take next. This output enables the staff to provide appropriate care to the patient.

[0567] Step 5:

[0568] The terminal sends staff responses and additional inputs to the server. This information is stored as system learning data and used to improve caregiving operations. In this step, the entered data is saved to a database and used for the next analysis.

[0569] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0570] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0571] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0575] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0576] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0577] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0578] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0579] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0580] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0581] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0582] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0583] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0584] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0585] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0586] This invention provides a comprehensive system to support home care settings. The system is designed as a platform usable by multiple professionals, each with access via a terminal. At its core, the system implements functions for integrated information management, enabling automated information generation and secure information sharing.

[0587] The server aggregates patient information sent from various specialists, checks its integrity, and then securely stores it in a database. This storage process involves data encryption to protect patient privacy. Based on the stored information, the server utilizes a generative model to automatically generate visit records and treatment plan summaries. This generated information is distributed in real time to all relevant specialists. The distributed information is immediately displayed on the user's device, allowing them to refer to and make decisions as needed.

[0588] The terminal provides an interface for efficiently transmitting user-inputted information to the server. This allows specialists to immediately input patient data after a visit and prepare for the next visit. The terminal also includes a chat function to facilitate communication among specialists. This enables users to quickly communicate with other specialists and share their expertise.

[0589] As a concrete example, suppose a nurse enters the patient's condition into a terminal after a home visit. The entered data is sent to a server, and a summary of the visit record is immediately generated. This summary is distributed to other doctors and caregivers and can be referenced during the next visit. Furthermore, if it is necessary to exchange opinions among professionals regarding important treatment plans, they can easily contact other professionals using the chat function and obtain the necessary information in a short time.

[0590] This system prioritizes information security, utilizing a secure domestic data center for server-side data management. Furthermore, the data accumulated in the system will be used for long-term data analysis to gain new insights into the field of elderly care. The knowledge gained from this analysis will contribute to the further development of the elderly care business.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The user uses a terminal to enter patient data after the visit. This information includes vital signs, observed symptoms, and procedures performed.

[0594] Step 2:

[0595] The terminal formats the input data into a predetermined format and sends it to the server using a secure communication protocol.

[0596] Step 3:

[0597] The server analyzes the data received from the terminal to verify its integrity and accuracy. If there are any problems with the data at this stage, an error message is sent to the terminal.

[0598] Step 4:

[0599] The server stores data that has been verified for integrity in the database. During the storage process, the data is encrypted to protect privacy.

[0600] Step 5:

[0601] The server uses a generative model to automatically generate a summary of visit records and a treatment plan from the received data. This process references past data and guidelines.

[0602] Step 6:

[0603] The server distributes the generated summary and treatment plan to the terminals of all relevant professionals, allowing them to receive information in real time.

[0604] Step 7:

[0605] The device receives the delivered information and displays it to the user. The user can then use this information to prepare for their next visit.

[0606] Step 8:

[0607] Users can use the device's chat function as needed to share information and exchange opinions with other experts. This chat function also saves the history of the conversations.

[0608] Step 9:

[0609] The server regularly backs up all data, including chat logs, and stores it in a secure data center within the country. This protects against data loss.

[0610] (Example 1)

[0611] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] In home care settings, the involvement of multiple specialists complicates the aggregation and management of patient information, requiring rapid and accurate information sharing while ensuring data consistency and security. Furthermore, there is a lack of automation for visit records and treatment plans, as well as mechanisms to facilitate smooth communication among specialists, which needs to be addressed.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0614] In this invention, the server includes data management means for aggregating and verifying the integrity of data input from terminals, information security management means for encrypting and securely transmitting and receiving data using a set protocol, and information generation means for automatically generating visit records and treatment plan summaries using a generation AI model. This enables more efficient information sharing among multiple experts, ensures the security of information, reduces workload through automation, and facilitates smooth communication among experts.

[0615] A "terminal" is a device that provides an interface for users to input information and send that information to a server.

[0616] A "data management system" is a function that collects data entered from terminals, verifies data integrity, and stores it in a database.

[0617] "Information security management measures" refer to functions that encrypt data using a configured protocol and send and receive it securely.

[0618] A "generative AI model" is an artificial intelligence technology that automatically generates visit records and treatment plan summaries based on input data.

[0619] The "information generation means" refers to a function that uses a generation AI model to automatically generate visit records and treatment plan summaries based on input data.

[0620] "Information distribution means" refers to a function for distributing generated summaries in real time among relevant experts.

[0621] "Dialogue support tools" refer to functions that provide chat capabilities and other features to facilitate communication between experts.

[0622] "Planning support tools" refer to functions for formulating optimized visit plans based on the patient's condition.

[0623] A "data storage method" is a function for accumulating collected data over the long term.

[0624] "Data analysis tools" are functions that analyze accumulated data to discover new insights.

[0625] This embodiment of the invention is a system for efficiently managing patient information and promoting information sharing among professionals in home care settings. This system includes terminals and a server, and provides various functions for each professional to input and utilize information.

[0626] The terminal provides an interface for users to input the patient's condition after their visit. This user interface is configured using hardware such as tablets or personal computers. The entered data is accurately and quickly transmitted to the server using text boxes and dropdown menus.

[0627] The server receives data sent from the terminal and verifies its integrity. Data security is ensured by using TLS (Transport Layer Security) encryption technology. The verified data is stored in a database, and an AI model automatically generates visit records and treatment plan summaries. This AI model utilizes natural language processing technology to extract meaning from the data and generate summaries in an easy-to-understand format.

[0628] The generated information is shared in real time among experts through information distribution channels. This allows experts to respond quickly and accurately based on the latest patient information. The terminal also features a chat function, allowing experts to exchange opinions with other experts as needed. This function is highly effective in situations requiring immediacy, facilitating information sharing and smooth communication.

[0629] As a concrete example, consider a scenario where a nurse inputs the patient's blood pressure and temperature measurements into a terminal. Once this information is sent to the server, the AI ​​model generates a summary such as, "The patient's blood pressure is stable. Please proceed with the next visit as scheduled." This summary is then distributed to the terminals of other relevant doctors and caregivers and used to plan the next care session.

[0630] Examples of prompts for the generating AI model include, "Automatically generate a visit record based on the patient's health status today." In this way, this system supports the efficiency and quality improvement of home care.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The user uses a terminal to input the patient's condition after providing home care. The input includes the patient's vital data (blood pressure, temperature, heart rate, etc.) and observations. This information is formatted as digital data based on the input form on the terminal. The final formatted data is then prepared as a dataset for the next processing step.

[0634] Step 2:

[0635] The terminal encrypts the data entered by the user and sends it to the server. This encryption uses the TLS protocol, ensuring secure transmission of data over the network. The input consists of formatted patient data, which is then encrypted and output as encrypted data. Once this encrypted data is sent to the server, the process proceeds to the next step.

[0636] Step 3:

[0637] The server decrypts the received data and checks its integrity before saving it to the database. The checking process verifies that the data items are accurate and complete. It receives encrypted data as input, decrypts it, and outputs it as clean, integrity-verified data. This output is then stored in a secure database.

[0638] Step 4:

[0639] The server uses stored data to leverage a generative AI model to generate visit records and treatment plan summaries. Decoded patient data is used as input, and the AI ​​generates summary text using natural language processing based on this data. This summary text is used to support rapid decision-making by experts.

[0640] Step 5:

[0641] The server distributes the generated summary in real time to the terminals of relevant experts using an information distribution system. In this step, the summary text output by the AI ​​model is received as input and output directly to the terminals of other experts. This allows all stakeholders to immediately grasp the latest patient information.

[0642] Step 6:

[0643] The terminal displays the distributed summary information to the user. Based on the displayed summary, the user checks the patient's condition and makes decisions regarding the next visit plan and treatment policy. The terminal also has a chat function for professionals, allowing the user to exchange information with other professionals as needed. The input is a summary text from the server, which is then visualized and output on the display screen.

[0644] (Application Example 1)

[0645] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In home care settings, the involvement of multiple specialists can lead to delays in sharing patient information and determining treatment plans, sometimes resulting in inconsistencies in information. Furthermore, information security must be considered, necessitating the protection of privacy. Additionally, there is a challenge to improve the quality of care by utilizing data related to caregiving operations over the long term.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0648] In this invention, the server includes data processing means for integrating and managing patient-related information, information generation means for automatically generating visit records and treatment plans using an artificial intelligence model, and dialogue means for providing conversational functions to facilitate communication among professionals. This enables rapid information sharing even after home care visits, and allows professionals to be notified of the patient's condition in real time. Furthermore, it enables efficient data processing while ensuring the protection of information, and allows for the use of data to improve the quality of long-term care.

[0649] "Patient-related information" refers to data that indicates the health status and living conditions of individuals who are important from a medical and nursing care perspective.

[0650] A "data processing system for integrated management" is a mechanism for centrally aggregating multiple data sets and efficiently storing and managing them.

[0651] An "artificial intelligence model" is an algorithm that learns patterns and rules using large amounts of data and has the ability to automatically extract insights from that data.

[0652] "Automatic generation of visit records" refers to creating records based on information collected during a visit, minimizing manual input.

[0653] "Information generation means for automatically generating treatment plans" refers to a function that proposes the optimal treatment plan based on the health status of the subject.

[0654] A "dialogue function to facilitate communication among experts" refers to a means of dialogue established to enable multiple experts to share information with each other and make decisions collaboratively.

[0655] "Information security management measures to ensure the protection of information" refer to technical and organizational measures to prevent unauthorized access to and leakage of data.

[0656] An "interface for efficiently transmitting user-inputted information" is a user interface that allows users to send data to a server concisely and quickly.

[0657] A "specialist" refers to someone who possesses advanced knowledge and skills in a specific field and performs work in that field.

[0658] In the system for implementing this invention, a server, terminals, and users function as the central components. The server manages real-time aggregated patient-related information and uses an artificial intelligence model to automatically generate visit records and treatment plans based on that information. Patient information is sent to the server, and after the data is protected by advanced security management measures, the generated information is immediately delivered to the specialist's terminal.

[0659] The terminal provides an interface that makes it easy for specialists to input new patient information immediately after a visit. By utilizing the terminal's built-in conversation function, specialists can communicate efficiently and share their knowledge with each other. The conversation function enables real-time information sharing using Socket.IO.

[0660] Users access each device using their smartphones or tablets and enter the patient's condition after the visit. The entered information is processed instantly on the server, and medical records are automatically summarized. This summarized information is quickly distributed to relevant professionals to inform future decisions and treatments.

[0661] The hardware used includes mobile devices such as smartphones and tablets. The software consists of an AI generation component built in Python, Firebase for data management, and React Native and Socket.IO for real-time communication.

[0662] As a concrete example, when a caregiver enters a patient's health data on a tablet during a home visit, that data is processed on a server, and a generating AI model summarizes the day's visit record. This summary is then quickly sent to the attending physician, who can use the data as a reference for future visits. An example of a prompt message would be, "Please summarize the patient's latest visit record and distribute it to the specialist."

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The terminal receives data on the patient's health status and living conditions entered by the caregiver, who is the user. This terminal is equipped with a data input interface designed for intuitive information entry. The entered data is temporarily stored on the terminal.

[0666] Step 2:

[0667] The terminal encodes the input information and then transmits it to the server via the internet. The input includes patient vital data, symptoms, and observations during visits, generating data packets as output. The data is encrypted using JWT before transmission.

[0668] Step 3:

[0669] The server decodes the received data and stores it in a secure database. The data's integrity is checked, and any inconsistencies are verified before it is saved to the database. Here, the input is the data packet sent from the terminal, and the output is the storage of the encrypted data into the database.

[0670] Step 4:

[0671] The server uses an artificial intelligence model based on the stored data to generate a summary of visit records. The AI ​​model creates the summary by analyzing the input data and extracting important information. In this step, patient data from the database is used as input, and summary information is generated as output.

[0672] Step 5:

[0673] The server delivers the generated summary information to the terminals of relevant experts in real time. The input here is the summary information from the AI ​​model, and the output is the display of the summary on each expert's terminal. Information is notified instantly via Socket.IO.

[0674] Step 6:

[0675] The terminal displays the received information and, if necessary, uses a conversation function between experts to exchange further opinions. At this stage, the input is summarized information from the server, and the output is the information displayed on the terminal, along with user responses and additional information.

[0676] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0677] This invention is a system designed to support information sharing and decision-making in home care settings, and in particular, incorporates an emotion engine to enable communication that takes user emotions into consideration. This emotion engine has the function of recognizing emotions from the user's facial expressions, voice, text, etc., and providing personalized support.

[0678] The server integrates and securely stores patient information entered by each specialist. It automatically generates visit records and treatment plans using a generative model and delivers them in real time. This allows specialists to quickly decide on their next course of action. Furthermore, an emotion engine operates on the server, analyzing the user's emotional state. This analysis is shared with other specialists through dialogue tools, facilitating more appropriate communication.

[0679] The device not only provides an interface for sending user-inputted information to the server, but also conveys feedback from the emotion engine to the user. This allows the user to objectively understand their own state. The device also includes emotion-sensitive prompts and alerts, designed to reduce the user's psychological burden.

[0680] For example, when a nurse enters a patient's condition, the terminal monitors the user's emotional state in real time. If the nurse is stressed, the emotion engine detects this and reports it to the server. This information is then shared with other healthcare professionals, enabling them to provide prompt and appropriate support.

[0681] This system facilitates information sharing among experts and enables decision-making that takes emotional factors into account. Furthermore, it will include data analysis capabilities to analyze accumulated emotional data in the future and use that data to improve caregiving services, thereby contributing to the further development of the caregiving field.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] The user uses a terminal to enter patient information. This information includes the patient's symptoms, vital signs, and treatment details. The terminal formats the entered information and prepares it for transmission to the server.

[0685] Step 2:

[0686] The terminal generates a packet containing the input data and sends it to the server using an encryption protocol. During this transmission process, the integrity and confidentiality of the information are ensured.

[0687] Step 3:

[0688] The server analyzes the data received from the terminal and verifies its integrity before saving it to the database. This process ensures that the data is managed consistently and securely.

[0689] Step 4:

[0690] The server uses a generative model to automatically generate visit records and treatment plan summaries from stored data. This generated information is distributed in real time to relevant professionals, enabling rapid action.

[0691] Step 5:

[0692] The emotion engine analyzes the user's voice and facial expression data to recognize their emotional state. It uses data obtained from the device's camera and microphone to determine emotions.

[0693] Step 6:

[0694] The server analyzes the emotional information recognized by the emotion engine and shares it with other experts as needed. Based on this information, the experts consider approaches that take into account the user's mental and physical state.

[0695] Step 7:

[0696] The device provides feedback to the user based on recognized emotional information. For example, if it determines that the user is highly stressed, it will display a message encouraging relaxation.

[0697] Step 8:

[0698] Users can use the device's chat function to interact with other experts and ask questions or seek information if needed. This chat provides support that takes emotional nuances into consideration.

[0699] Step 9:

[0700] The server regularly backs up all collected data and stores it in a secure data center. This storage ensures the permanent protection of the information.

[0701] (Example 2)

[0702] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In home care settings, delays in information sharing among professionals and communication that disregards emotional aspects hinder appropriate decision-making. Furthermore, the inability to adequately assess the stress and emotional state of professionals themselves often leads to increased workload. These challenges need to be addressed.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0705] In this invention, the server includes input means for receiving and transmitting user input information to the server, data analysis means for analyzing emotions from the user's facial expressions, voice, and text data, and information sharing means for notifying other users of the analysis results generated using the emotion engine. This enables real-time information sharing among experts, facilitating quick and appropriate decision-making that takes emotional aspects into consideration.

[0706] An "input means" is an interface for receiving information from a user and sending that information to a server.

[0707] A "data analysis method" is a mechanism that analyzes emotions based on the user's facial expressions, voice, and text data.

[0708] "Information sharing means" refers to a function that notifies other users of sentiment analysis results and facilitates communication among experts.

[0709] A "feedback mechanism" is a function that provides users with appropriate prompts or alerts based on the results of sentiment analysis.

[0710] An "information distribution means" is a mechanism for delivering information transmitted by a user to a server in real time.

[0711] A "data analysis tool" is an analytical mechanism that uses data accumulated over a long period of time to discover new insights.

[0712] This invention aims to construct a system for facilitating information sharing and emotion analysis in home care. The server includes a database for integrating and securely managing user input information. This database stores data such as patient symptoms and vital signs entered by professionals such as nurses via terminals.

[0713] The server also features a generative AI model that automatically generates visit records and treatment plans. This generative model analyzes the input data to calculate effective visit schedules and treatment plans. Furthermore, an emotion engine analyzes the user's emotional state based on received voice, facial expression, and text data. The analysis results are immediately shared with relevant parties to support appropriate decision-making.

[0714] The terminal has an interface that provides users with easy operation. This allows users to easily input information and send it to the server. The terminal also provides feedback to the user based on sentiment analysis. For example, if the user is feeling stressed, this will be displayed on the terminal, and appropriate alerts and prompts will be provided.

[0715] As a concrete example, a prompt message such as "Please enter the patient's condition" is used. Upon receiving this prompt, the user enters the information, and the server processes it, providing appropriate feedback and analysis results to relevant specialists. This enables rapid and emotionally conscious decision-making in home care settings.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The user uses a terminal to input patient information. Specifically, they enter the patient's symptoms and vital signs in a text field and click the submit button. The input data includes the patient's name, symptoms, measured heart rate, and body temperature. As output, the terminal sends this information to the server.

[0719] Step 2:

[0720] The server securely stores the received patient information. The input data is the patient information sent in step 1, which is written to the database. Specifically, the server stores the information through the database management system, applying redundancy and encryption. As output, the information is passed to the generating AI model.

[0721] Step 3:

[0722] The server automatically generates visit records and treatment plans using a generative AI model. The input data is the patient information saved in step 2. Based on this data, the model performs analysis and outputs an effective visit schedule and treatment plan. Specifically, the model performs inference based on the trained data.

[0723] Step 4:

[0724] The server uses an emotion engine to analyze the user's (expert's) emotional state. Input data consists of audio and facial expressions. The analysis engine analyzes this data and outputs the user's stress and fatigue levels. Specifically, the server uses voice analysis software and facial recognition technology.

[0725] Step 5:

[0726] The server notifies the terminal of the analysis results, facilitating information sharing among experts. The input data consists of the generated results and emotion analysis results obtained in steps 3 and 4. As output, the generated treatment plan and emotion information are distributed to other experts. Specifically, the server sends messages using a notification system.

[0727] Step 6:

[0728] The device provides the user with feedback based on emotion analysis. The input data is the emotion analysis results received from the server in step 5. The device displays prompts and alerts to the user to reduce stress. Specifically, a message encouraging relaxation is displayed on the screen.

[0729] (Application Example 2)

[0730] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0731] Providing efficient care without considering emotional factors is a challenging task in information sharing and decision-making support within the home care setting. In particular, real-time emotional recognition and information sharing are essential to balance reducing the burden on care staff with providing emotional care to patients. Means to achieve this are needed.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0733] In this invention, the server includes data processing means for integrating and managing patient information, information generation means for automatically generating visit records and treatment plans using a generative model, and emotion analysis means for analyzing emotional states using emotion recognition technology and providing feedback to the user. This makes it possible to provide care that takes emotional factors into consideration in real time in nursing care settings.

[0734] "Data processing means" refers to a function that provides a method for integrating and securely managing patient information.

[0735] "Information generation means" refers to a function that provides a method for automatically generating visit records and treatment plans using a generation model.

[0736] A "means of dialogue" refers to a function that provides methods to facilitate communication among experts and promote information exchange.

[0737] "Emotional analysis methods" refer to technologies that analyze a user's facial expressions, voice, etc., to recognize their emotional state.

[0738] A "visual information presentation method" is a function that provides a way to display emotion-based suggestions to care staff in real time.

[0739] "Information management means" refers to a function that provides management methods to ensure the confidentiality and security of information.

[0740] The system for realizing this invention is constructed using various hardware and software. The server integrates and securely manages patient information using data processing means. By using a generative model, it automatically generates visit records and treatment plans, functioning as an information generation means. Furthermore, as an emotion analysis means, it analyzes the user's facial expressions and voice data to recognize their emotional state in real time. For this analysis, for example, general face recognition APIs and voice analysis APIs are used.

[0741] On the terminal side, emotionally-based suggestions are displayed to care staff through visual information presentation methods. This allows staff during visits to receive specific guidance to provide appropriate communication and care to patients. Information management systems ensure that this data is securely stored and facilitate information sharing among professionals.

[0742] As a concrete example, when a caregiver is visiting a patient with dementia, if the device recognizes the patient's face and detects that the patient is anxious, a suggestion such as "Please speak to the patient in a calm tone to reassure them" will be displayed on the device's screen. The AI ​​model will then receive the instruction "Patient XX is anxious. Please suggest ways to reassure them."

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The server receives patient facial expression data transmitted from the terminal. This data is in the form of images or videos captured by a camera. The input data is preprocessed using image processing algorithms and converted into a format suitable for the face recognition model. This process extracts facial feature points.

[0746] Step 2:

[0747] The server uses a face recognition API to analyze the emotional state based on the extracted facial feature points. This analysis process passes the input feature points through an emotion classification model, classifying them into emotional categories such as joy or anxiety. The output provides a specific emotional state and its probability.

[0748] Step 3:

[0749] The server generates and sends prompts to the generative AI model based on the results of the emotion analysis. An example prompt that can be generated is, "Patient XX is feeling anxious. Please suggest ways to reassure them." Based on the emotional state provided as input, the generative AI model generates appropriate care responses.

[0750] Step 4:

[0751] The server sends suggestions generated by the AI ​​model to the terminal. These suggestions are in text or audio format. The terminal displays the received suggestions on its screen, providing specific instructions to the care staff on what actions to take next. This output enables the staff to provide appropriate care to the patient.

[0752] Step 5:

[0753] The terminal sends staff responses and additional inputs to the server. This information is stored as system learning data and used to improve caregiving operations. In this step, the entered data is saved to a database and used for the next analysis.

[0754] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0755] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0756] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0757] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0758] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0759] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0760] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0761] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0762] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0763] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0764] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0765] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0766] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0767] 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.

[0768] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0769] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0770] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0771] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0772] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0773] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0774] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A data processing system for integrating and managing patient information,

[0778] Information generation means for automatically generating visit records and treatment plans using a generative model,

[0779] A dialogue tool that provides a chat function to facilitate communication among experts,

[0780] Information security management measures to ensure the safety of information,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, further comprising an information distribution means for distributing information, including visit records and plan summaries, to each specialist in real time.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising a data analysis means for accumulating data collected on a platform over the long term and using that data to discover new insights related to the field of elderly care.

[0786] "Example 1"

[0787] (Claim 1)

[0788] A data management system that aggregates data entered from terminals and verifies its consistency,

[0789] An information security management system that encrypts data using a configured protocol and sends and receives it securely,

[0790] Information generation means for automatically generating visit records and treatment plan summaries using a generative AI model,

[0791] An information distribution method that distributes generated summaries among experts in real time,

[0792] A dialogue support means that provides a dialogue function to facilitate communication among experts,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising a means for supporting the formulation of a visit plan that optimizes the visit plan based on the patient's condition.

[0796] (Claim 3)

[0797] The system according to claim 1, further comprising a data analysis means for long-term storage of collected data and for discovering new insights in the field of nursing care.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] A data processing means for integrating and managing patient-related information,

[0801] Information generation means for automatically generating visit records and treatment plans using an artificial intelligence model,

[0802] A dialogue tool that provides conversational functions to facilitate communication among experts,

[0803] Information security management measures to ensure the protection of information,

[0804] A transmission means that provides an interface for efficiently transmitting information entered by users,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, further comprising an information distribution means for immediately distributing visit records and plan summaries to each specialist.

[0808] (Claim 3)

[0809] The system according to claim 1, further comprising a data analysis means for accumulating data collected on a platform over a long period of time and using that data to discover new knowledge related to the field of nursing care.

[0810] "Example 2 of combining an emotion engine"

[0811] (Claim 1)

[0812] An input means for receiving user input information and sending it to the server,

[0813] A data analysis method that analyzes emotions from the user's facial expressions, voice, and text data,

[0814] An information sharing method that notifies other users of the analysis results generated using an emotion engine,

[0815] A feedback mechanism that provides prompts and alerts that take sentiment analysis into consideration,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising an information distribution means for distributing user information entered on a terminal to a server in real time.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising data analysis means for accumulating data over a long period and discovering new insights from that data.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A data processing system for integrating and managing patient information,

[0824] Information generation means for automatically generating visit records and treatment plans using a generative model,

[0825] Dialogue tools to facilitate communication among experts,

[0826] An emotion analysis means that analyzes emotional states using emotion recognition technology and provides feedback to the user,

[0827] A visual information presentation method that displays emotion-based suggestions in real time,

[0828] Information management measures to ensure the security of information,

[0829] ...

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising an information distribution means for delivering visit records and plan summaries to each specialist in real time, and further sharing sentiment analysis results with other specialists.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising data analysis means for accumulating data collected on the platform over the long term and using that data to discover new insights in the field of nursing care, and for optimizing care based on emotion analysis. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing means for integrating and managing patient-related information, Information generation means for automatically generating visit records and treatment plans using an artificial intelligence model, A dialogue tool that provides conversational functions to facilitate communication among experts, Information security management measures to ensure the protection of information, A transmission means that provides an interface for efficiently transmitting information entered by users, A system that includes this.

2. The system according to claim 1, further comprising an information distribution means for immediately distributing visit records and plan summaries to each specialist.

3. The system according to claim 1, further comprising a data analysis means for accumulating data collected on a platform over a long period of time and using that data to discover new knowledge related to the field of nursing care.

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