System

Abstract

An object of a system according to an embodiment is to make care manager work more efficient and provide appropriate care plans and nursing services.SOLUTION: A system includes an interview agent part, an estimation part, a planning part, and a mediation part. The interview acting part acts for an interview with the family of the person to be cared or the person himself / herself. The estimation part estimates a nursing care state and a mental state from the contents of the conversation substituted by the interview substitution part. The planning unit plans an appropriate care plan or nursing care service based on the information estimated by the estimation unit. The mediation unit collects information on the care plan and the nursing care service planned by the planning unit, the availability of the nursing care facility, and the support business of the local government, and utilizes the information for proposal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of placing a heavy workload on care managers, making it difficult to efficiently develop care plans and provide nursing care services.

[0005] The system according to the embodiment aims to improve the efficiency of the work of care managers and provide appropriate care plans and nursing care services. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview proxy unit, an estimation unit, a planning unit, and an intermediary unit. The interview proxy unit conducts interviews with the care recipient's family and the care recipient themselves. The estimation unit estimates the care status and mental state from the content of the conversation conducted by the interview proxy unit. The planning unit plans appropriate care plans and nursing care services based on the information estimated by the estimation unit. The intermediary unit collects information on the availability of nursing care facilities and support programs offered by local governments, and uses this information to make proposals for the care plans and nursing care services planned by the planning unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the work of care managers and provide appropriate care plans and nursing care services. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The care manager support system according to an embodiment of the present invention is a system for supporting the work of care managers. This system allows care managers to complete tasks such as formulating care plans, mediating nursing care services, providing consultations to care recipients, and reporting to local governments simply by approving the output content of the AI. As a result, the care manager support system streamlines the work of care managers and enables them to provide appropriate support to all seniors.

[0029] A care manager support system according to an embodiment includes an interview proxy unit, an estimation unit, a planning unit, and an intermediary unit. The interview proxy unit conducts interviews with the care recipient's family or the care recipient himself / herself. For example, the generation AI conducts conversations with the care recipient's family or the care recipient himself / herself during interviews. The generation AI uses speech recognition technology to understand the content of the conversation and estimate the care status and mental state. For example, it asks questions such as, "How are you feeling lately?" and analyzes the other person's response. The estimation unit estimates the care status and mental state from the content of the conversation conducted by the interview proxy unit. For example, the generation AI analyzes the content of the conversation and estimates the care status and mental state. The planning unit plans appropriate care plans and care services based on the information estimated by the estimation unit. For example, the generation AI plans care plans based on the estimated information. The intermediary unit collects information on the availability of nursing care facilities and support programs offered by local governments and uses it to propose care plans and care services based on the care plans and care services proposed by the planning unit. For example, the generative AI constantly collects information on the availability of nursing care facilities and local government support programs, and uses this information to make proposals. This allows the care manager support system to streamline the work of care managers and provide appropriate support to all seniors.

[0030] The interview proxy unit can automatically record the interview content and generate a summary so that the care manager can check it later. In the interview proxy unit, for example, the generation AI automatically records the interview content and converts the audio data into text. It then extracts important points and generates a summary so that the care manager can check it later. The generation AI can also automatically record the interview content and generate a summary so that the care manager can check it later. For example, it can extract important points from the interview and generate a summary. This makes it easier for the care manager to check the interview content later.

[0031] The interview agent unit analyzes the physical movements and facial expressions of the care recipient, which can be used to estimate the care status. In the interview agent unit, for example, the generation AI uses a camera to analyze the physical movements of the care recipient and estimate the care status. For example, it observes the stability of walking and hand tremors and reflects this in the care plan. The generation AI also analyzes the facial expressions of the care recipient to help estimate the care status. For example, it analyzes facial expressions such as smiling and anger and reflects this in the care plan. This allows for a more accurate understanding of the care recipient's physical condition.

[0032] The estimation unit can refer to the care recipient's past medical records and nursing care history to propose a more accurate care plan. For example, the generation AI automatically obtains the care recipient's past medical records and refers to them during the interview. For example, the current health condition is evaluated based on past medical history and treatment history. The generation AI also refers to the care recipient's nursing care history to propose a more accurate care plan. For example, the current nursing care needs are evaluated based on past care plans and nursing care service usage history. This makes it possible to propose a more accurate care plan based on past data.

[0033] The interview proxy unit can also ask questions of the care recipient's family and develop a care plan that reflects the family's opinions and wishes. For example, the interview proxy unit asks questions of the care recipient's family during an interview, collecting the family's opinions and wishes. For example, it asks questions such as, "What kind of nursing care services would you like?" The generation AI also develops a care plan that reflects the family's opinions and wishes. For example, it adjusts the type and frequency of nursing care services based on the family's wishes. This makes it possible to develop a care plan that reflects the family's opinions and wishes.

[0034] The consistency check unit can automatically check the consistency of data when a report is created and correct errors. For example, the consistency check unit automatically checks the consistency of data when the generation AI creates a report and detects errors. For example, it detects inconsistencies or omissions in input data and suggests corrections. The generation AI also automatically corrects errors. For example, it corrects erroneous data to maintain data consistency. This improves the accuracy of the report.

[0035] The comparison unit can compare with past reports to clarify progress and areas for improvement. In the comparison unit, for example, the generation AI compares past reports with the current report to evaluate progress. For example, the report will include the degree of achievement of the care plan and areas for improvement. The generation AI can also compare with past reports to clarify progress and areas for improvement. For example, it can compare past reports with the current report to identify areas for improvement. This makes it possible to clearly understand progress and areas for improvement.

[0036] The reporting agency can compare reports from other care managers and incorporate best practices. For example, the generation AI in the reporting agency can refer to the reports of other care managers and incorporate best practices. For example, it can refer to the format and content of excellent reports. The generation AI can also compare reports from other care managers and incorporate best practices. For example, it can improve reports based on success stories. This improves the quality of reports.

[0037] The reporting agent unit can automatically reflect the latest guidelines and regulations of the local government. For example, the generation AI automatically obtains the latest guidelines and regulations of the local government and reflects them in the report. For example, it uses a report format based on the new regulations. The generation AI also automatically reflects the latest guidelines and regulations of the local government. For example, it creates a report based on the new guidelines. This ensures that the report complies with the latest guidelines and regulations.

[0038] The recording unit can automatically record the contents of the consultation and generate a summary so that the care manager can check it later. In the recording unit, for example, the generation AI automatically records the contents of the consultation and converts the audio data into text. It then extracts important points and generates a summary so that the care manager can check it later. The generation AI can also automatically record the contents of the consultation and generate a summary so that the care manager can check it later. For example, it can extract important points of the consultation and generate a summary. This makes it easier for the care manager to check the contents of the consultation later.

[0039] The priority processing unit can prioritize and handle consultations with a high level of urgency based on the content of the consultation. In the priority processing unit, for example, the generation AI analyzes the content of the consultation and automatically identifies consultations with a high level of urgency. For example, it prioritizes consultations regarding sudden changes in health status or urgent care needs. The generation AI also prioritizes consultations with a high level of urgency based on the content of the consultation. For example, it executes a protocol for handling consultations with a high level of urgency on a priority basis. This allows for a prompt response to consultations with a high level of urgency.

[0040] The consultation agent unit can provide the optimal answer by referring to similar consultation cases from the past. In the consultation agent unit, for example, the generation AI analyzes the content of the consultation and searches a database for similar consultation cases from the past. For example, it refers to past consultation cases regarding similar health issues or care needs. The generation AI also refers to similar consultation cases from the past to provide the optimal answer. For example, it provides the optimal answer based on past success cases. This makes it possible to provide the optimal answer based on past cases.

[0041] The consultation agent unit can automatically contact an expert as needed. For example, the generation AI analyzes the content of the consultation and automatically contacts an expert as needed. For example, it transfers the content of the consultation to a doctor or psychologist and asks for professional advice. The generation AI also automatically contacts an expert as needed. For example, it contacts a doctor to respond to urgent medical needs. This allows expert advice to be obtained quickly.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The care manager support system also includes a health monitoring unit. The health monitoring unit monitors the health status of the care recipient in real time and can issue an alert if an abnormality is detected. For example, the generation AI constantly monitors the care recipient's heart rate and blood pressure, and notifies the care manager if an abnormal value is detected. The health monitoring unit can also analyze the care recipient's sleep patterns and evaluate their sleep quality. For example, the generation AI collects the care recipient's sleep data, evaluates their sleep quality, and suggests improvement measures if necessary. This allows for a more detailed understanding of the care recipient's health status and enables prompt response.

[0044] The care manager support system also has a reminder section. The reminder section can remind the care recipient and their family of important appointments and tasks. For example, the generation AI can remind the care recipient when it's time to take their medicine, urging them to remember. The reminder section can also remind the care recipient's family of regular health checkups and medical appointments. For example, the generation AI can notify the family of the next medical appointment date, urging them to remember to visit. This allows the care recipient and their family to remember to complete important appointments and tasks.

[0045] The care manager support system also includes an education unit. The education unit can provide care managers and families of those receiving care with knowledge and skills related to caregiving. For example, the generative AI can provide online courses and video tutorials on caregiving, allowing care managers and families to improve their caregiving skills. The education unit can also provide the latest information and research results on caregiving to families of those receiving care. For example, the generative AI can inform families of the latest caregiving techniques and research results, improving the quality of caregiving. This allows care managers and families to improve their knowledge and skills related to caregiving and provide higher quality care.

[0046] The care manager support system also includes a feedback unit. The feedback unit collects feedback from the care recipient and their family members and can use it to improve the care plan. For example, the generation AI periodically conducts surveys of the care recipient and their family members to collect their opinions and requests regarding the care plan. The feedback unit can also automatically adjust the care plan based on the collected feedback. For example, the generation AI analyzes the feedback and adjusts the content and frequency of the care plan. This makes it possible to provide a flexible care plan that reflects the opinions of the care recipient and their family members.

[0047] The care manager support system also includes a resource management unit. The resource management unit can manage the resources and supplies required for nursing care. For example, the generation AI manages the inventory of medical equipment and consumables required for nursing care and automatically orders them as needed. The resource management unit can also monitor the resource usage status of nursing care facilities in real time and allocate resources efficiently. For example, the generation AI monitors the availability of beds in nursing care facilities and reallocates resources as needed. This allows the resources required for nursing care to be managed efficiently and provided at the appropriate time.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The interview agent conducts interviews with the care recipient's family or the care recipient themselves. For example, the generation AI acts as a proxy during interviews with the care recipient's family or the care recipient themselves, and understands the content of the conversation using voice recognition technology. Step 2: The estimation unit estimates the care and mental state of the patient from the content of the conversation conducted by the interview proxy unit. For example, the generation AI analyzes the content of the conversation and estimates the care and mental state of the patient. Step 3: The planning unit creates an appropriate care plan and nursing care services based on the information inferred by the estimation unit. For example, the generation AI creates a care plan based on the inferred information. Step 4: The intermediary department collects information on the availability of nursing care facilities and support programs offered by local governments, and uses this information to make proposals for the care plans and nursing care services proposed by the planning department. For example, the generation AI constantly collects information on the availability of nursing care facilities and support programs offered by local governments, and uses this information to make proposals.

[0050] (Example 2) The care manager support system according to an embodiment of the present invention is a system for supporting the work of care managers. This system allows care managers to complete tasks such as formulating care plans, mediating nursing care services, providing consultations to care recipients, and reporting to local governments simply by approving the output content of the AI. As a result, the care manager support system streamlines the work of care managers and enables them to provide appropriate support to all seniors.

[0051] A care manager support system according to an embodiment includes an interview proxy unit, an estimation unit, a planning unit, and an intermediary unit. The interview proxy unit conducts interviews with the care recipient's family or the care recipient himself / herself. For example, the generation AI conducts conversations with the care recipient's family or the care recipient himself / herself during interviews. The generation AI uses speech recognition technology to understand the content of the conversation and estimate the care status and mental state. For example, it asks questions such as, "How are you feeling lately?" and analyzes the other person's response. The estimation unit estimates the care status and mental state from the content of the conversation conducted by the interview proxy unit. For example, the generation AI analyzes the content of the conversation and estimates the care status and mental state. The planning unit plans appropriate care plans and care services based on the information estimated by the estimation unit. For example, the generation AI plans care plans based on the estimated information. The intermediary unit collects information on the availability of nursing care facilities and support programs offered by local governments and uses it to propose care plans and care services based on the care plans and care services proposed by the planning unit. For example, the generative AI constantly collects information on the availability of nursing care facilities and local government support programs, and uses this information to make proposals. This allows the care manager support system to streamline the work of care managers and provide appropriate support to all seniors.

[0052] The interview agent unit uses the emotion estimation function to analyze the emotions of the care recipient in real time and ask questions or make suggestions based on those emotions. For example, the generation AI in the interview agent unit analyzes the care recipient's facial expressions and tone of voice during the interview and estimates their emotions in real time. For example, if the care recipient looks anxious, the AI ​​will ask a question such as, "Is there anything you're worried about?" The generation AI also uses the emotion estimation function to analyze the care recipient's emotions in real time and ask questions or make suggestions based on those emotions. For example, if the care recipient looks relaxed, the AI ​​will ask a question such as, "Tell me about your recent hobbies." This makes it possible to respond appropriately to the care recipient's emotions.

[0053] The interview proxy unit can automatically record the interview content and generate a summary so that the care manager can check it later. In the interview proxy unit, for example, the generation AI automatically records the interview content and converts the audio data into text. It then extracts important points and generates a summary so that the care manager can check it later. The generation AI can also automatically record the interview content and generate a summary so that the care manager can check it later. For example, it can extract important points from the interview and generate a summary. This makes it easier for the care manager to check the interview content later.

[0054] The interview agent unit analyzes the physical movements and facial expressions of the care recipient, which can be used to estimate the care status. In the interview agent unit, for example, the generation AI uses a camera to analyze the physical movements of the care recipient and estimate the care status. For example, it observes the stability of walking and hand tremors and reflects this in the care plan. The generation AI also analyzes the facial expressions of the care recipient to help estimate the care status. For example, it analyzes facial expressions such as smiling and anger and reflects this in the care plan. This allows for a more accurate understanding of the care recipient's physical condition.

[0055] The estimation unit can refer to the care recipient's past medical records and nursing care history to propose a more accurate care plan. For example, the generation AI automatically obtains the care recipient's past medical records and refers to them during the interview. For example, the current health condition is evaluated based on past medical history and treatment history. The generation AI also refers to the care recipient's nursing care history to propose a more accurate care plan. For example, the current nursing care needs are evaluated based on past care plans and nursing care service usage history. This makes it possible to propose a more accurate care plan based on past data.

[0056] The interview proxy unit can also ask questions of the care recipient's family and develop a care plan that reflects the family's opinions and wishes. For example, the interview proxy unit asks questions of the care recipient's family during an interview, collecting the family's opinions and wishes. For example, it asks questions such as, "What kind of nursing care services would you like?" The generation AI also develops a care plan that reflects the family's opinions and wishes. For example, it adjusts the type and frequency of nursing care services based on the family's wishes. This makes it possible to develop a care plan that reflects the family's opinions and wishes.

[0057] The estimation unit can measure the stress level of the care recipient using the emotion estimation function and make suggestions for stress reduction. For example, the generation AI measures the stress level of the care recipient in real time during an interview. For example, it analyzes changes in heart rate and breathing to evaluate the degree of stress. The generation AI also makes suggestions for stress reduction based on the stress level. For example, it provides advice on relaxation techniques and stress management. This makes it possible to understand the stress level of the care recipient and propose appropriate stress reduction measures.

[0058] The care manager support system includes a reporting agent unit, an emotion estimation unit, a consistency check unit, and a comparison unit. The reporting agent unit acts as a care manager's reporting agent to the local government. For example, when a report on the progress of a care plan or the use of nursing care services needs to be reported, the generation AI automatically collects data and creates a report. The emotion estimation unit reflects the emotional state of the care recipient in the report created by the reporting agent unit. For example, the generation AI analyzes the emotional state of the care recipient in real time and reflects the results in the report. The consistency check unit checks the consistency of the data in the report created by the reporting agent unit. For example, the generation AI automatically checks the consistency of the data when creating the report and detects errors. The comparison unit compares the report created by the reporting agent unit with past reports. For example, the generation AI compares past reports with the current report to evaluate progress and areas for improvement. This enables the care manager support system to improve the accuracy and comprehensiveness of reports.

[0059] The consistency check unit can automatically check the consistency of data when a report is created and correct errors. For example, the consistency check unit automatically checks the consistency of data when the generation AI creates a report and detects errors. For example, it detects inconsistencies or omissions in input data and suggests corrections. The generation AI also automatically corrects errors. For example, it corrects erroneous data to maintain data consistency. This improves the accuracy of the report.

[0060] The comparison unit can compare with past reports to clarify progress and areas for improvement. In the comparison unit, for example, the generation AI compares past reports with the current report to evaluate progress. For example, the report will include the degree of achievement of the care plan and areas for improvement. The generation AI can also compare with past reports to clarify progress and areas for improvement. For example, it can compare past reports with the current report to identify areas for improvement. This makes it possible to clearly understand progress and areas for improvement.

[0061] The reporting agency can compare reports from other care managers and incorporate best practices. For example, the generation AI in the reporting agency can refer to the reports of other care managers and incorporate best practices. For example, it can refer to the format and content of excellent reports. The generation AI can also compare reports from other care managers and incorporate best practices. For example, it can improve reports based on success stories. This improves the quality of reports.

[0062] The reporting agent unit can automatically reflect the latest guidelines and regulations of the local government. For example, the generation AI automatically obtains the latest guidelines and regulations of the local government and reflects them in the report. For example, it uses a report format based on the new regulations. The generation AI also automatically reflects the latest guidelines and regulations of the local government. For example, it creates a report based on the new guidelines. This ensures that the report complies with the latest guidelines and regulations.

[0063] The emotion estimation unit also reflects the emotional state of the care recipient's family, enabling a more comprehensive report. For example, the generation AI analyzes the emotional state of the care recipient's family and reflects the results in the report. For example, the report may include the anxieties and hopes felt by the family. The generation AI also reflects the emotional state of the care recipient's family to provide a more comprehensive report. For example, it creates a report based on the emotional state of the family. This makes it possible to provide a comprehensive report that also takes into account the emotional state of the family.

[0064] The recording unit can automatically record the contents of the consultation and generate a summary so that the care manager can check it later. In the recording unit, for example, the generation AI automatically records the contents of the consultation and converts the audio data into text. It then extracts important points and generates a summary so that the care manager can check it later. The generation AI can also automatically record the contents of the consultation and generate a summary so that the care manager can check it later. For example, it can extract important points of the consultation and generate a summary. This makes it easier for the care manager to check the contents of the consultation later.

[0065] The priority processing unit can prioritize and handle consultations with a high level of urgency based on the content of the consultation. In the priority processing unit, for example, the generation AI analyzes the content of the consultation and automatically identifies consultations with a high level of urgency. For example, it prioritizes consultations regarding sudden changes in health status or urgent care needs. The generation AI also prioritizes consultations with a high level of urgency based on the content of the consultation. For example, it executes a protocol for handling consultations with a high level of urgency on a priority basis. This allows for a prompt response to consultations with a high level of urgency.

[0066] The consultation agent unit can provide the optimal answer by referring to similar consultation cases from the past. In the consultation agent unit, for example, the generation AI analyzes the content of the consultation and searches a database for similar consultation cases from the past. For example, it refers to past consultation cases regarding similar health issues or care needs. The generation AI also refers to similar consultation cases from the past to provide the optimal answer. For example, it provides the optimal answer based on past success cases. This makes it possible to provide the optimal answer based on past cases.

[0067] The consultation agent unit can automatically contact an expert as needed. For example, the generation AI analyzes the content of the consultation and automatically contacts an expert as needed. For example, it transfers the content of the consultation to a doctor or psychologist and asks for professional advice. The generation AI also automatically contacts an expert as needed. For example, it contacts a doctor to respond to urgent medical needs. This allows expert advice to be obtained quickly.

[0068] The emotion estimation unit can monitor the emotional state of the client in real time and respond according to the emotion. For example, the generation AI in the emotion estimation unit monitors the emotional state of the client in real time and responds according to the emotion. For example, if the client is feeling anxious, the AI ​​will ask a question such as, "What are you worried about?" The generation AI can also monitor the emotional state of the client in real time and respond according to the emotion. For example, if the client is relaxed, the AI ​​will ask a question such as, "Tell me about your recent hobbies." This makes it possible to respond appropriately according to the client's emotional state.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The care manager support system also includes a health monitoring unit. The health monitoring unit monitors the health status of the care recipient in real time and can issue an alert if an abnormality is detected. For example, the generation AI constantly monitors the care recipient's heart rate and blood pressure, and notifies the care manager if an abnormal value is detected. The health monitoring unit can also analyze the care recipient's sleep patterns and evaluate their sleep quality. For example, the generation AI collects the care recipient's sleep data, evaluates their sleep quality, and suggests improvement measures if necessary. This allows for a more detailed understanding of the care recipient's health status and enables prompt response.

[0071] The care manager support system also has a reminder section. The reminder section can remind the care recipient and their family of important appointments and tasks. For example, the generation AI can remind the care recipient when it's time to take their medicine, urging them to remember. The reminder section can also remind the care recipient's family of regular health checkups and medical appointments. For example, the generation AI can notify the family of the next medical appointment date, urging them to remember to visit. This allows the care recipient and their family to remember to complete important appointments and tasks.

[0072] The care manager support system also includes an education unit. The education unit can provide care managers and families of those receiving care with knowledge and skills related to caregiving. For example, the generative AI can provide online courses and video tutorials on caregiving, allowing care managers and families to improve their caregiving skills. The education unit can also provide the latest information and research results on caregiving to families of those receiving care. For example, the generative AI can inform families of the latest caregiving techniques and research results, improving the quality of caregiving. This allows care managers and families to improve their knowledge and skills related to caregiving and provide higher quality care.

[0073] The care manager support system also includes a feedback unit. The feedback unit collects feedback from the care recipient and their family members and can use it to improve the care plan. For example, the generation AI periodically conducts surveys of the care recipient and their family members to collect their opinions and requests regarding the care plan. The feedback unit can also automatically adjust the care plan based on the collected feedback. For example, the generation AI analyzes the feedback and adjusts the content and frequency of the care plan. This makes it possible to provide a flexible care plan that reflects the opinions of the care recipient and their family members.

[0074] The care manager support system also includes a resource management unit. The resource management unit can manage the resources and supplies required for nursing care. For example, the generation AI manages the inventory of medical equipment and consumables required for nursing care and automatically orders them as needed. The resource management unit can also monitor the resource usage status of nursing care facilities in real time and allocate resources efficiently. For example, the generation AI monitors the availability of beds in nursing care facilities and reallocates resources as needed. This allows the resources required for nursing care to be managed efficiently and provided at the appropriate time.

[0075] The estimation unit can measure the happiness level of the care recipient using the emotion estimation function and make suggestions to improve that happiness level. For example, the generation AI analyzes the care recipient's facial expressions and tone of voice to evaluate their happiness level in real time. The generation AI also makes suggestions to improve happiness level based on the happiness level. For example, it suggests hobbies and recreational activities to improve the care recipient's happiness level. This makes it possible to understand the care recipient's happiness level and propose appropriate measures to improve their happiness.

[0076] The interview agent unit can use the emotion estimation function to analyze the emotions of the care recipient in real time and provide music and videos that correspond to those emotions. For example, the generation AI analyzes the care recipient's facial expressions and tone of voice to estimate their emotions in real time. The generation AI also uses the emotion estimation function to provide music and videos that correspond to the care recipient's emotions. For example, if the care recipient is relaxed, it will provide music that has a relaxing effect. This makes it possible to provide appropriate entertainment that corresponds to the care recipient's emotions.

[0077] The estimation unit can measure the care recipient's sense of loneliness using the emotion estimation function and make suggestions to reduce that sense of loneliness. For example, the generation AI analyzes the care recipient's facial expressions and tone of voice to assess their sense of loneliness in real time. The generation AI also makes suggestions to reduce loneliness based on the sense of loneliness. For example, it suggests social activities or community participation to reduce the care recipient's sense of loneliness. This makes it possible to understand the care recipient's sense of loneliness and propose appropriate measures to reduce loneliness.

[0078] The interview agent unit uses the emotion estimation function to analyze the emotions of the care recipient in real time and can suggest relaxation methods according to the emotions. For example, the generation AI analyzes the care recipient's facial expressions and tone of voice to estimate their emotions in real time. The generation AI also uses the emotion estimation function to suggest relaxation methods according to the care recipient's emotions. For example, if the care recipient is feeling stressed, it can suggest deep breathing or meditation. This makes it possible to provide appropriate relaxation methods according to the care recipient's emotions.

[0079] The estimation unit can measure the care recipient's motivation using the emotion estimation function and make suggestions to improve it. For example, the generation AI analyzes the care recipient's facial expressions and tone of voice to evaluate their motivation in real time. The generation AI also makes suggestions to improve motivation based on their motivation. For example, it suggests goal setting and activities that will give them a sense of accomplishment, thereby improving the care recipient's motivation. This makes it possible to understand the care recipient's motivation and propose appropriate measures to improve their motivation.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The interview agent conducts interviews with the care recipient's family or the care recipient themselves. For example, the generation AI acts as a proxy during interviews with the care recipient's family or the care recipient themselves, and understands the content of the conversation using voice recognition technology. Step 2: The estimation unit estimates the care and mental state of the patient from the content of the conversation conducted by the interview proxy unit. For example, the generation AI analyzes the content of the conversation and estimates the care and mental state of the patient. Step 3: The planning unit creates an appropriate care plan and nursing care services based on the information inferred by the estimation unit. For example, the generation AI creates a care plan based on the inferred information. Step 4: The intermediary department collects information on the availability of nursing care facilities and support programs offered by local governments, and uses this information to make proposals for the care plans and nursing care services proposed by the planning department. For example, the generation AI constantly collects information on the availability of nursing care facilities and support programs offered by local governments, and uses this information to make proposals.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The interview agency handles interviews with the families and individuals receiving care, an estimation unit that estimates a care state and a mental state from the content of the conversation performed by the interview proxy unit; a planning unit that plans appropriate care plans and nursing care services based on the information estimated by the estimation unit; and an intermediary department that collects information on the availability of nursing care facilities and support programs of local governments and uses the information to make proposals based on the care plans and nursing care services proposed by the planning department. A system characterized by:

2. The interview agency department: Analyzing the emotions of the care recipient in real time and asking questions or making suggestions according to the emotions 2. The system of claim 1.

3. The estimation unit By referencing the care recipient's past medical records and nursing history, a more accurate care plan is proposed.

2. The system of claim 1.

4. A reporting agency that handles reporting duties to local governments on behalf of care managers; an emotion estimation unit that reflects the emotional state of the care recipient in the report created by the reporting agent unit; a consistency check unit that checks the consistency of data in the report created by the reporting agent unit; a comparison unit that compares the report created by the reporting agent unit with past reports. A system characterized by:

5. When the care manager is not available, the consultation department will respond to inquiries about caregiving 24 hours a day, 365 days a year through a dedicated app. an emotion estimation unit that grasps the emotional state of the client from the consultation content accepted by the consultation agent unit; a recording unit that automatically records the consultation content accepted by the consultation agent unit; a priority processing unit that evaluates the urgency of the consultation content accepted by the consultation proxy unit and handles consultations with a high degree of urgency on a priority basis. A system characterized by:

6. The estimation unit Measuring the care recipient's stress level and making stress reduction suggestions based on the stress level.

2. The system of claim 1.

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