System

Abstract

An object of a system according to an embodiment is to efficiently create an instruction document suitable for each stoma patient.SOLUTION: A system according to an embodiment includes an information collection unit and a document generation unit. The information collection unit collects information on stomal features, symptoms, and characteristics of patients using the generated AI. The document generation unit generates an explanation document based on the information collected by the information collection unit.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 burden on nurses when creating explanatory documents to accommodate the different self-management methods for each stoma patient.

[0005] The system according to the embodiment aims to efficiently create an explanatory document suitable for each stoma patient. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit and a document generation unit. The information collection unit uses a generation AI to collect information about the characteristics, symptoms, and personality of the patient's stoma. The document generation unit generates an explanatory document based on the information collected by the information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create an explanatory document suitable for each stoma patient. [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 explanatory document generation system according to an embodiment of the present invention uses a generation AI to create explanatory documents that reflect the characteristics of a stoma and the symptoms and personality of the patient, reducing the burden on nurses and doctors associated with explanations and supporting patient self-management. As a result, the explanatory document generation system can reduce the burden on nurses and doctors associated with explanations and support patient self-management.

[0029] An explanatory document generation system according to an embodiment includes an information collection unit and a document generation unit. The information collection unit uses a generation AI to collect information about the characteristics, symptoms, and personality of a patient's stoma. For example, the information collection unit collects information about the stoma's location and cavity direction, as well as the patient's lifestyle and personality, from the patient's medical records and nurses' observation records. The information collection unit also analyzes the information based on prompts from the generation AI, including detailed information about the patient's stoma and the patient's individual needs. The document generation unit generates an explanatory document based on the information collected by the information collection unit. For example, the document generation unit automatically generates an explanatory document including stoma replacement procedures, excretion management methods, and stoma appliance usage methods based on the information collected by the generation AI. The document generation unit also generates an explanatory document optimized for the patient's stoma's characteristics, symptoms, and personality. This allows the explanatory document generation system to reduce the burden on nurses and doctors associated with providing explanations and support patient self-management.

[0030] The information collection unit can collect detailed information about the patient's living environment and identify factors that affect stoma management. For example, the information collection unit may conduct a home visit to observe the home situation and lifestyle habits in order to collect detailed information about the patient's living environment. This identifies factors that affect stoma management. The information collection unit can also investigate the patient's work environment and collect factors related to stoma management. For example, it may evaluate the stress level and working environment at work. Furthermore, the information collection unit can conduct a questionnaire about the patient's living environment and collect detailed information. For example, it may conduct a questionnaire that includes questions about eating habits and exercise habits. This makes it possible to manage stoma based on the patient's living environment.

[0031] The information collecting unit can monitor the patient's dietary and exercise habits and collect factors that may affect the stoma condition. For example, the information collecting unit uses a food record app to monitor the patient's dietary habits. The patient records their daily meals, and the app analyzes the nutrients and calories to identify factors that affect the stoma condition. The information collecting unit can also use a fitness tracker to monitor the patient's exercise habits. For example, the frequency and intensity of exercise can be recorded to collect data related to the stoma condition. Furthermore, the information collecting unit can conduct a questionnaire regarding the patient's dietary and exercise habits to collect detailed information. For example, a questionnaire including questions about specific foods and types of exercise can be conducted. This enables stoma management based on the patient's dietary and exercise habits.

[0032] The information collection unit can collect information on the patient's family and caregivers and provide information to strengthen the support system. For example, the information collection unit conducts a family interview to collect information on the patient's family and caregivers. During the interview, the family's support system and the caregiver's role are confirmed, and information necessary for stoma management is provided. The information collection unit can also conduct a questionnaire for the family and caregivers to collect detailed information. For example, a questionnaire including questions about family structure and caregiver experiences is conducted. Furthermore, the information collection unit can evaluate the support system for the family and caregivers and collect information to provide necessary support. For example, the information collection unit can introduce care methods and support services. This makes it possible to strengthen the support system based on information from the patient's family and caregivers.

[0033] The information collecting unit can integrate information about the patient's past medical history and other diseases to support comprehensive health management. The information collecting unit, for example, collects the patient's past medical history and integrates information related to stoma management. For example, it checks past surgical history and treatment history to support comprehensive health management. The information collecting unit can also collect information about the patient's other diseases and integrate data related to stoma management. For example, it can evaluate the presence or absence of chronic diseases and their treatment status. Furthermore, the information collecting unit can conduct a questionnaire about the patient's medical history and disease information to collect detailed information. For example, it conducts a questionnaire including questions about past treatment experience and current health status. This enables comprehensive health management based on the patient's past medical history and other diseases.

[0034] The document generation unit can generate multimedia explanatory documents tailored to the patient's learning style. For example, the document generation unit conducts a visual, auditory, and tactile questionnaire to evaluate the patient's learning style. This results in the generation of multimedia explanatory documents tailored to the patient's learning style. The document generation unit can also incorporate a multimedia generation algorithm into the generation AI to generate explanatory documents tailored to the patient's learning style. For example, it can provide explanatory documents including videos and illustrations to visual learners and audio guides to auditory learners. Furthermore, the document generation unit can build a system for generating multimedia explanatory documents tailored to the patient's learning style. For example, it can generate explanatory documents including interactive content to enable patients to learn tactilely. This makes it possible to generate multimedia explanatory documents tailored to the patient's learning style.

[0035] The document generation unit can collect patient feedback in real time and continuously improve the content of the instruction manual. For example, the document generation unit conducts an online survey to collect patient feedback on the instruction manual in real time. This allows for continuous improvement of the content of the instruction manual. The document generation unit can also revise and periodically update the content of the instruction manual based on patient feedback. For example, the document generation unit makes revisions based on the feedback to reflect the latest information. Furthermore, the document generation unit can also build a system for collecting patient feedback in real time and continuously improving the content of the instruction manual. For example, a feedback analysis algorithm can be incorporated to generate an instruction manual that reflects patient opinions. This allows for continuous improvement of the instruction manual based on patient feedback.

[0036] The document generation unit can automatically generate explanatory documents in multiple languages, achieving multilingual support. For example, the document generation unit incorporates a multilingual translation algorithm into the generation AI to automatically generate explanatory documents in multiple languages. For example, languages ​​such as English, French, and Chinese are supported. The document generation unit can also build a system to provide explanatory documents automatically generated by the generation AI in multiple languages. For example, the document generation unit can make the explanatory documents available to patients in their selected language through an online platform. Furthermore, the document generation unit can generate explanatory documents in multimedia format, including audio guides and subtitles, to provide explanatory documents automatically generated by the generation AI in multiple languages. This enables the automatic generation of explanatory documents in multiple languages, enabling multilingual support.

[0037] The document generation unit can generate explanatory documents as animations or interactive content to make them visually easier to understand. For example, the document generation unit incorporates an animation generation algorithm into the generation AI to generate explanatory documents in animation format. For example, the document generation unit can show a stoma replacement procedure using animation. The document generation unit can also generate explanatory documents that include interactive content. For example, the document generation unit can provide an interactive guide that includes clickable elements. Furthermore, the document generation unit can generate explanatory documents that include 3D animations or interactive content to make the explanatory documents automatically generated by the generation AI visually easier to understand. This makes it possible to generate explanatory documents as animations or interactive content that are visually easier to understand.

[0038] The information collection unit can build a system that optimizes nurses' work schedules and efficiently distributes explanatory documents automatically generated by the generation AI. For example, the information collection unit incorporates a scheduling algorithm into the generation AI to optimize nurses' work schedules. For example, the information collection unit generates an optimal schedule taking into account nurses' shifts and work content. The information collection unit can also build an email or printed distribution system to efficiently distribute the explanatory documents automatically generated by the generation AI. For example, the explanatory documents can be sent by email so that nurses can quickly obtain the information they need. Furthermore, the information collection unit can build a system that provides the explanatory documents through an online platform to efficiently distribute the explanatory documents automatically generated by the generation AI. This makes it possible to optimize nurses' work schedules and efficiently distribute explanatory documents.

[0039] The information collection unit can collect feedback from nurses and continuously improve the document generation algorithm of the generative AI. For example, the information collection unit conducts an online survey to collect feedback from nurses. This allows for continuous improvement of the document generation algorithm of the generative AI. The information collection unit can also modify and periodically update the document generation algorithm based on the nurses' feedback. For example, it makes modifications based on the feedback and reflects the latest information. Furthermore, the information collection unit can also build a system for collecting feedback from nurses and continuously improving the document generation algorithm of the generative AI. This makes it possible to continuously improve the document generation algorithm based on nurses' feedback.

[0040] The information collection unit can reduce the burden on nurses by having the generation AI automatically perform some of the explanations that nurses give to patients. For example, the information collection unit incorporates a natural language generation algorithm into the generation AI so that the generation AI can automatically perform some of the explanations that nurses give to patients. For example, it can automatically explain stoma replacement procedures and excretion management methods. The information collection unit can also use the explanatory documents automatically generated by the generation AI to replace some of the explanations that nurses give. For example, the information collection unit can provide the explanatory documents automatically generated by the generation AI to patients, thereby shortening the time it takes for nurses to give explanations. Furthermore, the information collection unit can also build a system to reduce the burden on nurses by using the explanatory documents automatically generated by the generation AI. This reduces the burden on nurses and enables the generation AI to automatically perform some of the explanations.

[0041] The information collection unit can introduce generative AI into a nurse education program to streamline the training of new nurses. For example, to introduce generative AI into a nurse education program, the information collection unit incorporates an educational content generation algorithm into the generative AI. For example, it automatically generates training materials related to stoma management. The information collection unit can also use the educational content automatically generated by the generative AI to streamline the training of new nurses. For example, it can provide online courses and on-site training. Furthermore, the information collection unit can use the educational content automatically generated by the generative AI to build a system for streamlining the nurse education program. This makes it possible to streamline the training of new nurses.

[0042] The information collection unit automates the nurses' work records, allowing the generation AI to manage the progress of their work in real time. For example, the information collection unit incorporates a work record generation algorithm into the generation AI to automate the nurses' work records. For example, it automatically records the work content performed by the nurses. The information collection unit can also incorporate a progress management algorithm so that the generation AI can manage the progress of their work in real time. For example, it manages the completion status of tasks and the frequency of progress reports. Furthermore, the information collection unit can also build a system for managing the progress of nurses' work in real time using the work records automatically generated by the generation AI. This makes it possible to automate the nurses' work records and manage the progress of their work in real time.

[0043] The document generation unit can evaluate the patient's level of understanding in real time and provide additional explanations as needed. For example, the document generation unit conducts online quizzes and tests to evaluate the patient's level of understanding in real time. This measures the patient's level of understanding and provides additional explanations as needed. The document generation unit can also provide detailed explanations and supplementary materials based on the patient's level of understanding. For example, if the level of understanding is low, more specific explanations are provided. Furthermore, the document generation unit can also build a system for evaluating the patient's level of understanding in real time and providing additional explanations as needed. This makes it possible to provide additional explanations according to the patient's level of understanding.

[0044] The document generation unit can track the patient's learning progress and provide an individualized learning plan. For example, the document generation unit develops an online learning platform to track the patient's learning progress. The platform records the patient's learning history and provides an individualized learning plan. The document generation unit can also provide a customized learning curriculum in accordance with the patient's learning progress using the learning plan automatically generated by the generative AI. For example, it creates a plan based on the patient's learning goals. Furthermore, the document generation unit can build a system for tracking the patient's learning progress and providing an individualized learning plan. This makes it possible to provide an individualized learning plan in accordance with the patient's learning progress.

[0045] The document generation unit can gamify the explanatory documents, allowing patients to learn while having fun. For example, the document generation unit incorporates a game design algorithm into the generation AI to gamify the explanatory documents. For example, it can provide quizzes or puzzles related to stoma management. The document generation unit can also introduce a point system or reward system to allow patients to learn while having fun. For example, points can be earned for each correct answer to a quiz, and a reward can be obtained when a certain number of points are reached. Furthermore, the document generation unit can build a system to gamify the explanatory documents, allowing patients to learn while having fun. This makes it possible to gamify the explanatory documents, allowing patients to learn while having fun.

[0046] The document generation unit can also provide explanatory documents to the patient's family and caregivers to promote comprehensive understanding. The document generation unit, for example, develops an online platform to provide explanatory documents to the patient's family and caregivers. The platform allows the family and caregivers to access the explanatory documents. The document generation unit can also generate explanatory documents that include explanations and guidelines for family and caregivers. For example, the document generation unit provides explanatory documents that include explanations for family members and creates guidelines for caregivers. Furthermore, the document generation unit can also build a system for providing explanatory documents to the patient's family and caregivers to promote comprehensive understanding. This makes it possible to provide explanatory documents to the patient's family and caregivers to promote comprehensive understanding.

[0047] The information collection unit can monitor changes in the patient's condition in real time and update the explanatory document as necessary. For example, the information collection unit uses a wearable device to monitor changes in the patient's condition in real time. This collects physiological data such as heart rate and body temperature, and the generation AI updates the explanatory document based on that data. The information collection unit can also monitor changes in the patient's vital signs and the progression of symptoms and revise the explanatory document as necessary. For example, it can conduct periodic reviews to reflect the latest information. Furthermore, the information collection unit can also build a system for monitoring changes in the patient's condition in real time and updating the explanatory document as necessary. This makes it possible to update the explanatory document in real time in response to changes in the patient's condition.

[0048] The information collection unit collects patient feedback, and the generation AI can automatically improve the support content. For example, the information collection unit conducts an online survey to collect patient feedback. This allows the generation AI to automatically improve the support content. The information collection unit can also modify and regularly update the support content based on patient feedback. For example, it makes modifications based on feedback to reflect the latest information. Furthermore, the information collection unit can also collect patient feedback and build a system for the generation AI to automatically improve the support content. This makes it possible to automatically improve the support content based on patient feedback.

[0049] The information collection unit allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm in order to provide continuous support. For example, the information collection unit uses a wearable device to analyze the patient's lifestyle rhythm. This allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm. The information collection unit can also analyze the patient's daily schedule and sleep patterns and adjust the timing of sending reminders. For example, it can send reminders for taking medication or for regular health checks. Furthermore, the information collection unit can also build a system that allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm. This makes it possible to send reminders that are tailored to the patient's lifestyle rhythm.

[0050] The information collection unit can form a patient community and promote information sharing with other patients. For example, the information collection unit develops an online platform to form a patient community. The platform enables patients to share information and support each other. The information collection unit can also provide a forum for patients to share their experiences and advice. For example, an online forum can be set up to exchange information about stoma management. Furthermore, the information collection unit can build a system to form a patient community and promote information sharing with other patients. This makes it possible to form a patient community and promote information sharing with other patients.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The information collection unit can collect information about the patient's hobbies and interests and reflect it in the explanatory document. For example, if the patient is interested in a particular sport or art, the explanatory document can be created using examples and analogies related to that hobby. The information collection unit can also collect information about the patient's favorite movies and music and generate an explanatory document including examples based on that information. Furthermore, the information collection unit can also conduct a questionnaire about the patient's hobbies and interests and collect detailed information. This makes it possible to generate an explanatory document based on the patient's hobbies and interests.

[0053] The information collection unit can monitor the sound environment in the patient's living environment and suggest a sound environment suitable for stoma management. For example, it can measure the noise level in the patient's living environment and provide advice on providing a quiet environment. The information collection unit can also investigate the use of music in the patient's living environment and suggest music that has a relaxing effect. Furthermore, the information collection unit can also conduct a questionnaire about the patient's living environment and collect detailed information about the sound environment. This makes it possible to suggest a sound environment based on the patient's living environment.

[0054] The information collection unit can monitor the patient's sleep patterns, as well as their dietary and exercise habits, and collect factors that affect stoma management. For example, it can record the patient's sleep time and sleep quality and collect data related to the state of the stoma. The information collection unit can also investigate the patient's sleep environment and suggest areas for improvement. Furthermore, the information collection unit can also conduct questionnaires about the patient's sleep habits and collect detailed information. This makes it possible to manage stomas based on the patient's sleep patterns.

[0055] The information collection unit can collect genetic information in addition to the patient's past medical history and identify risk factors related to stoma management. For example, it can evaluate the patient's family history and genetic disease risk and identify factors that affect stoma management. The information collection unit can also propose an individual risk management plan based on the genetic information. Furthermore, the information collection unit can conduct a questionnaire regarding the patient's genetic information and collect detailed information. This makes it possible to manage stomas based on the patient's genetic information.

[0056] The document generation unit can generate interactive training modules that are tailored to the patient's learning style. For example, an interactive visual guide can be provided for a visual learner, and an audio guide can be provided for an auditory learner. The document generation unit can also generate training modules that include a simulation that can be actually operated for a tactile learner. Furthermore, the document generation unit can also build a system for generating interactive training modules that are tailored to the patient's learning style. This makes it possible to generate interactive training modules that are tailored to the patient's learning style.

[0057] The document generation unit can collect patient feedback and have the generation AI automatically improve the content of the instruction manual. For example, it can conduct an online survey to collect patient opinions. The document generation unit can also revise and regularly update the content of the instruction manual based on patient feedback. Furthermore, the document generation unit can also build a system that collects patient feedback and has the generation AI automatically improve the content of the instruction manual. This makes it possible to continuously improve the instruction manual based on patient feedback.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The information collection unit uses the generation AI to collect information about the patient's stoma characteristics, symptoms, and personality. For example, the information collection unit collects information about the stoma location and cavity direction, as well as the patient's lifestyle and personality, from the patient's medical records and nurse observation records. The information collection unit also analyzes the information based on prompts provided by the generation AI, which include detailed information about the patient's stoma and the patient's individual needs. Step 2: The document generation unit generates an explanatory document based on the information collected by the information collection unit. For example, the document generation unit automatically generates an explanatory document including stoma replacement procedures, excretion management methods, and how to use stoma appliances based on the information collected by the generation AI. The document generation unit also allows the generation AI to create the optimal explanatory document based on the patient's stoma characteristics, symptoms, and personality.

[0060] (Example 2) The explanatory document generation system according to an embodiment of the present invention uses a generation AI to create explanatory documents that reflect the characteristics of a stoma and the symptoms and personality of the patient, reducing the burden on nurses and doctors associated with explanations and supporting patient self-management. As a result, the explanatory document generation system can reduce the burden on nurses and doctors associated with explanations and support patient self-management.

[0061] An explanatory document generation system according to an embodiment includes an information collection unit and a document generation unit. The information collection unit uses a generation AI to collect information about the characteristics, symptoms, and personality of a patient's stoma. For example, the information collection unit collects information about the stoma's location and cavity direction, as well as the patient's lifestyle and personality, from the patient's medical records and nurses' observation records. The information collection unit also analyzes the information based on prompts from the generation AI, including detailed information about the patient's stoma and the patient's individual needs. The document generation unit generates an explanatory document based on the information collected by the information collection unit. For example, the document generation unit automatically generates an explanatory document including stoma replacement procedures, excretion management methods, and stoma appliance usage methods based on the information collected by the generation AI. The document generation unit also generates an explanatory document optimized for the patient's stoma's characteristics, symptoms, and personality. This allows the explanatory document generation system to reduce the burden on nurses and doctors associated with providing explanations and support patient self-management.

[0062] The information collecting unit can monitor the patient's emotional state in real time and use an emotion estimation function to collect information for reducing anxiety and stress related to the stoma. For example, the information collecting unit uses a wearable device to monitor the patient's emotional state in real time. This collects physiological data such as heart rate and electrodermal activity, and analyzes the level of anxiety and stress using an emotion estimation algorithm. The information collecting unit can also capture the patient's facial expressions with a camera and analyze the emotions using facial expression recognition technology. For example, it calculates an emotion score based on changes in facial expressions. Furthermore, the information collecting unit can record the patient's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. This makes it possible to collect information according to the patient's emotional state.

[0063] The information collection unit can collect detailed information about the patient's living environment and identify factors that affect stoma management. For example, the information collection unit may conduct a home visit to observe the home situation and lifestyle habits in order to collect detailed information about the patient's living environment. This identifies factors that affect stoma management. The information collection unit can also investigate the patient's work environment and collect factors related to stoma management. For example, it may evaluate the stress level and working environment at work. Furthermore, the information collection unit can conduct a questionnaire about the patient's living environment and collect detailed information. For example, it may conduct a questionnaire that includes questions about eating habits and exercise habits. This makes it possible to manage stoma based on the patient's living environment.

[0064] The information collecting unit can monitor the patient's dietary and exercise habits and collect factors that may affect the stoma condition. For example, the information collecting unit uses a food record app to monitor the patient's dietary habits. The patient records their daily meals, and the app analyzes the nutrients and calories to identify factors that affect the stoma condition. The information collecting unit can also use a fitness tracker to monitor the patient's exercise habits. For example, the frequency and intensity of exercise can be recorded to collect data related to the stoma condition. Furthermore, the information collecting unit can conduct a questionnaire regarding the patient's dietary and exercise habits to collect detailed information. For example, a questionnaire including questions about specific foods and types of exercise can be conducted. This enables stoma management based on the patient's dietary and exercise habits.

[0065] The information collection unit can collect information on the patient's family and caregivers and provide information to strengthen the support system. For example, the information collection unit conducts a family interview to collect information on the patient's family and caregivers. During the interview, the family's support system and the caregiver's role are confirmed, and information necessary for stoma management is provided. The information collection unit can also conduct a questionnaire for the family and caregivers to collect detailed information. For example, a questionnaire including questions about family structure and caregiver experiences is conducted. Furthermore, the information collection unit can evaluate the support system for the family and caregivers and collect information to provide necessary support. For example, the information collection unit can introduce care methods and support services. This makes it possible to strengthen the support system based on information from the patient's family and caregivers.

[0066] The information collecting unit can integrate information about the patient's past medical history and other diseases to support comprehensive health management. The information collecting unit, for example, collects the patient's past medical history and integrates information related to stoma management. For example, it checks past surgical history and treatment history to support comprehensive health management. The information collecting unit can also collect information about the patient's other diseases and integrate data related to stoma management. For example, it can evaluate the presence or absence of chronic diseases and their treatment status. Furthermore, the information collecting unit can conduct a questionnaire about the patient's medical history and disease information to collect detailed information. For example, it conducts a questionnaire including questions about past treatment experience and current health status. This enables comprehensive health management based on the patient's past medical history and other diseases.

[0067] The information collection unit can use the emotion estimation function to implement a customized information collection process according to the patient's emotional state. The information collection unit, for example, uses the emotion estimation function to customize the information collection process according to the patient's emotional state. For example, if the patient is feeling anxious, it prioritizes collecting information that provides a sense of security. The information collection unit can also adjust individual questions and collection frequency according to the patient's emotional state. For example, when the patient is relaxed, it collects detailed information. Furthermore, the information collection unit can use the emotion estimation function to build a system for implementing an information collection process based on the patient's emotional state. For example, an emotion analysis algorithm is incorporated to collect information according to the patient's emotional state. This makes it possible to customize information collection according to the patient's emotional state.

[0068] The document generation unit can use the emotion estimation function to select words and expressions that are most easily understood by the patient and generate an explanatory document. For example, the document generation unit can use the emotion estimation function to analyze the patient's past communication data to select words and expressions that are most easily understood by the patient. For example, the document generation unit can identify words and expressions that the patient prefers and reflect them in the explanatory document. The document generation unit can also generate an explanatory document that includes simple language and explanations of technical terms depending on the patient's emotional state. For example, if the patient is feeling anxious, words that give a sense of security can be used. Furthermore, the document generation unit can use the emotion estimation function to build a system for selecting words and expressions that are most easily understood by the patient. For example, an emotion analysis algorithm can be incorporated to generate an explanatory document based on the patient's emotional state. This makes it possible to generate an explanatory document that uses words and expressions that are most easily understood by the patient.

[0069] The document generation unit can generate multimedia explanatory documents tailored to the patient's learning style. For example, the document generation unit conducts a visual, auditory, and tactile questionnaire to evaluate the patient's learning style. This results in the generation of multimedia explanatory documents tailored to the patient's learning style. The document generation unit can also incorporate a multimedia generation algorithm into the generation AI to generate explanatory documents tailored to the patient's learning style. For example, it can provide explanatory documents including videos and illustrations to visual learners and audio guides to auditory learners. Furthermore, the document generation unit can build a system for generating multimedia explanatory documents tailored to the patient's learning style. For example, it can generate explanatory documents including interactive content to enable patients to learn tactilely. This makes it possible to generate multimedia explanatory documents tailored to the patient's learning style.

[0070] The document generation unit can collect patient feedback in real time and continuously improve the content of the instruction manual. For example, the document generation unit conducts an online survey to collect patient feedback on the instruction manual in real time. This allows for continuous improvement of the content of the instruction manual. The document generation unit can also revise and periodically update the content of the instruction manual based on patient feedback. For example, the document generation unit makes revisions based on the feedback to reflect the latest information. Furthermore, the document generation unit can also build a system for collecting patient feedback in real time and continuously improving the content of the instruction manual. For example, a feedback analysis algorithm can be incorporated to generate an instruction manual that reflects patient opinions. This allows for continuous improvement of the instruction manual based on patient feedback.

[0071] The document generation unit can automatically generate explanatory documents in multiple languages, achieving multilingual support. For example, the document generation unit incorporates a multilingual translation algorithm into the generation AI to automatically generate explanatory documents in multiple languages. For example, languages ​​such as English, French, and Chinese are supported. The document generation unit can also build a system to provide explanatory documents automatically generated by the generation AI in multiple languages. For example, the document generation unit can make the explanatory documents available to patients in their selected language through an online platform. Furthermore, the document generation unit can generate explanatory documents in multimedia format, including audio guides and subtitles, to provide explanatory documents automatically generated by the generation AI in multiple languages. This enables the automatic generation of explanatory documents in multiple languages, enabling multilingual support.

[0072] The document generation unit can generate explanatory documents as animations or interactive content to make them visually easier to understand. For example, the document generation unit incorporates an animation generation algorithm into the generation AI to generate explanatory documents in animation format. For example, the document generation unit can show a stoma replacement procedure using animation. The document generation unit can also generate explanatory documents that include interactive content. For example, the document generation unit can provide an interactive guide that includes clickable elements. Furthermore, the document generation unit can generate explanatory documents that include 3D animations or interactive content to make the explanatory documents automatically generated by the generation AI visually easier to understand. This makes it possible to generate explanatory documents as animations or interactive content that are visually easier to understand.

[0073] The document generation unit can use the emotion estimation function to adjust the tone and style of the explanatory document according to the patient's emotional state. For example, the document generation unit incorporates an emotion analysis algorithm into the generation AI to use the emotion estimation function to adjust the tone and style of the explanatory document according to the patient's emotional state. For example, if the patient is feeling anxious, a reassuring tone is used. The document generation unit can also select a friendly tone or a professional style according to the patient's emotional state. For example, if the patient is relaxed, a style including detailed explanations is used. Furthermore, the document generation unit can use the emotion estimation function to build a system for adjusting the tone and style of the explanatory document based on the patient's emotional state. This makes it possible to generate explanatory documents in a tone and style according to the patient's emotional state.

[0074] The information collection unit can build a system that optimizes nurses' work schedules and efficiently distributes explanatory documents automatically generated by the generation AI. For example, the information collection unit incorporates a scheduling algorithm into the generation AI to optimize nurses' work schedules. For example, the information collection unit generates an optimal schedule taking into account nurses' shifts and work content. The information collection unit can also build an email or printed distribution system to efficiently distribute the explanatory documents automatically generated by the generation AI. For example, the explanatory documents can be sent by email so that nurses can quickly obtain the information they need. Furthermore, the information collection unit can build a system that provides the explanatory documents through an online platform to efficiently distribute the explanatory documents automatically generated by the generation AI. This makes it possible to optimize nurses' work schedules and efficiently distribute explanatory documents.

[0075] The information collection unit can collect feedback from nurses and continuously improve the document generation algorithm of the generative AI. For example, the information collection unit conducts an online survey to collect feedback from nurses. This allows for continuous improvement of the document generation algorithm of the generative AI. The information collection unit can also modify and periodically update the document generation algorithm based on the nurses' feedback. For example, it makes modifications based on the feedback and reflects the latest information. Furthermore, the information collection unit can also build a system for collecting feedback from nurses and continuously improving the document generation algorithm of the generative AI. This makes it possible to continuously improve the document generation algorithm based on nurses' feedback.

[0076] The information collection unit can reduce the burden on nurses by having the generation AI automatically perform some of the explanations that nurses give to patients. For example, the information collection unit incorporates a natural language generation algorithm into the generation AI so that the generation AI can automatically perform some of the explanations that nurses give to patients. For example, it can automatically explain stoma replacement procedures and excretion management methods. The information collection unit can also use the explanatory documents automatically generated by the generation AI to replace some of the explanations that nurses give. For example, the information collection unit can provide the explanatory documents automatically generated by the generation AI to patients, thereby shortening the time it takes for nurses to give explanations. Furthermore, the information collection unit can also build a system to reduce the burden on nurses by using the explanatory documents automatically generated by the generation AI. This reduces the burden on nurses and enables the generation AI to automatically perform some of the explanations.

[0077] The information collection unit can introduce generative AI into a nurse education program to streamline the training of new nurses. For example, to introduce generative AI into a nurse education program, the information collection unit incorporates an educational content generation algorithm into the generative AI. For example, it automatically generates training materials related to stoma management. The information collection unit can also use the educational content automatically generated by the generative AI to streamline the training of new nurses. For example, it can provide online courses and on-site training. Furthermore, the information collection unit can use the educational content automatically generated by the generative AI to build a system for streamlining the nurse education program. This makes it possible to streamline the training of new nurses.

[0078] The information collection unit automates the nurses' work records, allowing the generation AI to manage the progress of their work in real time. For example, the information collection unit incorporates a work record generation algorithm into the generation AI to automate the nurses' work records. For example, it automatically records the work content performed by the nurses. The information collection unit can also incorporate a progress management algorithm so that the generation AI can manage the progress of their work in real time. For example, it manages the completion status of tasks and the frequency of progress reports. Furthermore, the information collection unit can also build a system for managing the progress of nurses' work in real time using the work records automatically generated by the generation AI. This makes it possible to automate the nurses' work records and manage the progress of their work in real time.

[0079] The information collecting unit can use the emotion estimation function to monitor the stress level of a nurse and provide appropriate support. For example, the information collecting unit uses a wearable device to monitor the stress level of a nurse using the emotion estimation function. This collects physiological data such as heart rate and electrodermal activity and analyzes the stress level. The information collecting unit can also capture the nurse's facial expression with a camera and analyze the stress level using facial expression recognition technology. For example, a stress score is calculated based on changes in facial expression. Furthermore, the information collecting unit can record the nurse's voice and estimate the stress level using voice analysis technology. For example, the tone and speed of voice can be analyzed to calculate a stress score. This makes it possible to monitor the nurse's stress level and provide appropriate support.

[0080] The document generation unit can use the emotion estimation function to provide an explanatory document at a timing that is easy for the patient to understand. For example, the document generation unit uses the emotion estimation function to monitor the emotional state of the patient in real time to identify the timing that is easy for the patient to understand. For example, the document generation unit provides the explanatory document when the patient is relaxed. The document generation unit can also provide the explanatory document at a timing that matches the patient's schedule. For example, the document generation unit provides the explanatory document during a time period when the patient has time to spare. Furthermore, the document generation unit can also use the emotion estimation function to build a system for providing the explanatory document at a timing that is easy for the patient to understand. This makes it possible to provide the explanatory document at a timing that is easy for the patient to understand.

[0081] The document generation unit can evaluate the patient's level of understanding in real time and provide additional explanations as needed. For example, the document generation unit conducts online quizzes and tests to evaluate the patient's level of understanding in real time. This measures the patient's level of understanding and provides additional explanations as needed. The document generation unit can also provide detailed explanations and supplementary materials based on the patient's level of understanding. For example, if the level of understanding is low, more specific explanations are provided. Furthermore, the document generation unit can also build a system for evaluating the patient's level of understanding in real time and providing additional explanations as needed. This makes it possible to provide additional explanations according to the patient's level of understanding.

[0082] The document generation unit can track the patient's learning progress and provide an individualized learning plan. For example, the document generation unit develops an online learning platform to track the patient's learning progress. The platform records the patient's learning history and provides an individualized learning plan. The document generation unit can also provide a customized learning curriculum in accordance with the patient's learning progress using the learning plan automatically generated by the generative AI. For example, it creates a plan based on the patient's learning goals. Furthermore, the document generation unit can build a system for tracking the patient's learning progress and providing an individualized learning plan. This makes it possible to provide an individualized learning plan in accordance with the patient's learning progress.

[0083] The document generation unit can gamify the explanatory documents, allowing patients to learn while having fun. For example, the document generation unit incorporates a game design algorithm into the generation AI to gamify the explanatory documents. For example, it can provide quizzes or puzzles related to stoma management. The document generation unit can also introduce a point system or reward system to allow patients to learn while having fun. For example, points can be earned for each correct answer to a quiz, and a reward can be obtained when a certain number of points are reached. Furthermore, the document generation unit can build a system to gamify the explanatory documents, allowing patients to learn while having fun. This makes it possible to gamify the explanatory documents, allowing patients to learn while having fun.

[0084] The document generation unit can also provide explanatory documents to the patient's family and caregivers to promote comprehensive understanding. The document generation unit, for example, develops an online platform to provide explanatory documents to the patient's family and caregivers. The platform allows the family and caregivers to access the explanatory documents. The document generation unit can also generate explanatory documents that include explanations and guidelines for family and caregivers. For example, the document generation unit provides explanatory documents that include explanations for family members and creates guidelines for caregivers. Furthermore, the document generation unit can also build a system for providing explanatory documents to the patient's family and caregivers to promote comprehensive understanding. This makes it possible to provide explanatory documents to the patient's family and caregivers to promote comprehensive understanding.

[0085] The document generation unit can use the emotion estimation function to provide learning support according to the patient's emotional state. For example, the document generation unit incorporates an emotion analysis algorithm into the generation AI to provide learning support according to the patient's emotional state using the emotion estimation function. For example, when the patient is feeling anxious, the document generation unit provides support that gives a sense of security. The document generation unit can also provide individualized instruction and supplementary materials according to the patient's emotional state. For example, when the patient is relaxed, the document generation unit provides support that includes detailed explanations. Furthermore, the document generation unit can also use the emotion estimation function to build a system for providing learning support according to the patient's emotional state. This makes it possible to provide learning support according to the patient's emotional state.

[0086] The information collection unit can use the emotion estimation function to continuously provide support according to the patient's emotional state. For example, the information collection unit incorporates an emotion analysis algorithm into the generative AI to continuously provide support according to the patient's emotional state using the emotion estimation function. For example, when the patient is feeling anxious, the information collection unit provides support that gives a sense of security. The information collection unit can also provide regular follow-up and ongoing counseling according to the patient's emotional state. For example, when the patient is relaxed, the information collection unit can provide detailed counseling. Furthermore, the information collection unit can also use the emotion estimation function to build a system for continuously providing support according to the patient's emotional state. This makes it possible to continuously provide support according to the patient's emotional state.

[0087] The information collection unit can monitor changes in the patient's condition in real time and update the explanatory document as necessary. For example, the information collection unit uses a wearable device to monitor changes in the patient's condition in real time. This collects physiological data such as heart rate and body temperature, and the generation AI updates the explanatory document based on that data. The information collection unit can also monitor changes in the patient's vital signs and the progression of symptoms and revise the explanatory document as necessary. For example, it can conduct periodic reviews to reflect the latest information. Furthermore, the information collection unit can also build a system for monitoring changes in the patient's condition in real time and updating the explanatory document as necessary. This makes it possible to update the explanatory document in real time in response to changes in the patient's condition.

[0088] The information collection unit collects patient feedback, and the generation AI can automatically improve the support content. For example, the information collection unit conducts an online survey to collect patient feedback. This allows the generation AI to automatically improve the support content. The information collection unit can also modify and regularly update the support content based on patient feedback. For example, it makes modifications based on feedback to reflect the latest information. Furthermore, the information collection unit can also collect patient feedback and build a system for the generation AI to automatically improve the support content. This makes it possible to automatically improve the support content based on patient feedback.

[0089] The information collection unit allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm in order to provide continuous support. For example, the information collection unit uses a wearable device to analyze the patient's lifestyle rhythm. This allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm. The information collection unit can also analyze the patient's daily schedule and sleep patterns and adjust the timing of sending reminders. For example, it can send reminders for taking medication or for regular health checks. Furthermore, the information collection unit can also build a system that allows the generation AI to send reminders that are tailored to the patient's lifestyle rhythm. This makes it possible to send reminders that are tailored to the patient's lifestyle rhythm.

[0090] The information collection unit can form a patient community and promote information sharing with other patients. For example, the information collection unit develops an online platform to form a patient community. The platform enables patients to share information and support each other. The information collection unit can also provide a forum for patients to share their experiences and advice. For example, an online forum can be set up to exchange information about stoma management. Furthermore, the information collection unit can build a system to form a patient community and promote information sharing with other patients. This makes it possible to form a patient community and promote information sharing with other patients.

[0091] The information collection unit can use the emotion estimation function to send support messages according to the patient's emotional state. For example, the information collection unit incorporates an emotion analysis algorithm into the generation AI to use the emotion estimation function to send support messages according to the patient's emotional state. For example, when the patient is feeling anxious, it sends a message that gives a sense of security. The information collection unit can also provide encouraging messages or advice according to the patient's emotional state. For example, when the patient is relaxed, it sends a positive message. Furthermore, the information collection unit can also use the emotion estimation function to build a system for sending support messages according to the patient's emotional state. This makes it possible to send support messages according to the patient's emotional state.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The information collection unit can collect information about the patient's hobbies and interests and reflect it in the explanatory document. For example, if the patient is interested in a particular sport or art, the explanatory document can be created using examples and analogies related to that hobby. The information collection unit can also collect information about the patient's favorite movies and music and generate an explanatory document including examples based on that information. Furthermore, the information collection unit can also conduct a questionnaire about the patient's hobbies and interests and collect detailed information. This makes it possible to generate an explanatory document based on the patient's hobbies and interests.

[0094] The information collecting unit can estimate the emotional state of the patient and provide relaxing music or videos based on the estimated emotions. For example, if the patient is feeling anxious, relaxing music can be provided. The information collecting unit can also provide videos including relaxing images or natural scenery according to the patient's emotional state. Furthermore, the information collecting unit can use the emotion estimation function to build a system for providing relaxing content based on the patient's emotional state. This makes it possible to provide relaxing content according to the patient's emotional state.

[0095] The information collection unit can monitor the sound environment in the patient's living environment and suggest a sound environment suitable for stoma management. For example, it can measure the noise level in the patient's living environment and provide advice on providing a quiet environment. The information collection unit can also investigate the use of music in the patient's living environment and suggest music that has a relaxing effect. Furthermore, the information collection unit can also conduct a questionnaire about the patient's living environment and collect detailed information about the sound environment. This makes it possible to suggest a sound environment based on the patient's living environment.

[0096] The information collection unit can monitor the patient's sleep patterns, as well as their dietary and exercise habits, and collect factors that affect stoma management. For example, it can record the patient's sleep time and sleep quality and collect data related to the state of the stoma. The information collection unit can also investigate the patient's sleep environment and suggest areas for improvement. Furthermore, the information collection unit can also conduct questionnaires about the patient's sleep habits and collect detailed information. This makes it possible to manage stomas based on the patient's sleep patterns.

[0097] The information collecting unit can estimate the emotional state of the patient's family or caregiver and provide a support message based on the estimated emotion. For example, if the family or caregiver is feeling stressed, an encouraging message can be sent. The information collecting unit can also suggest relaxing activities according to the emotional state of the family or caregiver. Furthermore, the information collecting unit can use the emotion estimation function to build a system for providing a support message based on the emotional state of the family or caregiver. This makes it possible to provide a support message according to the emotional state of the family or caregiver.

[0098] The information collection unit can collect genetic information in addition to the patient's past medical history and identify risk factors related to stoma management. For example, it can evaluate the patient's family history and genetic disease risk and identify factors that affect stoma management. The information collection unit can also propose an individual risk management plan based on the genetic information. Furthermore, the information collection unit can conduct a questionnaire regarding the patient's genetic information and collect detailed information. This makes it possible to manage stomas based on the patient's genetic information.

[0099] The information collecting unit can use the emotion estimation function to send reminders according to the patient's emotional state. For example, if the patient is feeling stressed, it can send a reminder to relax. The information collecting unit can also send reminders to take medication or change a stoma at an appropriate time according to the patient's emotional state. Furthermore, the information collecting unit can also use the emotion estimation function to build a system for sending reminders based on the patient's emotional state. This makes it possible to send reminders according to the patient's emotional state.

[0100] The document generation unit can use the emotion estimation function to provide the explanatory document at a timing when the patient is most relaxed. For example, providing the explanatory document when the patient is relaxed improves the patient's level of understanding. The document generation unit can also adjust the timing of providing the explanatory document depending on the patient's emotional state. Furthermore, the document generation unit can also use the emotion estimation function to build a system for providing the explanatory document at a timing when the patient is most relaxed. This makes it possible to provide the explanatory document at a timing when the patient is most relaxed.

[0101] The document generation unit can generate interactive training modules that are tailored to the patient's learning style. For example, an interactive visual guide can be provided for a visual learner, and an audio guide can be provided for an auditory learner. The document generation unit can also generate training modules that include a simulation that can be actually operated for a tactile learner. Furthermore, the document generation unit can also build a system for generating interactive training modules that are tailored to the patient's learning style. This makes it possible to generate interactive training modules that are tailored to the patient's learning style.

[0102] The document generation unit can collect patient feedback and have the generation AI automatically improve the content of the instruction manual. For example, it can conduct an online survey to collect patient opinions. The document generation unit can also revise and regularly update the content of the instruction manual based on patient feedback. Furthermore, the document generation unit can also build a system that collects patient feedback and has the generation AI automatically improve the content of the instruction manual. This makes it possible to continuously improve the instruction manual based on patient feedback.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The information collection unit uses the generation AI to collect information about the patient's stoma characteristics, symptoms, and personality. For example, the information collection unit collects information about the stoma location and cavity direction, as well as the patient's lifestyle and personality, from the patient's medical records and nurse observation records. The information collection unit also analyzes the information based on prompts provided by the generation AI, which include detailed information about the patient's stoma and the patient's individual needs. Step 2: The document generation unit generates an explanatory document based on the information collected by the information collection unit. For example, the document generation unit automatically generates an explanatory document including stoma replacement procedures, excretion management methods, and how to use stoma appliances based on the information collected by the generation AI. The document generation unit also allows the generation AI to create the optimal explanatory document based on the patient's stoma characteristics, symptoms, and personality.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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. An information gathering department uses generative AI to collect information on the patient's stoma characteristics, symptoms, and personality. a document generation unit that generates an explanatory document based on the information collected by the information collection unit. A system characterized by:

2. The information collecting unit Monitor the patient's emotional state in real time and use emotion estimation to collect information to reduce anxiety and stress related to the stoma.

2. The system of claim 1.

3. The information collecting unit Gather information from patients' families and caregivers to provide them with information to strengthen their support system 2. The system of claim 1.

4. The document generation unit Using an emotion estimation function, words and expressions that are most easily understood by the patient are selected, and the explanatory document is generated.

2. The system of claim 1.

5. The information collecting unit Optimize nurses' work schedules and build a system to efficiently distribute the explanatory documents automatically generated by the generation AI.

2. The system of claim 1.

6. The information collecting unit Using emotion estimation functionality to monitor nurses' stress levels and provide appropriate support 2. The system of claim 1.

7. The document generation unit Using an emotion estimation function, the explanatory document is provided at a timing that is easy for the patient to understand.

2. The system of claim 1.

8. The information collecting unit Using an emotion estimation function, a support message is sent according to the emotional state of the patient.

2. The system of claim 1.

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