system

Abstract

We provide the system. [Solution] In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field, The aforementioned knowledge processing device has means for accumulating the results of individual analyses, A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and make a final decision, A means of collecting user input data from a terminal and sending it to a database in an organized format, A means of displaying the received analysis results on the user's information terminal, 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 persona chatbot control method 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] In the examination for care certification, it is a time constraint for experts such as doctors and nurses to gather, and there is a problem that the quality of the examination varies due to differences in the abilities and experiences of the experts. As a result, it is difficult to conduct a rapid and fair care certification.

Means for Solving the Problems

[0005] Using an information processing device, multiple knowledge processing devices specializing in different fields are activated, and the results of their individual analyses are accumulated. Based on these analysis results, the knowledge processing devices consult with each other to make a final decision. Furthermore, feedback is received during these consultations, and the database is updated to ensure continuous improvement. Based on the final decision, a report is generated, and the results are notified to an external device, enabling rapid and highly accurate care needs assessment.

[0006] An "information processing device" is a device that collects, organizes, and analyzes data and uses it for a specific purpose.

[0007] A "knowledge processing device" is a device that can analyze information related to a specific field of expertise and make judgments specific to that field.

[0008] "Analysis results" refers to insights and conclusions obtained after analyzing data using a knowledge processing device.

[0009] "Storage" refers to the process of recording the analysis results generated by a knowledge processing device in a database or similar system, making them accessible for later reference.

[0010] "Consultation" refers to the exchange of opinions among multiple knowledge processing devices, based on their respective analysis results, in order to arrive at the optimal conclusion.

[0011] "Feedback" refers to opinions and data provided based on information and conclusions obtained by a knowledge processing device, with the aim of improving or adjusting the entire system.

[0012] A "database" is a structured collection of information used for efficient storage, management, and retrieval.

[0013] A "report" is a document created to summarize final judgments and conclusions and to present the results to external parties.

[0014] "External devices" refer to external electronic devices used to receive information in conjunction with information processing devices.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing apparatus and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a multi-agent system aimed at improving the efficiency and accuracy of long-term care certification assessments. This system consists of an information processing device and multiple knowledge processing devices, and complements assessments by experts in each field.

[0037] First, the user enters detailed information about the person receiving care through the terminal. This includes health status, medical history, and living situation. The terminal organizes the entered data and converts it into the appropriate format for transmission to the server.

[0038] The server stores the received data in a database and activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. It then sorts the relevant data so that the knowledge processing units can proceed with their individual analyses. During this process, the knowledge processing units analyze the data based on their specialized knowledge and construct hypotheses.

[0039] Each knowledge processing device sends its analysis results to a server, which collects them and facilitates discussions among the devices. During these discussions, each knowledge processing device announces its conclusions based on its analysis, and through exchanges of opinions with other devices, the optimal care certification level is determined.

[0040] For example, a medical knowledge processing system might point out a risk of heart disease, while a nutrition knowledge processing system might suggest the need for a low-salt diet. A health knowledge processing system would then offer suggestions to compensate for limitations in daily life, thereby unifying knowledge from each specialized field to make a final decision.

[0041] Ultimately, the server generates a report based on the results of the discussion and provides the results to the user via the terminal. Furthermore, by continuously updating the database based on feedback, the system automatically optimizes itself, improving the accuracy of future reviews.

[0042] Therefore, this system not only reduces the time burden on professionals but also improves the validity and fairness of care needs assessment.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters detailed information about the care recipient, such as their health status, medical history, and living situation, into the terminal. This information is neatly organized through an input form.

[0046] Step 2:

[0047] The terminal receives the input information and converts it into the appropriate data format. This prepares the information in a way that makes it easy for the server to process.

[0048] Step 3:

[0049] The terminal sends formatted data to the server. This data is treated as basic information necessary for assessing the level of care needs assessment.

[0050] Step 4:

[0051] The server stores the received data in a database and performs initial data processing. This makes the data easily accessible in subsequent processing steps.

[0052] Step 5:

[0053] The server activates multiple knowledge processing units, each specialized for a particular field. Knowledge processing units for the necessary specialized fields, such as medicine, health, and nutrition, are called up sequentially.

[0054] Step 6:

[0055] The server distributes the accumulated data to the relevant knowledge processing devices. This allows each device to begin data analysis based on its own area of ​​expertise.

[0056] Step 7:

[0057] The knowledge processing unit performs individual analyses based on the sorted data. The medical agent assesses the risk of disease, and the nutrition agent considers a dietary plan.

[0058] Step 8:

[0059] The server aggregates the analysis results obtained from each knowledge processing device and uses them to facilitate discussions among the knowledge processing devices.

[0060] Step 9:

[0061] The knowledge processing system conducts discussions and exchanges analysis results to derive the optimal care certification level. Through this process, specialized knowledge is integrated, and the final judgment is formed.

[0062] Step 10:

[0063] The server generates a report based on the optimal care needs assessment level determined as a result of the consultation.

[0064] Step 11:

[0065] The server provides the generated report to the user via the terminal. It also accepts user feedback through a feedback function.

[0066] Step 12:

[0067] The server records user feedback in a database and uses it as system learning data. This automatically optimizes the next review process.

[0068] (Example 1)

[0069] 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."

[0070] The current care needs assessment process is inefficient because it requires the involvement of many specialists, is time-consuming, and laborious. Furthermore, the accuracy and fairness of the assessment results are easily influenced by the subjective opinions of the specialists. There is a need to resolve these issues and implement a faster and more accurate assessment process.

[0071] 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.

[0072] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields, means for accumulating the results of analyses performed individually by the knowledge processing devices, and means for the knowledge processing devices to consult with each other based on the analysis results and make an optimal decision. This enables a rapid, fair, and highly accurate care certification process.

[0073] An "input device" is a device used by users to input information, and its role is to convert the input information into a predetermined format and transmit it to an information processing device.

[0074] An "information processing device" is a device that receives information transmitted from an input device and performs the various functions necessary for analysis and decision-making.

[0075] A "knowledge processing device" is a device that uses specialized knowledge to analyze data and construct hypotheses.

[0076] "Analysis results" refer to the data and conclusions obtained after a knowledge processing device analyzes the input data.

[0077] "Consultation" refers to the exchange of information and discussion between knowledge processing devices based on analysis results, with the aim of deriving the optimal decision.

[0078] A "report" is a document created based on the results of discussions and includes the final judgment and proposals.

[0079] An "external device" is a device that receives output from an information processing device and notifies the user of the results.

[0080] "Feedback" refers to the reactions and information received by a knowledge processing device, which is used to update the database.

[0081] A description of embodiments for carrying out the present invention will be provided.

[0082] The user enters detailed information about the person receiving care using a terminal. This input includes a wide range of information, such as health status, medical history, and living situation. The terminal converts this entered information into a predetermined format and prepares the data for transmission to the server. At this time, a generation AI model is used to display a prompt message, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life," to ensure the user enters the information accurately.

[0083] The server receives data transmitted from terminals and stores it in a central data storage device. The server then activates knowledge processing devices specialized for each area of ​​expertise and distributes the data in a format suitable for each device. The knowledge processing devices analyze the data based on their respective areas of expertise and construct hypotheses. This process involves specialized analysis in the fields of medicine, nutrition, and health.

[0084] The server integrates the analysis results returned from the knowledge processing devices and manages inter-device consultations. This consultation is crucial for deriving final decisions based on the analysis results and serves as a centralized platform for the specialized knowledge of each knowledge processing device.

[0085] Based on the final consultation results, the server generates a report and sends it to the terminal. This allows the user to receive the results of their care needs assessment and a specific care plan.

[0086] Through the process described above, the present invention aims to improve the efficiency and accuracy of care needs assessment and provide users with useful information.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user enters detailed information about the person receiving care. During this process, a guided prompt using a generated AI model appears on the terminal, prompting the user to enter information such as, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life." The input includes health status, medical history, and living situation. This input data is validated on the terminal before being converted into data ready for transmission.

[0090] Step 2:

[0091] The terminal converts the information entered by the user into an appropriate data format. Specifically, it uses a format conversion function to format the input text into CSV or JSON format. After data conversion, the terminal sends it to the server. The output of this series of processes is detailed information about the caregiver, converted into a format that can be processed by the server.

[0092] Step 3:

[0093] The server stores data received from terminals in a central database. This storage process involves data integrity and duplicate checks via a database management system. The input is formatted caregiver information, and the output is reliable information stored in the database.

[0094] Step 4:

[0095] The server activates knowledge processing units specialized for each area of ​​expertise. In this process, data is distributed to analysis units corresponding to the medical, health, and nutrition fields. The input consists of information from a database specific to each field, which is then supplied to the knowledge processing units. The output of the knowledge processing units consists of hypotheses and risk assessments based on initial analysis.

[0096] Step 5:

[0097] Knowledge processing devices analyze the assigned data and perform calculations to construct hypotheses. For example, they utilize statistical models and machine learning algorithms to assess health risks and nutritional balance. The input is data related to a specific field, and the output is specific analysis results based on that data.

[0098] Step 6:

[0099] The server receives the analysis results and initiates a consultation among the knowledge processing units. During the consultation, each knowledge processing unit exchanges opinions and determines the optimal care assessment level and proposal. The input is the analysis results from each individual knowledge processing unit, and the output is the final care plan integrated after the consultation.

[0100] Step 7:

[0101] The server generates a report based on the final outcome of the consultation. The report generation function is used to document and format the certification results. The output report is a detailed document including the care certification level and recommended care plan.

[0102] Step 8:

[0103] The user receives reports from the server via their terminal. If the user provides feedback, the server receives that feedback and updates the database to use it for future analyses. The input is the feedback information, and the output is the updated database.

[0104] (Application Example 1)

[0105] 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."

[0106] Conventional care needs assessment systems require a tremendous amount of time and effort from experts, and there is a need for increased efficiency and accuracy in the assessment process. Furthermore, there is a lack of a system that allows users to easily input data from their own devices and receive analysis results in real time. As a result, there is a problem in that appropriate support for those receiving care is delayed, and fair and timely care needs assessment is difficult.

[0107] 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.

[0108] In this invention, the server includes means for collecting user input data from a terminal in an information processing device and transmitting it to a database in an organized format, means for activating multiple knowledge processing devices specialized for each specialized field, and means for displaying the received analysis results on the user's information terminal. This significantly improves the efficiency of the examination process and makes it possible to receive analysis results in real time based on the information entered by the user.

[0109] An "information processing device" is a device that efficiently receives, analyzes, and displays data results to the user.

[0110] A "knowledge processing device" is a device that analyzes data in a specific specialized field and has the function of sharing and discussing the results with other devices.

[0111] "User input data" refers to information provided by the user via their device, such as the health status and living conditions of the person receiving care.

[0112] A "database" is a storage area that organizes and stores collected data and provides it to knowledge processing devices and information processing devices as needed.

[0113] "Analysis results" refer to information that shows conclusions or views obtained after a knowledge processing device has processed input data.

[0114] An "information terminal" is a device used by users to input data or check results.

[0115] This invention comprises a system aimed at improving the efficiency and accuracy of care needs assessment. The system is based on an information processing device and includes multiple knowledge processing devices specialized for each specialized field. The entire system operates using user terminals, a central server, and knowledge processing devices for each specialized field.

[0116] Users input data about the person receiving care using devices such as smartphones or smart glasses. The entered information is sent to the server in an organized format in a database. The server then activates various knowledge processing units and distributes the input data to the appropriate specialized domain.

[0117] The knowledge processing device performs expert analysis based on input data and sends the resulting insights to the server. The server aggregates these analysis results and supports discussions to integrate the overall analysis. The server makes the final judgment, organizes the results, and sends and displays them on the user's information terminal.

[0118] As a concrete example, when supporting an elderly person living alone in Tokyo, the user inputs details about the elderly person's health condition and lifestyle through a terminal. Based on the analysis results from this information, the system facilitates discussions between knowledge processing devices and proposes the optimal care level.

[0119] Example of a prompt:

[0120] Please enter the health status and medical history of the person receiving care.

[0121] To check your care needs assessment level, please click the button below.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] Users input information about the care recipient's health and living situation using a smartphone or smart glasses. This input data includes health status, medical history, and living environment. This information is formatted appropriately within the device and prepared for transmission to the database.

[0125] Step 2:

[0126] The data formatted by the terminal is sent to the server via the internet. The server verifies the received data and stores it in a database. The data obtained as input is organized within the database for subsequent analysis.

[0127] Step 3:

[0128] The server individually starts up the knowledge processing units and distributes the data retrieved from the database to each unit. In this process, data is sent to the unit in the relevant specialized field depending on the type of data. Each knowledge processing unit then begins data analysis based on its specialized knowledge.

[0129] Step 4:

[0130] In the knowledge processing unit, input data is analyzed. For example, a knowledge processing unit in the medical field assesses the risk of heart disease, while a knowledge processing unit in the nutrition field determines the necessity of a low-salt diet. Based on the analysis, each knowledge processing unit outputs its own conclusions and sends them back to the server.

[0131] Step 5:

[0132] The server aggregates the analysis results from each knowledge processing device and uses this data to support discussions. During the discussions, the knowledge processing devices share their conclusions and exchange opinions to determine the optimal care certification level. The server manages this process and makes the final decision.

[0133] Step 6:

[0134] The final assessment result is sent from the server to the user's terminal. The user can check the analysis results on their terminal and evaluate the proposed care needs assessment level. By displaying the results as instructed by the prompt, the user can quickly obtain the information necessary to support the person receiving care.

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

[0136] This invention relates to a system that provides more humane and insightful certification results by combining an emotion engine that recognizes the user's emotions with the care certification assessment process. This system consists of an information processing device, multiple knowledge processing devices, and an emotion engine.

[0137] First, the user enters detailed information about the person being cared for through the terminal. During this process, the emotion engine analyzes the user's input patterns and interactions in real time to evaluate the user's emotional state.

[0138] When the device sends acquired information to the server, it also includes emotional data recognized by the emotion engine. The server stores and organizes this data in a database.

[0139] The server activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. These units perform individual analyses based on relevant information, including emotional data. By considering the user's emotional state, as indicated by the emotion engine, during the analysis, more flexible decision-making becomes possible.

[0140] The knowledge processing device sends the analysis results to the server, which aggregates them and facilitates discussions among the devices. During these discussions, the user's emotional data obtained from the emotion engine is reflected and treated as an important factor in decision-making. For example, if a user expresses emotions such as "worry" or "anxiety" during input, these emotions are considered as indicators of the need for enhanced support from the medical side.

[0141] Finally, the server generates a report based on the certification level obtained as a result of the consultation and provides the results to the user via the terminal. Furthermore, the database is updated based on feedback, including sentiment data from the sentiment engine, to continuously improve the system.

[0142] As described above, by combining it with an emotional engine, it becomes possible to provide comprehensive care assessments that go beyond conventional mechanical processes and capture the emotional needs of users.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user inputs detailed information about the person being cared for, such as their health status and medical history, into the terminal. The terminal uses an emotion engine to analyze the user's typing speed and input content during the input process and evaluates their emotional state in real time.

[0146] Step 2:

[0147] The device prepares to format the evaluated sentiment data and entered information appropriately and send it to the server. This format includes metadata about the user's emotional state.

[0148] Step 3:

[0149] The server stores the received data and sentiment metadata in a database and activates knowledge processing units specialized for each area of ​​expertise. The database is organized to facilitate reference in subsequent analysis processes.

[0150] Step 4:

[0151] The server distributes the corresponding information, including emotional data, to each knowledge processing unit. This allows each unit to begin data analysis based on its own specialized knowledge.

[0152] Step 5:

[0153] The knowledge processing device performs individual analysis based on the sorted data, while also incorporating the user's emotional state, as indicated by the emotion engine, into the analysis process. This enables flexible recognition decisions that are tailored to the user's emotions.

[0154] Step 6:

[0155] The server integrates the analysis results collected from the knowledge processing units and facilitates discussions between them. These discussions include analysis results incorporating emotional data.

[0156] Step 7:

[0157] The knowledge processing system considers emotional data during discussions and mutually proposes the most suitable care needs assessment level. Emotional data is a particularly influential factor in determining the focus of support and the response strategy.

[0158] Step 8:

[0159] The server generates a report based on the final decision obtained through deliberation. This report also includes special considerations based on the user's feelings.

[0160] Step 9:

[0161] The server notifies the user of the generated report via the terminal and accepts feedback. This feedback is reflected in the database to help improve the sentiment data.

[0162] Step 10:

[0163] The server utilizes feedback and emotional data from the emotion engine to continuously optimize the system. This will enable more accurate care needs assessments in the next review process.

[0164] (Example 2)

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

[0166] Traditional care needs assessment processes prioritize mechanical judgments based on detailed information and medical data of the care recipient, often failing to adequately consider the user's emotions and emotional needs. As a result, assessment results may not reflect the user's true needs, potentially leading to inadequate support.

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

[0168] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields within the information processing device, means for accumulating the results of analyses performed individually by the knowledge processing devices, means for incorporating an emotion engine that analyzes the user's emotional state in real time, and means for the knowledge processing devices to consult with each other based on the analysis results and emotion data to make a final decision. This makes it possible to capture the emotional needs of the user and provide care assessment results that are more humane and insightful.

[0169] An "information processing device" is a general term for hardware and software used for data collection, analysis, and management.

[0170] A "knowledge processing device" is a device that analyzes data and makes decisions based on specific expertise.

[0171] An "emotion engine" is a system that analyzes a user's emotional state in real time based on their input patterns and interactions, and extracts the data from that analysis.

[0172] "Emotional data" refers to information analyzed by an emotion engine that indicates the user's emotional state.

[0173] "Consultation" is the process by which multiple knowledge processing devices bring together their analysis results to make a final decision.

[0174] "Judgment" refers to an evaluation or decision made based on various data, and is ultimately presented as the certification result for the user.

[0175] A description of embodiments for carrying out this invention will be given.

[0176] The user first enters detailed information about the person receiving care into the terminal. This includes the person's name, age, health status, and care needs. During this process, an emotion engine is built into the terminal, analyzing the user's input patterns and actions in real time to acquire emotional data. The emotion engine uses an algorithm that estimates the user's emotional state based on input speed and rhythm, as well as mouse movements.

[0177] The terminal transmits the acquired user details and emotional data to the server. The server, acting as an information processing device, activates multiple knowledge processing devices, each with its own area of ​​expertise. These knowledge processing devices specialize in various fields such as medicine, health, and nutrition, and perform detailed analysis in their respective fields based on the information stored in the database. The analysis takes into account the results of the emotional engine's analysis, allowing for flexibility in decision-making.

[0178] The server collects analysis results from each knowledge processing unit, discusses the data, and makes a final decision on the care needs assessment. Generative AI models are used in this process to improve the accuracy of the judgment by comparing it with past data. When a user expresses emotions such as "worry" or "anxiety," the system incorporates this emotional data and determines whether enhanced care support is necessary.

[0179] For example, if a user inputs information about the health status of a person receiving care and indicates "anxiety," the knowledge processing unit receives this emotional data and analyzes it to increase the need for medical support. Based on the final assessment results, a report is generated and provided to the user via the terminal. The emotional data received as feedback is also reflected in the database, leading to further improvements to the system.

[0180] A specific example of a prompt would be, "If the user expresses feelings of 'anxiety' while entering care records, how should the care plan be adjusted?" This allows the system to suggest specific support strategies.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The user uses a terminal to input detailed information about the person being cared for. This information includes the person's name, age, health status, and care needs. As the user inputs information, the emotion engine analyzes the interaction and estimates the emotional state from patterns such as input speed and mouse movements. At this stage, two types of data are generated: input data and emotion data.

[0184] Step 2:

[0185] The device sends detailed information obtained from the user and sentiment data generated by the sentiment engine to the server. This transmission takes place in a secure environment, and the information is encrypted. The server receives this data, stores it in a database, and categorizes it. Specifically, a unique identifier is assigned to each input data to prepare for future analysis.

[0186] Step 3:

[0187] The server activates multiple knowledge processing units, each specialized in a particular field, based on the information stored in the database. These include categories such as medicine, health, and nutrition. The knowledge processing units retrieve necessary information from the database and perform expert analysis. They use emotional data, among other factors, to determine the appropriate care support. Each processing unit uses its own algorithm to gain insights and generate analysis results.

[0188] Step 4:

[0189] The server aggregates the analysis results from each knowledge processing unit and conducts discussions based on them. The knowledge processing units collaborate with each other, using generative AI models to compare and examine past data with current analysis results. In this process, the user's emotional state is considered as an important factor in decision-making. For example, if a user expresses "anxiety," that emotion may be suggested as a reason for needing enhanced medical support.

[0190] Step 5:

[0191] The server generates a report based on the final decision made after deliberation. This report includes detailed analysis results and recommended support, providing useful information for the user. The generated report is delivered to the user via their terminal. The output here includes the care assessment result and a specific support plan.

[0192] Step 6:

[0193] Users receive reports and provide feedback on their contents. This feedback, along with sentiment data, is returned to the server and becomes an important factor in updating the database. The server uses this data to improve the system and as training data to improve the accuracy of future prompt generation and sentiment analysis.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] A challenge with the conventional care needs assessment process is that it fails to consider the user's emotional state, resulting in mechanical and inflexible outcomes. Furthermore, it lacks consideration for the psychological burden on caregivers, making it difficult to provide support that meets individual needs.

[0197] 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.

[0198] In this invention, the server includes means for combining an emotion recognition engine that analyzes the user's emotional state in real time, means for activating multiple knowledge processing devices specialized in various fields, and means for proposing additional support to the user based on the emotional data. This enables flexible and humane care assessment while taking the user's emotions into consideration.

[0199] An "information processing device" is a device or system that receives information input from a user and performs analysis and data storage.

[0200] An "emotion recognition engine" is a technology that analyzes user input and actions and evaluates the underlying emotional state in real time.

[0201] A "knowledge processing device" is a device or program that specializes in a particular field of expertise and is used to analyze related data.

[0202] "Analysis results" refer to the final output of the analysis performed by the knowledge processing device based on the input data.

[0203] "Consultation" is a process in which multiple knowledge processing devices discuss and make integrated decisions based on their analysis results.

[0204] "Emotional data" refers to information about a user's emotional state, analyzed by an emotion recognition engine.

[0205] "Additional support" refers to supplementary care-related services and support suggested based on the user's emotional data and circumstances.

[0206] A "database" is a system that stores analysis results and emotional data so that they can be used later.

[0207] A "report" is a document containing the final judgment results and is information submitted to an external device.

[0208] "External devices" refer to devices or systems used to receive the results of a report.

[0209] The system implementing this invention is configured to improve the process of assessing long-term care needs by linking an information processing device, multiple knowledge processing devices, and an emotion recognition engine. The following hardware and software are used for implementation.

[0210] The information processing device functions as a terminal for users to input detailed information for care needs assessment. This terminal is equipped with emotion recognition software and uses an emotion engine to acquire user emotion data in real time from input and operations. Specifically, it utilizes emotion recognition APIs (e.g., Amazon Rekognition or Microsoft® Azure® Face API).

[0211] The device sends acquired information and sentiment data to the server. The server stores this data in a database (e.g., MySQL®, Firebase) and activates knowledge processing units specialized for each area of ​​expertise. The knowledge processing units perform analysis that also takes sentiment data into consideration during the analysis process.

[0212] For example, if the emotion engine indicates that a user is experiencing anxiety, the system, through its knowledge processing unit, determines that additional care support is needed based on that anxiety. Based on this determination, it becomes possible to propose additional support to the user.

[0213] The final assessment results are compiled by the server and generated as a report. This report is then notified to relevant external organizations. Throughout the evaluation process, the server continuously updates its database and works to improve the system.

[0214] As a specific example, when conducting an online care needs assessment, if the emotion recognition engine detects that a user has expressed "worry" or "anxiety," the system will immediately respond by determining whether mental health care is needed and presenting options for consulting with a specialist.

[0215] An example of a prompt using a generative AI model is: "If the user shows anxiety while inputting emotional data through the app, what kind of care plan should you suggest? Based on the emotional data provided by the emotion engine, please provide the suggested care plan in the following format."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The terminal receives input from the user of information necessary for care needs assessment. The data entered by the user includes basic personal information and details regarding the need for care. At this time, the emotion recognition engine installed in the terminal analyzes the user's facial expressions and input patterns in real time and outputs their emotional state as numerical data. The input to the emotion recognition engine is the user's facial expression data, and the output is a report of their emotional state.

[0219] Step 2:

[0220] The terminal sends the acquired user data and emotional state report to the server. The server stores the received data in a database and organizes it. The input data is user information and emotional state, and the output is an update to the database in an organized format.

[0221] Step 3:

[0222] The server activates knowledge processing units specialized for each area of ​​expertise. These units retrieve necessary information from the database and begin analysis that takes emotional data into account. An analysis algorithm is then applied based on the emotional data. The input consists of user information and emotional states from the database, and the output is an individually analyzed expert evaluation.

[0223] Step 4:

[0224] The server aggregates the analysis results obtained from the knowledge processing devices and uses this information to conduct discussions among the devices. In these discussions, each analysis result and emotional data are considered as important factors, and the final care needs assessment result is derived. The input consists of multiple analysis results and emotional data, and the output is a provisional care needs assessment decision after the discussions.

[0225] Step 5:

[0226] The server generates a report based on the final assessment results and sentiment data, and notifies an external device of the results. This report is formatted into a specific format and communicated to care service providers and other relevant organizations. Its input is the final assessment results and sentiment data, and its output is document data in the form of a report.

[0227] Step 6:

[0228] The server incorporates data acquired through each process and user feedback into a database, using it to improve the system. In particular, it contributes to improving the accuracy of the emotion recognition engine and optimizing the algorithms of the knowledge processing unit. The input is feedback data, and the output is improved system performance.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention relates to a multi-agent system aimed at improving the efficiency and accuracy of long-term care certification assessments. This system consists of an information processing device and multiple knowledge processing devices, and complements assessments by experts in each field.

[0246] First, the user enters detailed information about the person receiving care through the terminal. This includes health status, medical history, and living situation. The terminal organizes the entered data and converts it into the appropriate format for transmission to the server.

[0247] The server stores the received data in a database and activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. It then sorts the relevant data so that the knowledge processing units can proceed with their individual analyses. During this process, the knowledge processing units analyze the data based on their specialized knowledge and construct hypotheses.

[0248] Each knowledge processing device sends its analysis results to a server, which collects them and facilitates discussions among the devices. During these discussions, each knowledge processing device announces its conclusions based on its analysis, and through exchanges of opinions with other devices, the optimal care certification level is determined.

[0249] For example, a medical knowledge processing system might point out a risk of heart disease, while a nutrition knowledge processing system might suggest the need for a low-salt diet. A health knowledge processing system would then offer suggestions to compensate for limitations in daily life, thereby unifying knowledge from each specialized field to make a final decision.

[0250] Ultimately, the server generates a report based on the results of the discussion and provides the results to the user via the terminal. Furthermore, by continuously updating the database based on feedback, the system automatically optimizes itself, improving the accuracy of future reviews.

[0251] Therefore, this system not only reduces the time burden on professionals but also improves the validity and fairness of care needs assessment.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user enters detailed information about the care recipient, such as their health status, medical history, and living situation, into the terminal. This information is neatly organized through an input form.

[0255] Step 2:

[0256] The terminal receives the input information and converts it into the appropriate data format. This prepares the information in a way that makes it easy for the server to process.

[0257] Step 3:

[0258] The terminal sends formatted data to the server. This data is treated as basic information necessary for assessing the level of care needs assessment.

[0259] Step 4:

[0260] The server stores the received data in a database and performs initial data processing. This makes the data easily accessible in subsequent processing steps.

[0261] Step 5:

[0262] The server activates multiple knowledge processing units, each specialized for a particular field. Knowledge processing units for the necessary specialized fields, such as medicine, health, and nutrition, are called up sequentially.

[0263] Step 6:

[0264] The server distributes the accumulated data to the relevant knowledge processing devices. This allows each device to begin data analysis based on its own area of ​​expertise.

[0265] Step 7:

[0266] The knowledge processing unit performs individual analyses based on the sorted data. The medical agent assesses the risk of disease, and the nutrition agent considers a dietary plan.

[0267] Step 8:

[0268] The server aggregates the analysis results obtained from each knowledge processing device and uses them to facilitate discussions among the knowledge processing devices.

[0269] Step 9:

[0270] The knowledge processing system conducts discussions and exchanges analysis results to derive the optimal care certification level. Through this process, specialized knowledge is integrated, and the final judgment is formed.

[0271] Step 10:

[0272] The server generates a report based on the optimal care needs assessment level determined as a result of the consultation.

[0273] Step 11:

[0274] The server provides the generated report to the user via the terminal. It also accepts user feedback through a feedback function.

[0275] Step 12:

[0276] The server records user feedback in a database and uses it as system learning data. This automatically optimizes the next review process.

[0277] (Example 1)

[0278] 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."

[0279] The current care needs assessment process is inefficient because it requires the involvement of many specialists, is time-consuming, and laborious. Furthermore, the accuracy and fairness of the assessment results are easily influenced by the subjective opinions of the specialists. There is a need to resolve these issues and implement a faster and more accurate assessment process.

[0280] 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.

[0281] In this invention, in an information processing apparatus, the server includes means for starting a plurality of knowledge processing apparatuses specialized in each specialized field, means for accumulating the results individually analyzed by the knowledge processing apparatuses, and means for the knowledge processing apparatuses to conduct consultations with each other based on the analysis results and make an optimal determination. As a result, a quick, fair, and highly accurate care certification process becomes possible.

[0282] The "input device" is a device for a user to input information, which is responsible for converting the input information into a predetermined format and transmitting it to the information processing apparatus.

[0283] The "information processing apparatus" is a device that receives information transmitted from the input device and executes each function necessary for analysis and determination.

[0284] The "knowledge processing apparatus" is a device that analyzes data using knowledge specialized in a specific specialized field and constructs hypotheses.

[0285] The "analysis result" refers to data and conclusions obtained after the knowledge processing apparatus analyzes the input data.

[0286] "Consultation" means information exchange and discussion conducted between knowledge processing apparatuses based on analysis results, aiming to derive an optimal judgment.

[0287] The "report" is a document created based on the results of the consultation, including the final determination and proposal.

[0288] The "external device" is a device that receives the output from the information processing apparatus and notifies the user of the result.

[0289] "Feedback" is the reaction and information received by the knowledge processing apparatus, which is used for updating the database.

[0290] The embodiments for implementing the present invention will be described.

[0291] The user enters detailed information about the person receiving care using a terminal. This input includes a wide range of information, such as health status, medical history, and living situation. The terminal converts this entered information into a predetermined format and prepares the data for transmission to the server. At this time, a generation AI model is used to display a prompt message, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life," to ensure the user enters the information accurately.

[0292] The server receives data transmitted from terminals and stores it in a central data storage device. The server then activates knowledge processing devices specialized for each area of ​​expertise and distributes the data in a format suitable for each device. The knowledge processing devices analyze the data based on their respective areas of expertise and construct hypotheses. This process involves specialized analysis in the fields of medicine, nutrition, and health.

[0293] The server integrates the analysis results returned from the knowledge processing devices and manages inter-device consultations. This consultation is crucial for deriving final decisions based on the analysis results and serves as a centralized platform for the specialized knowledge of each knowledge processing device.

[0294] Based on the final consultation results, the server generates a report and sends it to the terminal. This allows the user to receive the results of their care needs assessment and a specific care plan.

[0295] Through the process described above, the present invention aims to improve the efficiency and accuracy of care needs assessment and provide users with useful information.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user enters detailed information about the person receiving care. During this process, a guided prompt using a generated AI model appears on the terminal, prompting the user to enter information such as, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life." The input includes health status, medical history, and living situation. This input data is validated on the terminal before being converted into data ready for transmission.

[0299] Step 2:

[0300] The terminal converts the information entered by the user into an appropriate data format. Specifically, it uses a format conversion function to format the input text into CSV or JSON format. After data conversion, the terminal sends it to the server. The output of this series of processes is detailed information about the caregiver, converted into a format that can be processed by the server.

[0301] Step 3:

[0302] The server stores data received from terminals in a central database. This storage process involves data integrity and duplicate checks via a database management system. The input is formatted caregiver information, and the output is reliable information stored in the database.

[0303] Step 4:

[0304] The server activates knowledge processing units specialized for each area of ​​expertise. In this process, data is distributed to analysis units corresponding to the medical, health, and nutrition fields. The input consists of information from a database specific to each field, which is then supplied to the knowledge processing units. The output of the knowledge processing units consists of hypotheses and risk assessments based on initial analysis.

[0305] Step 5:

[0306] The knowledge processing device analyzes the allocated data and performs calculations for constructing hypotheses. For example, it utilizes statistical models and machine learning algorithms to evaluate health risks and nutritional balance. There is data related to a specialized field as input, and the output is specific analysis results based on that data.

[0307] Step 6:

[0308] The server receives the analysis results and initiates a consultation among the knowledge processing devices. In the consultation, each knowledge processing device exchanges opinions to determine the optimal care certification level and proposals. The input is the analysis results from individual knowledge processing devices, and the output is the final integrated care plan after the consultation.

[0309] Step 7:

[0310] The server generates a report based on the final result of the consultation. Using the report generation function, it documents and formats the certification results. The output report is a detailed document including the care certification level and recommended care plan.

[0311] Step 8:

[0312] The user receives the report provided by the server via the terminal. If there is user feedback, the server receives the feedback and updates the database for use in subsequent analyses. The input is the feedback information, and the output is the updated database.

[0313] (Application Example 1)

[0314] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] Conventional care needs assessment systems require a tremendous amount of time and effort from experts, and there is a need for increased efficiency and accuracy in the assessment process. Furthermore, there is a lack of a system that allows users to easily input data from their own devices and receive analysis results in real time. As a result, there is a problem in that appropriate support for those receiving care is delayed, and fair and timely care needs assessment is difficult.

[0316] 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.

[0317] In this invention, the server includes means for collecting user input data from a terminal in an information processing device and transmitting it to a database in an organized format, means for activating multiple knowledge processing devices specialized for each specialized field, and means for displaying the received analysis results on the user's information terminal. This significantly improves the efficiency of the examination process and makes it possible to receive analysis results in real time based on the information entered by the user.

[0318] An "information processing device" is a device that efficiently receives, analyzes, and displays data results to the user.

[0319] A "knowledge processing device" is a device that analyzes data in a specific specialized field and has the function of sharing and discussing the results with other devices.

[0320] "User input data" refers to information provided by the user via their device, such as the health status and living conditions of the person receiving care.

[0321] A "database" is a storage area that organizes and stores collected data and provides it to knowledge processing devices and information processing devices as needed.

[0322] "Analysis results" refer to information that shows conclusions or views obtained after a knowledge processing device has processed input data.

[0323] An "information terminal" is a device used by users to input data or check results.

[0324] This invention comprises a system aimed at improving the efficiency and accuracy of care needs assessment. The system is based on an information processing device and includes multiple knowledge processing devices specialized for each specialized field. The entire system operates using user terminals, a central server, and knowledge processing devices for each specialized field.

[0325] Users input data about the person receiving care using devices such as smartphones or smart glasses. The entered information is sent to the server in an organized format in a database. The server then activates various knowledge processing units and distributes the input data to the appropriate specialized domain.

[0326] The knowledge processing device performs expert analysis based on input data and sends the resulting insights to the server. The server aggregates these analysis results and supports discussions to integrate the overall analysis. The server makes the final judgment, organizes the results, and sends and displays them on the user's information terminal.

[0327] As a concrete example, when supporting an elderly person living alone in Tokyo, the user inputs details about the elderly person's health condition and lifestyle through a terminal. Based on the analysis results from this information, the system facilitates discussions between knowledge processing devices and proposes the optimal care level.

[0328] Example of a prompt:

[0329] Please enter the health status and medical history of the person receiving care.

[0330] To check your care needs assessment level, please click the button below.

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] Users input information about the care recipient's health and living situation using a smartphone or smart glasses. This input data includes health status, medical history, and living environment. This information is formatted appropriately within the device and prepared for transmission to the database.

[0334] Step 2:

[0335] The data formatted by the terminal is sent to the server via the internet. The server verifies the received data and stores it in a database. The data obtained as input is organized within the database for subsequent analysis.

[0336] Step 3:

[0337] The server individually starts up the knowledge processing units and distributes the data retrieved from the database to each unit. In this process, data is sent to the unit in the relevant specialized field depending on the type of data. Each knowledge processing unit then begins data analysis based on its specialized knowledge.

[0338] Step 4:

[0339] In the knowledge processing unit, input data is analyzed. For example, a knowledge processing unit in the medical field assesses the risk of heart disease, while a knowledge processing unit in the nutrition field determines the necessity of a low-salt diet. Based on the analysis, each knowledge processing unit outputs its own conclusions and sends them back to the server.

[0340] Step 5:

[0341] The server aggregates the analysis results from each knowledge processing device and uses this data to support discussions. During the discussions, the knowledge processing devices share their conclusions and exchange opinions to determine the optimal care certification level. The server manages this process and makes the final decision.

[0342] Step 6:

[0343] The final assessment result is sent from the server to the user's terminal. The user can check the analysis results on their terminal and evaluate the proposed care needs assessment level. By displaying the results as instructed by the prompt, the user can quickly obtain the information necessary to support the person receiving care.

[0344] 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.

[0345] This invention relates to a system that provides more humane and insightful certification results by combining an emotion engine that recognizes the user's emotions with the care certification assessment process. This system consists of an information processing device, multiple knowledge processing devices, and an emotion engine.

[0346] First, the user enters detailed information about the person being cared for through the terminal. During this process, the emotion engine analyzes the user's input patterns and interactions in real time to evaluate the user's emotional state.

[0347] When the device sends acquired information to the server, it also includes emotional data recognized by the emotion engine. The server stores and organizes this data in a database.

[0348] The server activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. These units perform individual analyses based on relevant information, including emotional data. By considering the user's emotional state, as indicated by the emotion engine, during the analysis, more flexible decision-making becomes possible.

[0349] The knowledge processing device sends the analysis results to the server, which aggregates them and facilitates discussions among the devices. During these discussions, the user's emotional data obtained from the emotion engine is reflected and treated as an important factor in decision-making. For example, if a user expresses emotions such as "worry" or "anxiety" during input, these emotions are considered as indicators of the need for enhanced support from the medical side.

[0350] Finally, the server generates a report based on the certification level obtained as a result of the consultation and provides the results to the user via the terminal. Furthermore, the database is updated based on feedback, including sentiment data from the sentiment engine, to continuously improve the system.

[0351] As described above, by combining it with an emotional engine, it becomes possible to provide comprehensive care assessments that go beyond conventional mechanical processes and capture the emotional needs of users.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The user inputs detailed information about the person being cared for, such as their health status and medical history, into the terminal. The terminal uses an emotion engine to analyze the user's typing speed and input content during the input process and evaluates their emotional state in real time.

[0355] Step 2:

[0356] The device prepares to format the evaluated sentiment data and entered information appropriately and send it to the server. This format includes metadata about the user's emotional state.

[0357] Step 3:

[0358] The server stores the received data and sentiment metadata in a database and activates knowledge processing units specialized for each area of ​​expertise. The database is organized to facilitate reference in subsequent analysis processes.

[0359] Step 4:

[0360] The server distributes the corresponding information, including emotional data, to each knowledge processing unit. This allows each unit to begin data analysis based on its own specialized knowledge.

[0361] Step 5:

[0362] The knowledge processing device performs individual analysis based on the sorted data, while also incorporating the user's emotional state, as indicated by the emotion engine, into the analysis process. This enables flexible recognition decisions that are tailored to the user's emotions.

[0363] Step 6:

[0364] The server integrates the analysis results collected from the knowledge processing units and facilitates discussions between them. These discussions include analysis results incorporating emotional data.

[0365] Step 7:

[0366] The knowledge processing system considers emotional data during discussions and mutually proposes the most suitable care needs assessment level. Emotional data is a particularly influential factor in determining the focus of support and the response strategy.

[0367] Step 8:

[0368] The server generates a report based on the final decision obtained through deliberation. This report also includes special considerations based on the user's feelings.

[0369] Step 9:

[0370] The server notifies the user of the generated report via the terminal and accepts feedback. This feedback is reflected in the database to help improve the sentiment data.

[0371] Step 10:

[0372] The server utilizes feedback and emotional data from the emotion engine to continuously optimize the system. This will enable more accurate care needs assessments in the next review process.

[0373] (Example 2)

[0374] 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".

[0375] Traditional care needs assessment processes prioritize mechanical judgments based on detailed information and medical data of the care recipient, often failing to adequately consider the user's emotions and emotional needs. As a result, assessment results may not reflect the user's true needs, potentially leading to inadequate support.

[0376] 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.

[0377] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields within the information processing device, means for accumulating the results of analyses performed individually by the knowledge processing devices, means for incorporating an emotion engine that analyzes the user's emotional state in real time, and means for the knowledge processing devices to consult with each other based on the analysis results and emotion data to make a final decision. This makes it possible to capture the emotional needs of the user and provide care assessment results that are more humane and insightful.

[0378] An "information processing device" is a general term for hardware and software used for data collection, analysis, and management.

[0379] A "knowledge processing device" is a device that analyzes data and makes decisions based on specific expertise.

[0380] An "emotion engine" is a system that analyzes a user's emotional state in real time based on their input patterns and interactions, and extracts the data from that analysis.

[0381] "Emotional data" refers to information analyzed by an emotion engine that indicates the user's emotional state.

[0382] "Consultation" is the process by which multiple knowledge processing devices bring together their analysis results to make a final decision.

[0383] "Judgment" refers to an evaluation or decision made based on various data, and is ultimately presented as the certification result for the user.

[0384] A description of embodiments for carrying out this invention will be given.

[0385] The user first enters detailed information about the person receiving care into the terminal. This includes the person's name, age, health status, and care needs. During this process, an emotion engine is built into the terminal, analyzing the user's input patterns and actions in real time to acquire emotional data. The emotion engine uses an algorithm that estimates the user's emotional state based on input speed and rhythm, as well as mouse movements.

[0386] The terminal transmits the acquired user details and emotional data to the server. The server, acting as an information processing device, activates multiple knowledge processing devices, each with its own area of ​​expertise. These knowledge processing devices specialize in various fields such as medicine, health, and nutrition, and perform detailed analysis in their respective fields based on the information stored in the database. The analysis takes into account the results of the emotional engine's analysis, allowing for flexibility in decision-making.

[0387] The server collects analysis results from each knowledge processing unit, discusses the data, and makes a final decision on the care needs assessment. Generative AI models are used in this process to improve the accuracy of the judgment by comparing it with past data. When a user expresses emotions such as "worry" or "anxiety," the system incorporates this emotional data and determines whether enhanced care support is necessary.

[0388] For example, if a user inputs information about the health status of a person receiving care and indicates "anxiety," the knowledge processing unit receives this emotional data and analyzes it to increase the need for medical support. Based on the final assessment results, a report is generated and provided to the user via the terminal. The emotional data received as feedback is also reflected in the database, leading to further improvements to the system.

[0389] A specific example of a prompt would be, "If the user expresses feelings of 'anxiety' while entering care records, how should the care plan be adjusted?" This allows the system to suggest specific support strategies.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The user uses a terminal to input detailed information about the person being cared for. This information includes the person's name, age, health status, and care needs. As the user inputs information, the emotion engine analyzes the interaction and estimates the emotional state from patterns such as input speed and mouse movements. At this stage, two types of data are generated: input data and emotion data.

[0393] Step 2:

[0394] The device sends detailed information obtained from the user and sentiment data generated by the sentiment engine to the server. This transmission takes place in a secure environment, and the information is encrypted. The server receives this data, stores it in a database, and categorizes it. Specifically, a unique identifier is assigned to each input data to prepare for future analysis.

[0395] Step 3:

[0396] The server activates multiple knowledge processing units, each specialized in a particular field, based on the information stored in the database. These include categories such as medicine, health, and nutrition. The knowledge processing units retrieve necessary information from the database and perform expert analysis. They use emotional data, among other factors, to determine the appropriate care support. Each processing unit uses its own algorithm to gain insights and generate analysis results.

[0397] Step 4:

[0398] The server aggregates the analysis results from each knowledge processing unit and conducts discussions based on them. The knowledge processing units collaborate with each other, using generative AI models to compare and examine past data with current analysis results. In this process, the user's emotional state is considered as an important factor in decision-making. For example, if a user expresses "anxiety," that emotion may be suggested as a reason for needing enhanced medical support.

[0399] Step 5:

[0400] The server generates a report based on the final decision made after deliberation. This report includes detailed analysis results and recommended support, providing useful information for the user. The generated report is delivered to the user via their terminal. The output here includes the care assessment result and a specific support plan.

[0401] Step 6:

[0402] Users receive reports and provide feedback on their contents. This feedback, along with sentiment data, is returned to the server and becomes an important factor in updating the database. The server uses this data to improve the system and as training data to improve the accuracy of future prompt generation and sentiment analysis.

[0403] (Application Example 2)

[0404] 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."

[0405] A challenge with the conventional care needs assessment process is that it fails to consider the user's emotional state, resulting in mechanical and inflexible outcomes. Furthermore, it lacks consideration for the psychological burden on caregivers, making it difficult to provide support that meets individual needs.

[0406] 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.

[0407] In this invention, the server includes means for combining an emotion recognition engine that analyzes the user's emotional state in real time, means for activating multiple knowledge processing devices specialized in various fields, and means for proposing additional support to the user based on the emotional data. This enables flexible and humane care assessment while taking the user's emotions into consideration.

[0408] An "information processing device" is a device or system that receives information input from a user and performs analysis and data storage.

[0409] An "emotion recognition engine" is a technology that analyzes user input and actions and evaluates the underlying emotional state in real time.

[0410] A "knowledge processing device" is a device or program that specializes in a particular field of expertise and is used to analyze related data.

[0411] "Analysis results" refer to the final output of the analysis performed by the knowledge processing device based on the input data.

[0412] "Consultation" is a process in which multiple knowledge processing devices discuss and make integrated decisions based on their analysis results.

[0413] "Emotional data" refers to information about a user's emotional state, analyzed by an emotion recognition engine.

[0414] "Additional support" refers to supplementary care-related services and support suggested based on the user's emotional data and circumstances.

[0415] A "database" is a system that stores analysis results and emotional data so that they can be used later.

[0416] A "report" is a document containing the final judgment results and is information submitted to an external device.

[0417] "External devices" refer to devices or systems used to receive the results of a report.

[0418] The system implementing this invention is configured to improve the process of assessing long-term care needs by linking an information processing device, multiple knowledge processing devices, and an emotion recognition engine. The following hardware and software are used for implementation.

[0419] The information processing device functions as a terminal for users to input detailed information for care needs assessment. This terminal is equipped with emotion recognition software and uses an emotion engine to acquire user emotion data in real time from input and operations. Specifically, it utilizes emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure Face API).

[0420] The device sends acquired information and sentiment data to the server. The server stores this data in a database (e.g., MySQL, Firebase) and activates knowledge processing units specialized for each area of ​​expertise. The knowledge processing units perform analysis that also takes sentiment data into consideration during the analysis process.

[0421] For example, if the emotion engine indicates that a user is experiencing anxiety, the system, through its knowledge processing unit, determines that additional care support is needed based on that anxiety. Based on this determination, it becomes possible to propose additional support to the user.

[0422] The final assessment results are compiled by the server and generated as a report. This report is then notified to relevant external organizations. Throughout the evaluation process, the server continuously updates its database and works to improve the system.

[0423] As a specific example, when conducting an online care needs assessment, if the emotion recognition engine detects that a user has expressed "worry" or "anxiety," the system will immediately respond by determining whether mental health care is needed and presenting options for consulting with a specialist.

[0424] An example of a prompt using a generative AI model is: "If the user shows anxiety while inputting emotional data through the app, what kind of care plan should you suggest? Based on the emotional data provided by the emotion engine, please provide the suggested care plan in the following format."

[0425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0426] Step 1:

[0427] The terminal receives input from the user of information necessary for care needs assessment. The data entered by the user includes basic personal information and details regarding the need for care. At this time, the emotion recognition engine installed in the terminal analyzes the user's facial expressions and input patterns in real time and outputs their emotional state as numerical data. The input to the emotion recognition engine is the user's facial expression data, and the output is a report of their emotional state.

[0428] Step 2:

[0429] The terminal sends the acquired user data and emotional state report to the server. The server stores the received data in a database and organizes it. The input data is user information and emotional state, and the output is an update to the database in an organized format.

[0430] Step 3:

[0431] The server activates knowledge processing units specialized for each area of ​​expertise. These units retrieve necessary information from the database and begin analysis that takes emotional data into account. An analysis algorithm is then applied based on the emotional data. The input consists of user information and emotional states from the database, and the output is an individually analyzed expert evaluation.

[0432] Step 4:

[0433] The server aggregates the analysis results obtained from the knowledge processing devices and uses this information to conduct discussions among the devices. In these discussions, each analysis result and emotional data are considered as important factors, and the final care needs assessment result is derived. The input consists of multiple analysis results and emotional data, and the output is a provisional care needs assessment decision after the discussions.

[0434] Step 5:

[0435] The server generates a report based on the final assessment results and sentiment data, and notifies an external device of the results. This report is formatted into a specific format and communicated to care service providers and other relevant organizations. Its input is the final assessment results and sentiment data, and its output is document data in the form of a report.

[0436] Step 6:

[0437] The server incorporates data acquired through each process and user feedback into a database, using it to improve the system. In particular, it contributes to improving the accuracy of the emotion recognition engine and optimizing the algorithms of the knowledge processing unit. The input is feedback data, and the output is improved system performance.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] This invention relates to a multi-agent system aimed at improving the efficiency and accuracy of long-term care certification assessments. This system consists of an information processing device and multiple knowledge processing devices, and complements assessments by experts in each field.

[0455] First, the user enters detailed information about the person receiving care through the terminal. This includes health status, medical history, and living situation. The terminal organizes the entered data and converts it into the appropriate format for transmission to the server.

[0456] The server stores the received data in a database and activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. It then sorts the relevant data so that the knowledge processing units can proceed with their individual analyses. During this process, the knowledge processing units analyze the data based on their specialized knowledge and construct hypotheses.

[0457] Each knowledge processing device sends its analysis results to a server, which collects them and facilitates discussions among the devices. During these discussions, each knowledge processing device announces its conclusions based on its analysis, and through exchanges of opinions with other devices, the optimal care certification level is determined.

[0458] For example, a medical knowledge processing system might point out a risk of heart disease, while a nutrition knowledge processing system might suggest the need for a low-salt diet. A health knowledge processing system would then offer suggestions to compensate for limitations in daily life, thereby unifying knowledge from each specialized field to make a final decision.

[0459] Ultimately, the server generates a report based on the results of the discussion and provides the results to the user via the terminal. Furthermore, by continuously updating the database based on feedback, the system automatically optimizes itself, improving the accuracy of future reviews.

[0460] Therefore, this system not only reduces the time burden on professionals but also improves the validity and fairness of care needs assessment.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user enters detailed information about the care recipient, such as their health status, medical history, and living situation, into the terminal. This information is neatly organized through an input form.

[0464] Step 2:

[0465] The terminal receives the input information and converts it into the appropriate data format. This prepares the information in a way that makes it easy for the server to process.

[0466] Step 3:

[0467] The terminal sends formatted data to the server. This data is treated as basic information necessary for assessing the level of care needs assessment.

[0468] Step 4:

[0469] The server stores the received data in a database and performs initial data processing. This makes the data easily accessible in subsequent processing steps.

[0470] Step 5:

[0471] The server activates multiple knowledge processing units, each specialized for a particular field. Knowledge processing units for the necessary specialized fields, such as medicine, health, and nutrition, are called up sequentially.

[0472] Step 6:

[0473] The server distributes the accumulated data to the relevant knowledge processing devices. This allows each device to begin data analysis based on its own area of ​​expertise.

[0474] Step 7:

[0475] The knowledge processing unit performs individual analyses based on the sorted data. The medical agent assesses the risk of disease, and the nutrition agent considers a dietary plan.

[0476] Step 8:

[0477] The server aggregates the analysis results obtained from each knowledge processing device and uses them to facilitate discussions among the knowledge processing devices.

[0478] Step 9:

[0479] The knowledge processing system conducts discussions and exchanges analysis results to derive the optimal care certification level. Through this process, specialized knowledge is integrated, and the final judgment is formed.

[0480] Step 10:

[0481] The server generates a report based on the optimal care needs assessment level determined as a result of the consultation.

[0482] Step 11:

[0483] The server provides the generated report to the user via the terminal. It also accepts user feedback through a feedback function.

[0484] Step 12:

[0485] The server records user feedback in a database and uses it as system learning data. This automatically optimizes the next review process.

[0486] (Example 1)

[0487] 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."

[0488] The current care needs assessment process is inefficient because it requires the involvement of many specialists, is time-consuming, and laborious. Furthermore, the accuracy and fairness of the assessment results are easily influenced by the subjective opinions of the specialists. There is a need to resolve these issues and implement a faster and more accurate assessment process.

[0489] 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.

[0490] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields, means for accumulating the results of analyses performed individually by the knowledge processing devices, and means for the knowledge processing devices to consult with each other based on the analysis results and make an optimal decision. This enables a rapid, fair, and highly accurate care certification process.

[0491] An "input device" is a device used by users to input information, and its role is to convert the input information into a predetermined format and transmit it to an information processing device.

[0492] An "information processing device" is a device that receives information transmitted from an input device and performs the various functions necessary for analysis and decision-making.

[0493] A "knowledge processing device" is a device that uses specialized knowledge to analyze data and construct hypotheses.

[0494] "Analysis results" refer to the data and conclusions obtained after a knowledge processing device analyzes the input data.

[0495] "Consultation" refers to the exchange of information and discussion between knowledge processing devices based on analysis results, with the aim of deriving the optimal decision.

[0496] A "report" is a document created based on the results of discussions and includes the final judgment and proposals.

[0497] An "external device" is a device that receives output from an information processing device and notifies the user of the results.

[0498] "Feedback" refers to the reactions and information received by a knowledge processing device, which is used to update the database.

[0499] A description of embodiments for carrying out the present invention will be provided.

[0500] The user enters detailed information about the person receiving care using a terminal. This input includes a wide range of information, such as health status, medical history, and living situation. The terminal converts this entered information into a predetermined format and prepares the data for transmission to the server. At this time, a generation AI model is used to display a prompt message, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life," to ensure the user enters the information accurately.

[0501] The server receives data transmitted from terminals and stores it in a central data storage device. The server then activates knowledge processing devices specialized for each area of ​​expertise and distributes the data in a format suitable for each device. The knowledge processing devices analyze the data based on their respective areas of expertise and construct hypotheses. This process involves specialized analysis in the fields of medicine, nutrition, and health.

[0502] The server integrates the analysis results returned from the knowledge processing devices and manages inter-device consultations. This consultation is crucial for deriving final decisions based on the analysis results and serves as a centralized platform for the specialized knowledge of each knowledge processing device.

[0503] Based on the final consultation results, the server generates a report and sends it to the terminal. This allows the user to receive the results of their care needs assessment and a specific care plan.

[0504] Through the process described above, the present invention aims to improve the efficiency and accuracy of care needs assessment and provide users with useful information.

[0505] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0506] Step 1:

[0507] The user enters detailed information about the person receiving care. During this process, a guided prompt using a generated AI model appears on the terminal, prompting the user to enter information such as, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life." The input includes health status, medical history, and living situation. This input data is validated on the terminal before being converted into data ready for transmission.

[0508] Step 2:

[0509] The terminal converts the information entered by the user into an appropriate data format. Specifically, it uses a format conversion function to format the input text into CSV or JSON format. After data conversion, the terminal sends it to the server. The output of this series of processes is detailed information about the caregiver, converted into a format that can be processed by the server.

[0510] Step 3:

[0511] The server stores data received from terminals in a central database. This storage process involves data integrity and duplicate checks via a database management system. The input is formatted caregiver information, and the output is reliable information stored in the database.

[0512] Step 4:

[0513] The server activates knowledge processing units specialized for each area of ​​expertise. In this process, data is distributed to analysis units corresponding to the medical, health, and nutrition fields. The input consists of information from a database specific to each field, which is then supplied to the knowledge processing units. The output of the knowledge processing units consists of hypotheses and risk assessments based on initial analysis.

[0514] Step 5:

[0515] Knowledge processing devices analyze the assigned data and perform calculations to construct hypotheses. For example, they utilize statistical models and machine learning algorithms to assess health risks and nutritional balance. The input is data related to a specific field, and the output is specific analysis results based on that data.

[0516] Step 6:

[0517] The server receives the analysis results and initiates a consultation among the knowledge processing units. During the consultation, each knowledge processing unit exchanges opinions and determines the optimal care assessment level and proposal. The input is the analysis results from each individual knowledge processing unit, and the output is the final care plan integrated after the consultation.

[0518] Step 7:

[0519] The server generates a report based on the final outcome of the consultation. The report generation function is used to document and format the certification results. The output report is a detailed document including the care certification level and recommended care plan.

[0520] Step 8:

[0521] The user receives reports from the server via their terminal. If the user provides feedback, the server receives that feedback and updates the database to use it for future analyses. The input is the feedback information, and the output is the updated database.

[0522] (Application Example 1)

[0523] 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."

[0524] Conventional care needs assessment systems require a tremendous amount of time and effort from experts, and there is a need for increased efficiency and accuracy in the assessment process. Furthermore, there is a lack of a system that allows users to easily input data from their own devices and receive analysis results in real time. As a result, there is a problem in that appropriate support for those receiving care is delayed, and fair and timely care needs assessment is difficult.

[0525] 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.

[0526] In this invention, the server includes means for collecting user input data from a terminal in an information processing device and transmitting it to a database in an organized format, means for activating multiple knowledge processing devices specialized for each specialized field, and means for displaying the received analysis results on the user's information terminal. This significantly improves the efficiency of the examination process and makes it possible to receive analysis results in real time based on the information entered by the user.

[0527] An "information processing device" is a device that efficiently receives, analyzes, and displays data results to the user.

[0528] A "knowledge processing device" is a device that analyzes data in a specific specialized field and has the function of sharing and discussing the results with other devices.

[0529] "User input data" refers to information provided by the user via their device, such as the health status and living conditions of the person receiving care.

[0530] A "database" is a storage area that organizes and stores collected data and provides it to knowledge processing devices and information processing devices as needed.

[0531] "Analysis results" refer to information that shows conclusions or views obtained after a knowledge processing device has processed input data.

[0532] An "information terminal" is a device used by users to input data or check results.

[0533] This invention comprises a system aimed at improving the efficiency and accuracy of care needs assessment. The system is based on an information processing device and includes multiple knowledge processing devices specialized for each specialized field. The entire system operates using user terminals, a central server, and knowledge processing devices for each specialized field.

[0534] Users input data about the person receiving care using devices such as smartphones or smart glasses. The entered information is sent to the server in an organized format in a database. The server then activates various knowledge processing units and distributes the input data to the appropriate specialized domain.

[0535] The knowledge processing device performs expert analysis based on input data and sends the resulting insights to the server. The server aggregates these analysis results and supports discussions to integrate the overall analysis. The server makes the final judgment, organizes the results, and sends and displays them on the user's information terminal.

[0536] As a concrete example, when supporting an elderly person living alone in Tokyo, the user inputs details about the elderly person's health condition and lifestyle through a terminal. Based on the analysis results from this information, the system facilitates discussions between knowledge processing devices and proposes the optimal care level.

[0537] Example of a prompt:

[0538] Please enter the health status and medical history of the person receiving care.

[0539] To check your care needs assessment level, please click the button below.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] Users input information about the care recipient's health and living situation using a smartphone or smart glasses. This input data includes health status, medical history, and living environment. This information is formatted appropriately within the device and prepared for transmission to the database.

[0543] Step 2:

[0544] The data formatted by the terminal is sent to the server via the internet. The server verifies the received data and stores it in a database. The data obtained as input is organized within the database for subsequent analysis.

[0545] Step 3:

[0546] The server individually starts up the knowledge processing units and distributes the data retrieved from the database to each unit. In this process, data is sent to the unit in the relevant specialized field depending on the type of data. Each knowledge processing unit then begins data analysis based on its specialized knowledge.

[0547] Step 4:

[0548] In the knowledge processing unit, input data is analyzed. For example, a knowledge processing unit in the medical field assesses the risk of heart disease, while a knowledge processing unit in the nutrition field determines the necessity of a low-salt diet. Based on the analysis, each knowledge processing unit outputs its own conclusions and sends them back to the server.

[0549] Step 5:

[0550] The server aggregates the analysis results from each knowledge processing device and uses this data to support discussions. During the discussions, the knowledge processing devices share their conclusions and exchange opinions to determine the optimal care certification level. The server manages this process and makes the final decision.

[0551] Step 6:

[0552] The final assessment result is sent from the server to the user's terminal. The user can check the analysis results on their terminal and evaluate the proposed care needs assessment level. By displaying the results as instructed by the prompt, the user can quickly obtain the information necessary to support the person receiving care.

[0553] 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.

[0554] This invention relates to a system that provides more humane and insightful certification results by combining an emotion engine that recognizes the user's emotions with the care certification assessment process. This system consists of an information processing device, multiple knowledge processing devices, and an emotion engine.

[0555] First, the user enters detailed information about the person being cared for through the terminal. During this process, the emotion engine analyzes the user's input patterns and interactions in real time to evaluate the user's emotional state.

[0556] When the device sends acquired information to the server, it also includes emotional data recognized by the emotion engine. The server stores and organizes this data in a database.

[0557] The server activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. These units perform individual analyses based on relevant information, including emotional data. By considering the user's emotional state, as indicated by the emotion engine, during the analysis, more flexible decision-making becomes possible.

[0558] The knowledge processing device sends the analysis results to the server, which aggregates them and facilitates discussions among the devices. During these discussions, the user's emotional data obtained from the emotion engine is reflected and treated as an important factor in decision-making. For example, if a user expresses emotions such as "worry" or "anxiety" during input, these emotions are considered as indicators of the need for enhanced support from the medical side.

[0559] Finally, the server generates a report based on the certification level obtained as a result of the consultation and provides the results to the user via the terminal. Furthermore, the database is updated based on feedback, including sentiment data from the sentiment engine, to continuously improve the system.

[0560] As described above, by combining it with an emotional engine, it becomes possible to provide comprehensive care assessments that go beyond conventional mechanical processes and capture the emotional needs of users.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] The user inputs detailed information about the person being cared for, such as their health status and medical history, into the terminal. The terminal uses an emotion engine to analyze the user's typing speed and input content during the input process and evaluates their emotional state in real time.

[0564] Step 2:

[0565] The device prepares to format the evaluated sentiment data and entered information appropriately and send it to the server. This format includes metadata about the user's emotional state.

[0566] Step 3:

[0567] The server stores the received data and sentiment metadata in a database and activates knowledge processing units specialized for each area of ​​expertise. The database is organized to facilitate reference in subsequent analysis processes.

[0568] Step 4:

[0569] The server distributes the corresponding information, including emotional data, to each knowledge processing unit. This allows each unit to begin data analysis based on its own specialized knowledge.

[0570] Step 5:

[0571] The knowledge processing device performs individual analysis based on the sorted data, while also incorporating the user's emotional state, as indicated by the emotion engine, into the analysis process. This enables flexible recognition decisions that are tailored to the user's emotions.

[0572] Step 6:

[0573] The server integrates the analysis results collected from the knowledge processing units and facilitates discussions between them. These discussions include analysis results incorporating emotional data.

[0574] Step 7:

[0575] The knowledge processing system considers emotional data during discussions and mutually proposes the most suitable care needs assessment level. Emotional data is a particularly influential factor in determining the focus of support and the response strategy.

[0576] Step 8:

[0577] The server generates a report based on the final decision obtained through deliberation. This report also includes special considerations based on the user's feelings.

[0578] Step 9:

[0579] The server notifies the user of the generated report via the terminal and accepts feedback. This feedback is reflected in the database to help improve the sentiment data.

[0580] Step 10:

[0581] The server utilizes feedback and emotional data from the emotion engine to continuously optimize the system. This will enable more accurate care needs assessments in the next review process.

[0582] (Example 2)

[0583] 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."

[0584] Traditional care needs assessment processes prioritize mechanical judgments based on detailed information and medical data of the care recipient, often failing to adequately consider the user's emotions and emotional needs. As a result, assessment results may not reflect the user's true needs, potentially leading to inadequate support.

[0585] 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.

[0586] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields within the information processing device, means for accumulating the results of analyses performed individually by the knowledge processing devices, means for incorporating an emotion engine that analyzes the user's emotional state in real time, and means for the knowledge processing devices to consult with each other based on the analysis results and emotion data to make a final decision. This makes it possible to capture the emotional needs of the user and provide care assessment results that are more humane and insightful.

[0587] An "information processing device" is a general term for hardware and software used for data collection, analysis, and management.

[0588] A "knowledge processing device" is a device that analyzes data and makes decisions based on specific expertise.

[0589] An "emotion engine" is a system that analyzes a user's emotional state in real time based on their input patterns and interactions, and extracts the data from that analysis.

[0590] "Emotional data" refers to information analyzed by an emotion engine that indicates the user's emotional state.

[0591] "Consultation" is the process by which multiple knowledge processing devices bring together their analysis results to make a final decision.

[0592] "Judgment" refers to an evaluation or decision made based on various data, and is ultimately presented as the certification result for the user.

[0593] A description of embodiments for carrying out this invention will be given.

[0594] The user first enters detailed information about the person receiving care into the terminal. This includes the person's name, age, health status, and care needs. During this process, an emotion engine is built into the terminal, analyzing the user's input patterns and actions in real time to acquire emotional data. The emotion engine uses an algorithm that estimates the user's emotional state based on input speed and rhythm, as well as mouse movements.

[0595] The terminal transmits the acquired user details and emotional data to the server. The server, acting as an information processing device, activates multiple knowledge processing devices, each with its own area of ​​expertise. These knowledge processing devices specialize in various fields such as medicine, health, and nutrition, and perform detailed analysis in their respective fields based on the information stored in the database. The analysis takes into account the results of the emotional engine's analysis, allowing for flexibility in decision-making.

[0596] The server collects analysis results from each knowledge processing unit, discusses the data, and makes a final decision on the care needs assessment. Generative AI models are used in this process to improve the accuracy of the judgment by comparing it with past data. When a user expresses emotions such as "worry" or "anxiety," the system incorporates this emotional data and determines whether enhanced care support is necessary.

[0597] For example, if a user inputs information about the health status of a person receiving care and indicates "anxiety," the knowledge processing unit receives this emotional data and analyzes it to increase the need for medical support. Based on the final assessment results, a report is generated and provided to the user via the terminal. The emotional data received as feedback is also reflected in the database, leading to further improvements to the system.

[0598] A specific example of a prompt would be, "If the user expresses feelings of 'anxiety' while entering care records, how should the care plan be adjusted?" This allows the system to suggest specific support strategies.

[0599] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0600] Step 1:

[0601] The user uses a terminal to input detailed information about the person being cared for. This information includes the person's name, age, health status, and care needs. As the user inputs information, the emotion engine analyzes the interaction and estimates the emotional state from patterns such as input speed and mouse movements. At this stage, two types of data are generated: input data and emotion data.

[0602] Step 2:

[0603] The device sends detailed information obtained from the user and sentiment data generated by the sentiment engine to the server. This transmission takes place in a secure environment, and the information is encrypted. The server receives this data, stores it in a database, and categorizes it. Specifically, a unique identifier is assigned to each input data to prepare for future analysis.

[0604] Step 3:

[0605] The server activates multiple knowledge processing units, each specialized in a particular field, based on the information stored in the database. These include categories such as medicine, health, and nutrition. The knowledge processing units retrieve necessary information from the database and perform expert analysis. They use emotional data, among other factors, to determine the appropriate care support. Each processing unit uses its own algorithm to gain insights and generate analysis results.

[0606] Step 4:

[0607] The server aggregates the analysis results from each knowledge processing unit and conducts discussions based on them. The knowledge processing units collaborate with each other, using generative AI models to compare and examine past data with current analysis results. In this process, the user's emotional state is considered as an important factor in decision-making. For example, if a user expresses "anxiety," that emotion may be suggested as a reason for needing enhanced medical support.

[0608] Step 5:

[0609] The server generates a report based on the final decision made after deliberation. This report includes detailed analysis results and recommended support, providing useful information for the user. The generated report is delivered to the user via their terminal. The output here includes the care assessment result and a specific support plan.

[0610] Step 6:

[0611] Users receive reports and provide feedback on their contents. This feedback, along with sentiment data, is returned to the server and becomes an important factor in updating the database. The server uses this data to improve the system and as training data to improve the accuracy of future prompt generation and sentiment analysis.

[0612] (Application Example 2)

[0613] 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."

[0614] A challenge with the conventional care needs assessment process is that it fails to consider the user's emotional state, resulting in mechanical and inflexible outcomes. Furthermore, it lacks consideration for the psychological burden on caregivers, making it difficult to provide support that meets individual needs.

[0615] 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.

[0616] In this invention, the server includes means for combining an emotion recognition engine that analyzes the user's emotional state in real time, means for activating multiple knowledge processing devices specialized in various fields, and means for proposing additional support to the user based on the emotional data. This enables flexible and humane care assessment while taking the user's emotions into consideration.

[0617] An "information processing device" is a device or system that receives information input from a user and performs analysis and data storage.

[0618] An "emotion recognition engine" is a technology that analyzes user input and actions and evaluates the underlying emotional state in real time.

[0619] A "knowledge processing device" is a device or program that specializes in a particular field of expertise and is used to analyze related data.

[0620] "Analysis results" refer to the final output of the analysis performed by the knowledge processing device based on the input data.

[0621] "Consultation" is a process in which multiple knowledge processing devices discuss and make integrated decisions based on their analysis results.

[0622] "Emotional data" refers to information about a user's emotional state, analyzed by an emotion recognition engine.

[0623] "Additional support" refers to supplementary care-related services and support suggested based on the user's emotional data and circumstances.

[0624] A "database" is a system that stores analysis results and emotional data so that they can be used later.

[0625] A "report" is a document containing the final judgment results and is information submitted to an external device.

[0626] "External devices" refer to devices or systems used to receive the results of a report.

[0627] The system implementing this invention is configured to improve the process of assessing long-term care needs by linking an information processing device, multiple knowledge processing devices, and an emotion recognition engine. The following hardware and software are used for implementation.

[0628] The information processing device functions as a terminal for users to input detailed information for care needs assessment. This terminal is equipped with emotion recognition software and uses an emotion engine to acquire user emotion data in real time from input and operations. Specifically, it utilizes emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure Face API).

[0629] The device sends acquired information and sentiment data to the server. The server stores this data in a database (e.g., MySQL, Firebase) and activates knowledge processing units specialized for each area of ​​expertise. The knowledge processing units perform analysis that also takes sentiment data into consideration during the analysis process.

[0630] For example, if the emotion engine indicates that a user is experiencing anxiety, the system, through its knowledge processing unit, determines that additional care support is needed based on that anxiety. Based on this determination, it becomes possible to propose additional support to the user.

[0631] The final assessment results are compiled by the server and generated as a report. This report is then notified to relevant external organizations. Throughout the evaluation process, the server continuously updates its database and works to improve the system.

[0632] As a specific example, when conducting an online care needs assessment, if the emotion recognition engine detects that a user has expressed "worry" or "anxiety," the system will immediately respond by determining whether mental health care is needed and presenting options for consulting with a specialist.

[0633] An example of a prompt using a generative AI model is: "If the user shows anxiety while inputting emotional data through the app, what kind of care plan should you suggest? Based on the emotional data provided by the emotion engine, please provide the suggested care plan in the following format."

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The terminal receives input from the user of information necessary for care needs assessment. The data entered by the user includes basic personal information and details regarding the need for care. At this time, the emotion recognition engine installed in the terminal analyzes the user's facial expressions and input patterns in real time and outputs their emotional state as numerical data. The input to the emotion recognition engine is the user's facial expression data, and the output is a report of their emotional state.

[0637] Step 2:

[0638] The terminal sends the acquired user data and emotional state report to the server. The server stores the received data in a database and organizes it. The input data is user information and emotional state, and the output is an update to the database in an organized format.

[0639] Step 3:

[0640] The server activates knowledge processing units specialized for each area of ​​expertise. These units retrieve necessary information from the database and begin analysis that takes emotional data into account. An analysis algorithm is then applied based on the emotional data. The input consists of user information and emotional states from the database, and the output is an individually analyzed expert evaluation.

[0641] Step 4:

[0642] The server aggregates the analysis results obtained from the knowledge processing devices and uses this information to conduct discussions among the devices. In these discussions, each analysis result and emotional data are considered as important factors, and the final care needs assessment result is derived. The input consists of multiple analysis results and emotional data, and the output is a provisional care needs assessment decision after the discussions.

[0643] Step 5:

[0644] The server generates a report based on the final assessment results and sentiment data, and notifies an external device of the results. This report is formatted into a specific format and communicated to care service providers and other relevant organizations. Its input is the final assessment results and sentiment data, and its output is document data in the form of a report.

[0645] Step 6:

[0646] The server incorporates data acquired through each process and user feedback into a database, using it to improve the system. In particular, it contributes to improving the accuracy of the emotion recognition engine and optimizing the algorithms of the knowledge processing unit. The input is feedback data, and the output is improved system performance.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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".

[0664] This invention relates to a multi-agent system aimed at improving the efficiency and accuracy of long-term care certification assessments. This system consists of an information processing device and multiple knowledge processing devices, and complements assessments by experts in each field.

[0665] First, the user enters detailed information about the person receiving care through the terminal. This includes health status, medical history, and living situation. The terminal organizes the entered data and converts it into the appropriate format for transmission to the server.

[0666] The server stores the received data in a database and activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. It then sorts the relevant data so that the knowledge processing units can proceed with their individual analyses. During this process, the knowledge processing units analyze the data based on their specialized knowledge and construct hypotheses.

[0667] Each knowledge processing device sends its analysis results to a server, which collects them and facilitates discussions among the devices. During these discussions, each knowledge processing device announces its conclusions based on its analysis, and through exchanges of opinions with other devices, the optimal care certification level is determined.

[0668] For example, a medical knowledge processing system might point out a risk of heart disease, while a nutrition knowledge processing system might suggest the need for a low-salt diet. A health knowledge processing system would then offer suggestions to compensate for limitations in daily life, thereby unifying knowledge from each specialized field to make a final decision.

[0669] Ultimately, the server generates a report based on the results of the discussion and provides the results to the user via the terminal. Furthermore, by continuously updating the database based on feedback, the system automatically optimizes itself, improving the accuracy of future reviews.

[0670] Therefore, this system not only reduces the time burden on professionals but also improves the validity and fairness of care needs assessment.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The user enters detailed information about the care recipient, such as their health status, medical history, and living situation, into the terminal. This information is neatly organized through an input form.

[0674] Step 2:

[0675] The terminal receives the input information and converts it into the appropriate data format. This prepares the information in a way that makes it easy for the server to process.

[0676] Step 3:

[0677] The terminal sends formatted data to the server. This data is treated as basic information necessary for assessing the level of care needs assessment.

[0678] Step 4:

[0679] The server stores the received data in a database and performs initial data processing. This makes the data easily accessible in subsequent processing steps.

[0680] Step 5:

[0681] The server activates multiple knowledge processing units, each specialized for a particular field. Knowledge processing units for the necessary specialized fields, such as medicine, health, and nutrition, are called up sequentially.

[0682] Step 6:

[0683] The server distributes the accumulated data to the relevant knowledge processing devices. This allows each device to begin data analysis based on its own area of ​​expertise.

[0684] Step 7:

[0685] The knowledge processing unit performs individual analyses based on the sorted data. The medical agent assesses the risk of disease, and the nutrition agent considers a dietary plan.

[0686] Step 8:

[0687] The server aggregates the analysis results obtained from each knowledge processing device and uses them to facilitate discussions among the knowledge processing devices.

[0688] Step 9:

[0689] The knowledge processing system conducts discussions and exchanges analysis results to derive the optimal care certification level. Through this process, specialized knowledge is integrated, and the final judgment is formed.

[0690] Step 10:

[0691] The server generates a report based on the optimal care needs assessment level determined as a result of the consultation.

[0692] Step 11:

[0693] The server provides the generated report to the user via the terminal. It also accepts user feedback through a feedback function.

[0694] Step 12:

[0695] The server records user feedback in a database and uses it as system learning data. This automatically optimizes the next review process.

[0696] (Example 1)

[0697] 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".

[0698] The current care needs assessment process is inefficient because it requires the involvement of many specialists, is time-consuming, and laborious. Furthermore, the accuracy and fairness of the assessment results are easily influenced by the subjective opinions of the specialists. There is a need to resolve these issues and implement a faster and more accurate assessment process.

[0699] 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.

[0700] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields, means for accumulating the results of analyses performed individually by the knowledge processing devices, and means for the knowledge processing devices to consult with each other based on the analysis results and make an optimal decision. This enables a rapid, fair, and highly accurate care certification process.

[0701] An "input device" is a device used by users to input information, and its role is to convert the input information into a predetermined format and transmit it to an information processing device.

[0702] An "information processing device" is a device that receives information transmitted from an input device and performs the various functions necessary for analysis and decision-making.

[0703] A "knowledge processing device" is a device that uses specialized knowledge to analyze data and construct hypotheses.

[0704] "Analysis results" refer to the data and conclusions obtained after a knowledge processing device analyzes the input data.

[0705] "Consultation" refers to the exchange of information and discussion between knowledge processing devices based on analysis results, with the aim of deriving the optimal decision.

[0706] A "report" is a document created based on the results of discussions and includes the final judgment and proposals.

[0707] An "external device" is a device that receives output from an information processing device and notifies the user of the results.

[0708] "Feedback" refers to the reactions and information received by a knowledge processing device, which is used to update the database.

[0709] A description of embodiments for carrying out the present invention will be provided.

[0710] The user enters detailed information about the person receiving care using a terminal. This input includes a wide range of information, such as health status, medical history, and living situation. The terminal converts this entered information into a predetermined format and prepares the data for transmission to the server. At this time, a generation AI model is used to display a prompt message, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life," to ensure the user enters the information accurately.

[0711] The server receives data transmitted from terminals and stores it in a central data storage device. The server then activates knowledge processing devices specialized for each area of ​​expertise and distributes the data in a format suitable for each device. The knowledge processing devices analyze the data based on their respective areas of expertise and construct hypotheses. This process involves specialized analysis in the fields of medicine, nutrition, and health.

[0712] The server integrates the analysis results returned from the knowledge processing devices and manages inter-device consultations. This consultation is crucial for deriving final decisions based on the analysis results and serves as a centralized platform for the specialized knowledge of each knowledge processing device.

[0713] Based on the final consultation results, the server generates a report and sends it to the terminal. This allows the user to receive the results of their care needs assessment and a specific care plan.

[0714] Through the process described above, the present invention aims to improve the efficiency and accuracy of care needs assessment and provide users with useful information.

[0715] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0716] Step 1:

[0717] The user enters detailed information about the person receiving care. During this process, a guided prompt using a generated AI model appears on the terminal, prompting the user to enter information such as, "Please enter the health information of the person receiving care. We will perform an analysis to consider medical risks, nutritional needs, and limitations in daily life." The input includes health status, medical history, and living situation. This input data is validated on the terminal before being converted into data ready for transmission.

[0718] Step 2:

[0719] The terminal converts the information entered by the user into an appropriate data format. Specifically, it uses a format conversion function to format the input text into CSV or JSON format. After data conversion, the terminal sends it to the server. The output of this series of processes is detailed information about the caregiver, converted into a format that can be processed by the server.

[0720] Step 3:

[0721] The server stores data received from terminals in a central database. This storage process involves data integrity and duplicate checks via a database management system. The input is formatted caregiver information, and the output is reliable information stored in the database.

[0722] Step 4:

[0723] The server activates knowledge processing units specialized for each area of ​​expertise. In this process, data is distributed to analysis units corresponding to the medical, health, and nutrition fields. The input consists of information from a database specific to each field, which is then supplied to the knowledge processing units. The output of the knowledge processing units consists of hypotheses and risk assessments based on initial analysis.

[0724] Step 5:

[0725] Knowledge processing devices analyze the assigned data and perform calculations to construct hypotheses. For example, they utilize statistical models and machine learning algorithms to assess health risks and nutritional balance. The input is data related to a specific field, and the output is specific analysis results based on that data.

[0726] Step 6:

[0727] The server receives the analysis results and initiates a consultation among the knowledge processing units. During the consultation, each knowledge processing unit exchanges opinions and determines the optimal care assessment level and proposal. The input is the analysis results from each individual knowledge processing unit, and the output is the final care plan integrated after the consultation.

[0728] Step 7:

[0729] The server generates a report based on the final outcome of the consultation. The report generation function is used to document and format the certification results. The output report is a detailed document including the care certification level and recommended care plan.

[0730] Step 8:

[0731] The user receives reports from the server via their terminal. If the user provides feedback, the server receives that feedback and updates the database to use it for future analyses. The input is the feedback information, and the output is the updated database.

[0732] (Application Example 1)

[0733] 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".

[0734] Conventional care needs assessment systems require a tremendous amount of time and effort from experts, and there is a need for increased efficiency and accuracy in the assessment process. Furthermore, there is a lack of a system that allows users to easily input data from their own devices and receive analysis results in real time. As a result, there is a problem in that appropriate support for those receiving care is delayed, and fair and timely care needs assessment is difficult.

[0735] 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.

[0736] In this invention, the server includes means for collecting user input data from a terminal in an information processing device and transmitting it to a database in an organized format, means for activating multiple knowledge processing devices specialized for each specialized field, and means for displaying the received analysis results on the user's information terminal. This significantly improves the efficiency of the examination process and makes it possible to receive analysis results in real time based on the information entered by the user.

[0737] An "information processing device" is a device that efficiently receives, analyzes, and displays data results to the user.

[0738] A "knowledge processing device" is a device that analyzes data in a specific specialized field and has the function of sharing and discussing the results with other devices.

[0739] "User input data" refers to information provided by the user via their device, such as the health status and living conditions of the person receiving care.

[0740] A "database" is a storage area that organizes and stores collected data and provides it to knowledge processing devices and information processing devices as needed.

[0741] "Analysis results" refer to information that shows conclusions or views obtained after a knowledge processing device has processed input data.

[0742] An "information terminal" is a device used by users to input data or check results.

[0743] This invention comprises a system aimed at improving the efficiency and accuracy of care needs assessment. The system is based on an information processing device and includes multiple knowledge processing devices specialized for each specialized field. The entire system operates using user terminals, a central server, and knowledge processing devices for each specialized field.

[0744] Users input data about the person receiving care using devices such as smartphones or smart glasses. The entered information is sent to the server in an organized format in a database. The server then activates various knowledge processing units and distributes the input data to the appropriate specialized domain.

[0745] The knowledge processing device performs expert analysis based on input data and sends the resulting insights to the server. The server aggregates these analysis results and supports discussions to integrate the overall analysis. The server makes the final judgment, organizes the results, and sends and displays them on the user's information terminal.

[0746] As a concrete example, when supporting an elderly person living alone in Tokyo, the user inputs details about the elderly person's health condition and lifestyle through a terminal. Based on the analysis results from this information, the system facilitates discussions between knowledge processing devices and proposes the optimal care level.

[0747] Example of a prompt:

[0748] Please enter the health status and medical history of the person receiving care.

[0749] To check your care needs assessment level, please click the button below.

[0750] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0751] Step 1:

[0752] Users input information about the care recipient's health and living situation using a smartphone or smart glasses. This input data includes health status, medical history, and living environment. This information is formatted appropriately within the device and prepared for transmission to the database.

[0753] Step 2:

[0754] The data formatted by the terminal is sent to the server via the internet. The server verifies the received data and stores it in a database. The data obtained as input is organized within the database for subsequent analysis.

[0755] Step 3:

[0756] The server individually starts up the knowledge processing units and distributes the data retrieved from the database to each unit. In this process, data is sent to the unit in the relevant specialized field depending on the type of data. Each knowledge processing unit then begins data analysis based on its specialized knowledge.

[0757] Step 4:

[0758] In the knowledge processing unit, input data is analyzed. For example, a knowledge processing unit in the medical field assesses the risk of heart disease, while a knowledge processing unit in the nutrition field determines the necessity of a low-salt diet. Based on the analysis, each knowledge processing unit outputs its own conclusions and sends them back to the server.

[0759] Step 5:

[0760] The server aggregates the analysis results from each knowledge processing device and uses this data to support discussions. During the discussions, the knowledge processing devices share their conclusions and exchange opinions to determine the optimal care certification level. The server manages this process and makes the final decision.

[0761] Step 6:

[0762] The final assessment result is sent from the server to the user's terminal. The user can check the analysis results on their terminal and evaluate the proposed care needs assessment level. By displaying the results as instructed by the prompt, the user can quickly obtain the information necessary to support the person receiving care.

[0763] 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.

[0764] This invention relates to a system that provides more humane and insightful certification results by combining an emotion engine that recognizes the user's emotions with the care certification assessment process. This system consists of an information processing device, multiple knowledge processing devices, and an emotion engine.

[0765] First, the user enters detailed information about the person being cared for through the terminal. During this process, the emotion engine analyzes the user's input patterns and interactions in real time to evaluate the user's emotional state.

[0766] When the device sends acquired information to the server, it also includes emotional data recognized by the emotion engine. The server stores and organizes this data in a database.

[0767] The server activates knowledge processing units specialized in various fields such as medicine, health, and nutrition. These units perform individual analyses based on relevant information, including emotional data. By considering the user's emotional state, as indicated by the emotion engine, during the analysis, more flexible decision-making becomes possible.

[0768] The knowledge processing device sends the analysis results to the server, which aggregates them and facilitates discussions among the devices. During these discussions, the user's emotional data obtained from the emotion engine is reflected and treated as an important factor in decision-making. For example, if a user expresses emotions such as "worry" or "anxiety" during input, these emotions are considered as indicators of the need for enhanced support from the medical side.

[0769] Finally, the server generates a report based on the certification level obtained as a result of the consultation and provides the results to the user via the terminal. Furthermore, the database is updated based on feedback, including sentiment data from the sentiment engine, to continuously improve the system.

[0770] As described above, by combining it with an emotional engine, it becomes possible to provide comprehensive care assessments that go beyond conventional mechanical processes and capture the emotional needs of users.

[0771] The following describes the processing flow.

[0772] Step 1:

[0773] The user inputs detailed information about the person being cared for, such as their health status and medical history, into the terminal. The terminal uses an emotion engine to analyze the user's typing speed and input content during the input process and evaluates their emotional state in real time.

[0774] Step 2:

[0775] The device prepares to format the evaluated sentiment data and entered information appropriately and send it to the server. This format includes metadata about the user's emotional state.

[0776] Step 3:

[0777] The server stores the received data and sentiment metadata in a database and activates knowledge processing units specialized for each area of ​​expertise. The database is organized to facilitate reference in subsequent analysis processes.

[0778] Step 4:

[0779] The server distributes the corresponding information, including emotional data, to each knowledge processing unit. This allows each unit to begin data analysis based on its own specialized knowledge.

[0780] Step 5:

[0781] The knowledge processing device performs individual analysis based on the sorted data, while also incorporating the user's emotional state, as indicated by the emotion engine, into the analysis process. This enables flexible recognition decisions that are tailored to the user's emotions.

[0782] Step 6:

[0783] The server integrates the analysis results collected from the knowledge processing units and facilitates discussions between them. These discussions include analysis results incorporating emotional data.

[0784] Step 7:

[0785] The knowledge processing system considers emotional data during discussions and mutually proposes the most suitable care needs assessment level. Emotional data is a particularly influential factor in determining the focus of support and the response strategy.

[0786] Step 8:

[0787] The server generates a report based on the final decision obtained through deliberation. This report also includes special considerations based on the user's feelings.

[0788] Step 9:

[0789] The server notifies the user of the generated report via the terminal and accepts feedback. This feedback is reflected in the database to help improve the sentiment data.

[0790] Step 10:

[0791] The server utilizes feedback and emotional data from the emotion engine to continuously optimize the system. This will enable more accurate care needs assessments in the next review process.

[0792] (Example 2)

[0793] 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".

[0794] Traditional care needs assessment processes prioritize mechanical judgments based on detailed information and medical data of the care recipient, often failing to adequately consider the user's emotions and emotional needs. As a result, assessment results may not reflect the user's true needs, potentially leading to inadequate support.

[0795] 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.

[0796] In this invention, the server includes means for activating multiple knowledge processing devices specialized in various fields within the information processing device, means for accumulating the results of analyses performed individually by the knowledge processing devices, means for incorporating an emotion engine that analyzes the user's emotional state in real time, and means for the knowledge processing devices to consult with each other based on the analysis results and emotion data to make a final decision. This makes it possible to capture the emotional needs of the user and provide care assessment results that are more humane and insightful.

[0797] An "information processing device" is a general term for hardware and software used for data collection, analysis, and management.

[0798] A "knowledge processing device" is a device that analyzes data and makes decisions based on specific expertise.

[0799] An "emotion engine" is a system that analyzes a user's emotional state in real time based on their input patterns and interactions, and extracts the data from that analysis.

[0800] "Emotional data" refers to information analyzed by an emotion engine that indicates the user's emotional state.

[0801] "Consultation" is the process by which multiple knowledge processing devices bring together their analysis results to make a final decision.

[0802] "Judgment" refers to an evaluation or decision made based on various data, and is ultimately presented as the certification result for the user.

[0803] A description of embodiments for carrying out this invention will be given.

[0804] The user first enters detailed information about the person receiving care into the terminal. This includes the person's name, age, health status, and care needs. During this process, an emotion engine is built into the terminal, analyzing the user's input patterns and actions in real time to acquire emotional data. The emotion engine uses an algorithm that estimates the user's emotional state based on input speed and rhythm, as well as mouse movements.

[0805] The terminal transmits the acquired user details and emotional data to the server. The server, acting as an information processing device, activates multiple knowledge processing devices, each with its own area of ​​expertise. These knowledge processing devices specialize in various fields such as medicine, health, and nutrition, and perform detailed analysis in their respective fields based on the information stored in the database. The analysis takes into account the results of the emotional engine's analysis, allowing for flexibility in decision-making.

[0806] The server collects analysis results from each knowledge processing unit, discusses the data, and makes a final decision on the care needs assessment. Generative AI models are used in this process to improve the accuracy of the judgment by comparing it with past data. When a user expresses emotions such as "worry" or "anxiety," the system incorporates this emotional data and determines whether enhanced care support is necessary.

[0807] For example, if a user inputs information about the health status of a person receiving care and indicates "anxiety," the knowledge processing unit receives this emotional data and analyzes it to increase the need for medical support. Based on the final assessment results, a report is generated and provided to the user via the terminal. The emotional data received as feedback is also reflected in the database, leading to further improvements to the system.

[0808] A specific example of a prompt would be, "If the user expresses feelings of 'anxiety' while entering care records, how should the care plan be adjusted?" This allows the system to suggest specific support strategies.

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] The user uses a terminal to input detailed information about the person being cared for. This information includes the person's name, age, health status, and care needs. As the user inputs information, the emotion engine analyzes the interaction and estimates the emotional state from patterns such as input speed and mouse movements. At this stage, two types of data are generated: input data and emotion data.

[0812] Step 2:

[0813] The device sends detailed information obtained from the user and sentiment data generated by the sentiment engine to the server. This transmission takes place in a secure environment, and the information is encrypted. The server receives this data, stores it in a database, and categorizes it. Specifically, a unique identifier is assigned to each input data to prepare for future analysis.

[0814] Step 3:

[0815] The server activates multiple knowledge processing units, each specialized in a particular field, based on the information stored in the database. These include categories such as medicine, health, and nutrition. The knowledge processing units retrieve necessary information from the database and perform expert analysis. They use emotional data, among other factors, to determine the appropriate care support. Each processing unit uses its own algorithm to gain insights and generate analysis results.

[0816] Step 4:

[0817] The server aggregates the analysis results from each knowledge processing unit and conducts discussions based on them. The knowledge processing units collaborate with each other, using generative AI models to compare and examine past data with current analysis results. In this process, the user's emotional state is considered as an important factor in decision-making. For example, if a user expresses "anxiety," that emotion may be suggested as a reason for needing enhanced medical support.

[0818] Step 5:

[0819] The server generates a report based on the final decision made after deliberation. This report includes detailed analysis results and recommended support, providing useful information for the user. The generated report is delivered to the user via their terminal. The output here includes the care assessment result and a specific support plan.

[0820] Step 6:

[0821] Users receive reports and provide feedback on their contents. This feedback, along with sentiment data, is returned to the server and becomes an important factor in updating the database. The server uses this data to improve the system and as training data to improve the accuracy of future prompt generation and sentiment analysis.

[0822] (Application Example 2)

[0823] 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".

[0824] A challenge with the conventional care needs assessment process is that it fails to consider the user's emotional state, resulting in mechanical and inflexible outcomes. Furthermore, it lacks consideration for the psychological burden on caregivers, making it difficult to provide support that meets individual needs.

[0825] 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.

[0826] In this invention, the server includes means for combining an emotion recognition engine that analyzes the user's emotional state in real time, means for activating multiple knowledge processing devices specialized in various fields, and means for proposing additional support to the user based on the emotional data. This enables flexible and humane care assessment while taking the user's emotions into consideration.

[0827] An "information processing device" is a device or system that receives information input from a user and performs analysis and data storage.

[0828] An "emotion recognition engine" is a technology that analyzes user input and actions and evaluates the underlying emotional state in real time.

[0829] A "knowledge processing device" is a device or program that specializes in a particular field of expertise and is used to analyze related data.

[0830] "Analysis results" refer to the final output of the analysis performed by the knowledge processing device based on the input data.

[0831] "Consultation" is a process in which multiple knowledge processing devices discuss and make integrated decisions based on their analysis results.

[0832] "Emotional data" refers to information about a user's emotional state, analyzed by an emotion recognition engine.

[0833] "Additional support" refers to supplementary care-related services and support suggested based on the user's emotional data and circumstances.

[0834] A "database" is a system that stores analysis results and emotional data so that they can be used later.

[0835] A "report" is a document containing the final judgment results and is information submitted to an external device.

[0836] "External devices" refer to devices or systems used to receive the results of a report.

[0837] The system implementing this invention is configured to improve the process of assessing long-term care needs by linking an information processing device, multiple knowledge processing devices, and an emotion recognition engine. The following hardware and software are used for implementation.

[0838] The information processing device functions as a terminal for users to input detailed information for care needs assessment. This terminal is equipped with emotion recognition software and uses an emotion engine to acquire user emotion data in real time from input and operations. Specifically, it utilizes emotion recognition APIs (e.g., Amazon Rekognition and Microsoft Azure Face API).

[0839] The device sends acquired information and sentiment data to the server. The server stores this data in a database (e.g., MySQL, Firebase) and activates knowledge processing units specialized for each area of ​​expertise. The knowledge processing units perform analysis that also takes sentiment data into consideration during the analysis process.

[0840] For example, if the emotion engine indicates that a user is experiencing anxiety, the system, through its knowledge processing unit, determines that additional care support is needed based on that anxiety. Based on this determination, it becomes possible to propose additional support to the user.

[0841] The final assessment results are compiled by the server and generated as a report. This report is then notified to relevant external organizations. Throughout the evaluation process, the server continuously updates its database and works to improve the system.

[0842] As a specific example, when conducting an online care needs assessment, if the emotion recognition engine detects that a user has expressed "worry" or "anxiety," the system will immediately respond by determining whether mental health care is needed and presenting options for consulting with a specialist.

[0843] An example of a prompt using a generative AI model is: "If the user shows anxiety while inputting emotional data through the app, what kind of care plan should you suggest? Based on the emotional data provided by the emotion engine, please provide the suggested care plan in the following format."

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The terminal receives input from the user of information necessary for care needs assessment. The data entered by the user includes basic personal information and details regarding the need for care. At this time, the emotion recognition engine installed in the terminal analyzes the user's facial expressions and input patterns in real time and outputs their emotional state as numerical data. The input to the emotion recognition engine is the user's facial expression data, and the output is a report of their emotional state.

[0847] Step 2:

[0848] The terminal sends the acquired user data and emotional state report to the server. The server stores the received data in a database and organizes it. The input data is user information and emotional state, and the output is an update to the database in an organized format.

[0849] Step 3:

[0850] The server activates knowledge processing units specialized for each area of ​​expertise. These units retrieve necessary information from the database and begin analysis that takes emotional data into account. An analysis algorithm is then applied based on the emotional data. The input consists of user information and emotional states from the database, and the output is an individually analyzed expert evaluation.

[0851] Step 4:

[0852] The server aggregates the analysis results obtained from the knowledge processing devices and uses this information to conduct discussions among the devices. In these discussions, each analysis result and emotional data are considered as important factors, and the final care needs assessment result is derived. The input consists of multiple analysis results and emotional data, and the output is a provisional care needs assessment decision after the discussions.

[0853] Step 5:

[0854] The server generates a report based on the final assessment results and sentiment data, and notifies an external device of the results. This report is formatted into a specific format and communicated to care service providers and other relevant organizations. Its input is the final assessment results and sentiment data, and its output is document data in the form of a report.

[0855] Step 6:

[0856] The server incorporates data acquired through each process and user feedback into a database, using it to improve the system. In particular, it contributes to improving the accuracy of the emotion recognition engine and optimizing the algorithms of the knowledge processing unit. The input is feedback data, and the output is improved system performance.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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."

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field,

[0881] The aforementioned knowledge processing device has means for accumulating the results of individual analyses,

[0882] A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and make a final decision,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising means for receiving feedback from each knowledge processing device during consultations between knowledge processing devices and updating the database.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising means for generating a report based on the final determination and notifying an external device of the result.

[0888] "Example 1"

[0889] (Claim 1)

[0890] An input device for users to enter information,

[0891] Means for converting the input information into a predetermined format and transmitting it to an information processing device,

[0892] In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field,

[0893] The aforementioned knowledge processing device includes means for analyzing data and constructing hypotheses based on specialized knowledge,

[0894] The aforementioned knowledge processing device has means for accumulating the results of individual analyses,

[0895] A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and make the optimal decision through the exchange of opinions,

[0896] A means for generating a report based on the aforementioned optimal determination and notifying an external device of the results,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, further comprising means for receiving feedback from each knowledge processing device during consultations between knowledge processing devices and updating an information storage device.

[0900] (Claim 3)

[0901] The system according to claim 1, further comprising means for continuously updating data to improve the system's analysis accuracy based on user feedback.

[0902] "Application Example 1"

[0903] (Claim 1)

[0904] In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field,

[0905] The aforementioned knowledge processing device has means for accumulating the results of individual analyses,

[0906] A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and make a final decision,

[0907] A means of collecting user input data from a terminal and sending it to a database in an organized format,

[0908] A means of displaying the received analysis results on the user's information terminal,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising means for receiving feedback from each knowledge processing device during consultations between knowledge processing devices and updating the database.

[0912] (Claim 3)

[0913] The system according to claim 1, further comprising means for generating a report based on the final determination and notifying an external device of the result.

[0914] "Example 2 of combining an emotion engine"

[0915] (Claim 1)

[0916] In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field,

[0917] The aforementioned knowledge processing device has means for accumulating the results of individual analyses,

[0918] A means of incorporating an emotion engine that analyzes the user's emotional state in real time,

[0919] A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and emotional data to make a final decision,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, comprising means for receiving feedback from each knowledge processing device during consultations between knowledge processing devices and updating a database containing emotion data.

[0923] (Claim 3)

[0924] The system according to claim 1, comprising means for generating a report based on the final judgment and notifying an external device of the results, and means for utilizing the analysis results from the emotion engine as feedback.

[0925] "Application example 2 when combining with an emotional engine"

[0926] (Claim 1)

[0927] An information processing device that combines an emotion recognition engine that analyzes the user's emotional state in real time,

[0928] A means of activating multiple knowledge processing devices specialized in each specialized field,

[0929] The aforementioned knowledge processing device has means for accumulating the results of individual analyses,

[0930] A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and emotional data to make a final decision,

[0931] A means of suggesting additional support to users based on emotional data,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising means for receiving feedback from each knowledge processing device and emotion data from an emotion recognition engine during consultations between knowledge processing devices, and updating the database.

[0935] (Claim 3)

[0936] The system according to claim 1, further comprising means for generating a report based on the final judgment and sentiment data and notifying an external device of the results. [Explanation of Symbols]

[0937] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In an information processing device, means for activating multiple knowledge processing devices specialized in each specialized field, The aforementioned knowledge processing device has means for accumulating the results of individual analyses, A means by which knowledge processing devices consult with each other based on the aforementioned analysis results and make a final decision, A means of collecting user input data from a terminal and sending it to a database in an organized format, A means of displaying the received analysis results on the user's information terminal, A system that includes this.

2. The system according to claim 1, comprising means for receiving feedback from each knowledge processing device during consultations between knowledge processing devices and updating the database.

3. The system according to claim 1, further comprising means for generating a report based on the final determination and notifying an external device of the result.

Images