system

Abstract

The system according to this embodiment aims to appropriately convey the doctor's explanation according to the patient's level of understanding. [Solution] The system according to the embodiment comprises a determination unit, an analysis unit, a translation unit, and a provision unit. The determination unit determines the patient's comprehension level. The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the determination unit. The translation unit translates the content of the explanation analyzed by the analysis unit. The provision unit provides the content translated by the translation unit to the patient.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that because the doctor's explanation is not properly conveyed according to the patient's comprehension ability, the patient has anxiety.

[0005] The system according to the embodiment aims to properly convey the doctor's explanation according to the patient's comprehension ability.

Means for Solving the Problems

[0006] The system according to the embodiment includes a determination unit, an analysis unit, a translation unit, and a provision unit. The determination unit determines the patient's comprehension ability. The analysis unit analyzes the doctor's explanation based on the comprehension ability determined by the determination unit. The translation unit translates the content of the explanation analyzed by the analysis unit. The provision unit provides the content translated by the translation unit to the patient. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately convey the doctor's explanation according to the patient's level of understanding. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical explanation translation system according to an embodiment of the present invention is a system that provides translations tailored to the patient's level of understanding when the patient receives an explanation from a doctor during an outpatient visit to a hospital. By providing translations tailored to the patient's level of understanding, this medical explanation translation system can reduce the patient's anxiety and deepen their understanding. Similarly, it also has a function to automatically translate documents such as consent forms according to the patient's level of understanding. For example, when a doctor explains something to a patient, the AI ​​analyzes the content in real time and translates it according to the patient's level of understanding. This makes it easier for the patient to understand the doctor's explanation and reduces their anxiety. Furthermore, the AI ​​also analyzes documents such as consent forms and automatically translates them according to the patient's level of understanding. For example, if a doctor explains, "It's stage 4 cancer. There are options for radiation therapy and surgery, which treatment would you prefer?", this content is input into the AI. Next, the AI ​​analyzes the input explanation and translates it according to the patient's level of understanding. For example, for children and the elderly, technical terms are avoided and the explanation is given in simple language. Specifically, it is translated as, "The cancer is progressing. There are two treatment methods: radiation therapy and surgery. Which would you prefer?" Furthermore, the AI ​​analyzes documents such as consent forms and automatically translates them to match the patient's level of understanding. For example, if a consent form states, "This treatment carries risks," the AI ​​will translate it as, "This treatment is dangerous." This system makes it easier for patients to understand the doctor's explanations and the contents of consent forms, reducing anxiety. It also reduces the burden on accompanying family members. For example, if a doctor explains, "It will attack other parts of the body," the AI ​​will translate it as, "The cancer will spread to other parts of the body." In this way, by providing translations tailored to the other party's level of understanding through AI, misunderstandings between both parties are eliminated, leading to a reduction in anxiety. In addition, since documents such as consent forms are automatically translated, it becomes possible to provide information tailored to the patient's level of understanding. As a result, the medical explanation translation system can reduce patient anxiety and deepen their understanding by providing doctor's explanations tailored to the patient's level of understanding.

[0029] The medical explanation translation system according to this embodiment comprises a determination unit, an analysis unit, a translation unit, and a provision unit. The determination unit determines the patient's comprehension level. The determination unit determines comprehension level based, for example, on the patient's age, past medical history, and real-time reactions. For example, the determination unit can determine comprehension level based on the patient's age. The determination unit can also determine comprehension level based on the patient's past medical history. The determination unit can also determine comprehension level based on real-time reactions. For example, the determination unit analyzes the patient's facial expressions and reaction speed to determine comprehension level. The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the determination unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology. For example, the analysis unit can analyze the doctor's explanation using morphological analysis. The analysis unit can also analyze the doctor's explanation using grammatical analysis. The analysis unit can also analyze the doctor's explanation using semantic analysis. For example, the analysis unit analyzes the content of the doctor's explanation based on context and prioritizes extracting information of high importance. The translation unit translates the explanation content analyzed by the analysis unit. The translation unit performs translations by, for example, referring to a medical terminology database. For example, the translation unit can translate technical terms into simpler language by referring to a medical terminology database. The translation unit can also improve the accuracy of translations by referring to a medical terminology database. Furthermore, the translation unit can dynamically update the medical terminology database to accommodate new medical information. For example, the translation unit dynamically updates the database when new medical terms are added. The delivery unit provides the translated content to the patient. The delivery unit provides the translated content visually or audibly. For example, the delivery unit can provide the translated content in text format. Furthermore, the delivery unit can provide the translated content in audio format. Furthermore, the delivery unit can provide the translated content in visual format. For example, the delivery unit automatically sends the translated content to the patient's device. As a result, the medical explanation translation system according to this embodiment can reduce patient anxiety and deepen their understanding by providing a doctor's explanation tailored to the patient's level of comprehension.

[0030] The assessment unit determines the patient's comprehension level. For example, it determines comprehension based on the patient's age, past medical history, and real-time responses. Specifically, when determining comprehension based on the patient's age, it evaluates general comprehension trends for each age group based on a database. For example, elderly people and children are generally considered to have lower comprehension levels, so simpler explanations are required. When determining comprehension based on past medical history, it considers the treatments and diagnoses the patient has received in the past, as well as their understanding of medical terminology. For example, a patient who has previously received treatment for the same illness is judged to have knowledge about that illness, allowing for more specialized explanations. Furthermore, when determining comprehension based on real-time responses, it analyzes the patient's facial expressions and response speed. For example, the facial expressions the patient shows while listening to an explanation and their response speed to questions are detected by cameras and sensors, and analyzed by AI. The AI ​​uses facial recognition technology to evaluate the patient's emotional state, and if it is determined that the comprehension level is low, simpler explanations are required. In this way, the assessment unit can accurately determine the patient's comprehension level according to their individual circumstances and provide basic information for providing appropriate explanations.

[0031] The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the judgment unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology. Specifically, it analyzes the doctor's explanation using morphological analysis to clarify the meaning and role of each word. Morphological analysis is a technology that breaks down a sentence into individual words and determines the part of speech of each word. This allows for a detailed analysis of the doctor's explanation and the extraction of important information. It is also possible to analyze the doctor's explanation using grammatical analysis. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies the relationships between subjects, predicates, objects, etc. This allows for an understanding of the context of the doctor's explanation and the accurate grasp of important information. Furthermore, it is also possible to analyze the doctor's explanation using semantic analysis. Semantic analysis is a technology that analyzes the meaning of a sentence and prioritizes the extraction of information of high importance based on the context. For example, it is possible to extract important information and points to note for the patient from the doctor's explanation and provide them in an easy-to-understand format. This allows the analysis unit to appropriately analyze the doctor's explanation based on the comprehension level determined by the judgment unit, and prepare to provide the information in the most easily understandable format for the patient.

[0032] The translation department translates the explanations analyzed by the analysis department. The translation department performs translations by, for example, referring to a medical terminology database. Specifically, it can translate technical terms into simpler language by referring to the medical terminology database. For example, it can replace the technical term "hypertension" with a more general expression such as "a state of high blood pressure." The translation department can also improve the accuracy of translations by referring to the medical terminology database. For example, since the meaning of the same medical term can differ depending on the context, it can provide appropriate translations based on the context. Furthermore, the translation department can dynamically update the medical terminology database to accommodate new medical information. For example, if a new medical term is added, the database can be dynamically updated to provide translations based on the latest information. This allows the translation department to always provide accurate translations based on the latest medical information and present information in a way that is easy for patients to understand. The translation department can also improve translation accuracy using AI. AI can learn from past translation data and continuously improve translation accuracy. This allows the translation department to always provide highly accurate translations, deepening patients' understanding.

[0033] The information delivery unit provides patients with the content translated by the translation unit. The information delivery unit provides the translated content visually or audibly, for example. Specifically, the translated content can be provided in text format. For example, it can be displayed in text format on the patient's smartphone or tablet so that the patient can check it at any time. The information delivery unit can also provide the translated content in audio format. For example, using speech synthesis technology, the translated content can be played back as audio, providing information to patients with visual impairments or those who have difficulty reading text. Furthermore, the information delivery unit can provide the translated content in visual format. For example, medical explanations can be provided visually and clearly using illustrations and diagrams. This allows the information delivery unit to provide information in the most optimal format according to the patient's comprehension level and situation, deepening the patient's understanding. The information delivery unit can also automatically send the translated content to the patient's device. For example, the translated explanation can be automatically sent to the patient's smartphone after the consultation so that the patient can check it later. This allows the information delivery unit to make it easier for patients to review the doctor's explanation after the consultation, deepening their understanding. Furthermore, the information delivery unit can collect feedback from patients and continuously improve the accuracy and effectiveness of the provided content. For example, patients can evaluate their understanding and satisfaction with the information provided, and the content of the information provided can be reviewed based on that feedback. This allows the information provider to always provide the most optimal information for the patient and maximize the effectiveness of medical explanations.

[0034] The document analysis unit can analyze documents such as consent forms. The document analysis unit can analyze documents using, for example, text mining technology. For example, the document analysis unit can analyze the contents of a consent form using text mining technology. The document analysis unit can also analyze documents using natural language processing technology. For example, the document analysis unit can analyze the contents of a consent form using natural language processing technology. This allows the document analysis unit to provide information tailored to the patient's level of understanding by analyzing documents such as consent forms. Some or all of the above-described processes in the document analysis unit may be performed using, for example, AI, or without AI. For example, the document analysis unit can input the contents of a consent form into a generating AI and have the generating AI perform the analysis.

[0035] The document translation unit can translate the content of the consent form analyzed by the document analysis unit. The document translation unit performs translations by referring to, for example, a medical terminology database. For example, the document translation unit can refer to the medical terminology database to translate technical terms into simpler language. The document translation unit can also refer to the medical terminology database to improve the accuracy of the translation. Furthermore, the document translation unit can dynamically update the medical terminology database to accommodate new medical information. For example, the document translation unit dynamically updates the database when new medical terms are added. This allows the document translation unit to improve the patient's understanding by translating the content of the consent form to match the patient's comprehension level. Some or all of the above processes in the document translation unit may be performed using, for example, AI, or not using AI. For example, the document translation unit can input the content of the consent form into a generating AI and have the generating AI perform the translation.

[0036] The assessment unit can determine comprehension based on the patient's age, past medical history, and real-time responses. For example, the assessment unit can determine comprehension based on the patient's age. For example, the assessment unit can take the patient's age as input and determine comprehension based on age. The assessment unit can also determine comprehension based on the patient's past medical history. For example, the assessment unit can take the patient's past medical records as input and determine comprehension based on medical history. The assessment unit can also determine comprehension based on real-time responses. For example, the assessment unit can take the patient's facial expressions and reaction speed as input and determine comprehension based on real-time responses. This allows for a more accurate determination of comprehension by determining comprehension based on the patient's age, past medical history, and real-time responses. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the patient's age, medical history, and real-time response data into a generating AI, and have the generating AI perform the comprehension determination.

[0037] The analysis unit can analyze the doctor's explanation using natural language processing technology. For example, the analysis unit can analyze the doctor's explanation using morphological analysis. For example, the analysis unit can analyze the content of the doctor's explanation using morphological analysis technology. The analysis unit can also analyze the doctor's explanation using grammatical analysis. For example, the analysis unit can analyze the content of the doctor's explanation using grammatical analysis technology. The analysis unit can also analyze the doctor's explanation using semantic analysis. For example, the analysis unit can analyze the content of the doctor's explanation using semantic analysis technology. As a result, the accuracy of the analysis of the doctor's explanation is improved by using natural language processing technology. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the doctor's explanation into a generating AI and have the generating AI perform the analysis.

[0038] The translation unit can perform translations by referring to a database of medical terms. For example, the translation unit can translate technical terms into simpler terms by referring to a database of medical terms. The translation unit can also improve the accuracy of translations by referring to a database of medical terms. The translation unit can also dynamically update the database of medical terms to accommodate new medical information. For example, if a new medical term is added, the translation unit will dynamically update the database. This improves the accuracy of translations by referring to the database of medical terms. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the database of medical terms into a generating AI and have the generating AI perform the translation.

[0039] The delivery unit can provide the translated content to the patient visually or audibly. The delivery unit can, for example, provide the translated content in text format. The delivery unit can also provide the translated content in audio format. The delivery unit can also provide the translated content in visual format. This helps the patient understand the translated content by providing it visually or audibly. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the translated content into a generating AI, which can then provide it visually or audibly.

[0040] The assessment unit can determine comprehension by combining the patient's past medical history and current health status. For example, the assessment unit can refer to the patient's past medical records and determine comprehension by comparing them with the current health status. For example, the assessment unit can take the patient's past medical records as input and determine comprehension based on the current health status. The assessment unit can also determine comprehension by considering the current health status based on the patient's past treatment history. For example, the assessment unit can take the patient's past treatment history as input and determine comprehension based on the current health status. The assessment unit can also integrate the patient's past medical history and current health status to help determine comprehension. For example, the assessment unit can take the patient's past medical history and current health status as input and integrate them to determine comprehension. This improves the accuracy of comprehension determination by combining past medical history and current health status. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input data on the patient's past medical history and current health status into a generating AI and have the generating AI perform the comprehension determination.

[0041] The assessment unit can analyze the patient's real-time reaction speed and facial expressions and dynamically update their comprehension level. For example, the assessment unit can analyze the patient's facial expressions using a camera and reflect this in the assessment of comprehension level. For example, the assessment unit can capture the patient's facial expressions with a camera and analyze them using facial expression analysis technology. The assessment unit can also measure the patient's reaction speed with a sensor and use this to assess comprehension level. For example, the assessment unit can measure the patient's reaction speed with a sensor and determine comprehension level based on the reaction speed. The assessment unit can also analyze the patient's real-time reactions and dynamically update their comprehension level. For example, the assessment unit can take the patient's real-time reaction data as input and dynamically update their comprehension level. This makes it possible to dynamically update comprehension level by analyzing real-time reaction speed and facial expressions. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the patient's real-time reaction data into a generating AI and have the generating AI perform the dynamic update of comprehension level.

[0042] The assessment unit can determine comprehension by considering the patient's lifestyle and educational background. For example, the assessment unit can analyze the patient's lifestyle and reflect it in the assessment of comprehension. For example, the assessment unit can take the patient's lifestyle as input and determine comprehension based on that lifestyle. The assessment unit can also analyze the patient's educational background and use it in determining comprehension. For example, the assessment unit can take the patient's educational background as input and determine comprehension based on that educational background. The assessment unit can also integrate the patient's lifestyle and educational background to aid in determining comprehension. For example, the assessment unit can take the patient's lifestyle and educational background as input, integrate them, and determine comprehension. This improves the accuracy of comprehension assessment by considering lifestyle and educational background. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input data on the patient's lifestyle and educational background into a generating AI and have the generating AI perform the comprehension assessment.

[0043] The assessment unit can determine comprehension by incorporating feedback from the patient's family and caregivers. For example, the assessment unit can analyze feedback from the patient's family and reflect it in the assessment of comprehension. For example, the assessment unit can take feedback from the patient's family as input and determine comprehension based on that feedback. The assessment unit can also analyze feedback from the patient's caregivers and use it in determining comprehension. For example, the assessment unit can take feedback from the patient's caregivers as input and determine comprehension based on that feedback. The assessment unit can also integrate feedback from the patient's family and caregivers and use it to help determine comprehension. For example, the assessment unit can take feedback from the patient's family and caregivers as input, integrate it, and determine comprehension. This improves the accuracy of comprehension assessment by incorporating feedback from family and caregivers. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input feedback data from the patient's family and caregivers into a generating AI and have the generating AI perform the comprehension assessment.

[0044] The analysis unit can analyze the physician's explanation based on context and prioritize extracting information of high importance. For example, the analysis unit can analyze the physician's explanation using contextual analysis techniques and prioritize extracting important information. Furthermore, the analysis unit can analyze the physician's explanation and highlight information that is important to the patient. For example, the analysis unit can analyze the physician's explanation and highlight information that is important to the patient. Furthermore, the analysis unit can analyze the physician's explanation based on context and extract information of high importance. This allows for the provision of important information to the patient by extracting information of high importance based on context. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the physician's explanation into a generating AI, have the generating AI perform contextual analysis, and extract important information.

[0045] The analysis unit can analyze the physician's explanation in multiple languages ​​and accommodate patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and accommodate patients with different cultural backgrounds. Furthermore, the analysis unit can analyze the physician's explanation in multiple languages ​​and provide it to patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and provide it to patients with different cultural backgrounds. Furthermore, the analysis unit can analyze the physician's explanation in multiple languages ​​and adapt it to patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and adapt it to patients with different cultural backgrounds. This allows for analysis in multiple languages, thus accommodating patients with different cultural backgrounds. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the physician's explanation into a generating AI, which can then perform the analysis in multiple languages.

[0046] The analysis unit can analyze the doctor's explanation as audio data and convert it to text using speech recognition technology. For example, the analysis unit can analyze the doctor's explanation as audio data and convert it to text using speech recognition technology. The analysis unit can also analyze the doctor's explanation as audio data, convert it to text, and provide it to the patient. The analysis unit can also analyze the doctor's explanation as audio data, convert it to text using speech recognition technology, and save it. This makes it easier to record the doctor's explanation by converting the audio data to text. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the audio data of the doctor's explanation into a generating AI, have the generating AI perform speech recognition, and convert it to text.

[0047] The analysis unit can analyze the doctor's explanation as visual data and generate diagrams and illustrations. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams. The analysis unit can also analyze the doctor's explanation as visual data and generate illustrations. For example, the analysis unit can analyze the doctor's explanation as visual data and generate illustrations. The analysis unit can also analyze the doctor's explanation as visual data and generate diagrams and illustrations to provide to the patient. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams and illustrations to provide to the patient. This makes it easier for patients to understand visually by generating visual data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the doctor's explanation into a generation AI and have the generation AI generate visual data.

[0048] The translation unit can dynamically update its medical terminology database to accommodate new medical information. For example, if a new medical term is added, the translation unit can dynamically update the database. The translation unit can also dynamically update the database and reflect new treatments in its translations. The translation unit can also dynamically update its database and use it in translations when new medical information is published. This allows the translation unit to keep up with the latest medical information by dynamically updating its medical terminology database. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information on new medical terms and treatments into a generating AI, which can then perform the database update.

[0049] The translation unit can provide translation results in multiple formats (text, audio, visual). For example, the translation unit can provide translation results in text format. For example, the translation unit can provide translation results in text format. The translation unit can also provide translation results in audio format. For example, the translation unit can provide translation results in audio format. The translation unit can also provide translation results in visual format. For example, the translation unit can provide translation results in visual format. By providing information in multiple formats, patients can receive information in a format that is easy for them to understand. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input translation results into a generating AI, and the generating AI can provide them in text, audio, and visual formats.

[0050] The translation unit can provide translation results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the translation unit can provide concise translation results. For example, if the patient's level of understanding is low, the translation unit can provide concise translation results. The translation unit can also provide detailed translation results if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the translation unit can provide detailed translation results. For example, if the patient's level of understanding is high, the translation unit can provide specialized translation results. For example, if the patient's level of understanding is high, the translation unit can provide specialized translation results. This allows for more appropriate information to be provided by providing translation results in stages according to the patient's level of understanding. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input patient understanding data into a generating AI, and the generating AI can provide translation results in stages.

[0051] The translation unit can customize the translation results to match the patient's native language. For example, if the patient's native language is English, the translation unit can customize the translation results to English. For example, if the patient's native language is English, the translation unit can customize the translation results to English. Also, if the patient's native language is Spanish, the translation unit can customize the translation results to Spanish. For example, if the patient's native language is Chinese, the translation unit can customize the translation results to Chinese. This allows for the provision of more appropriate information by customizing the translation results to match the patient's native language. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the patient's native language data into a generating AI, and the generating AI can customize the translation results.

[0052] The delivery unit can automatically send the translated content to the patient's device. For example, the delivery unit can automatically send the translated content to the patient's smartphone. The delivery unit can also automatically send the translated content to the patient's tablet. For example, the delivery unit can automatically send the translated content to the patient's tablet. The delivery unit can also automatically send the translated content to the patient's computer. For example, the delivery unit can automatically send the translated content to the patient's computer. This makes it easier for the patient to receive the information by automatically sending the translated content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the translated content into a generating AI, and the generating AI can automatically send it to the patient's device.

[0053] The service provider can collect patient feedback on the information provided and reflect it in the next service provision method. For example, the service provider can collect patient feedback on the information provided and reflect it in the next service provision method. For example, the service provider can collect patient feedback on the information provided and reflect it in the next service provision method. The service provider can also analyze patient feedback on the information provided and improve the next service provision method. For example, the service provider can analyze patient feedback on the information provided and improve the next service provision method. The service provider can also collect patient feedback on the information provided and use it to improve the next service provision method. For example, the service provider can collect patient feedback on the information provided and use it to improve the next service provision method. By collecting patient feedback, the service provider can improve the next service provision method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input patient feedback data into a generating AI, and the generating AI can improve the next service provision method.

[0054] The information provider can also share the provided information with the patient's family and caregivers. For example, the information provider can automatically share the provided information with the patient's family. For example, the information provider can automatically share the provided information with the patient's family. The information provider can also automatically share the provided information with the patient's caregivers. For example, the information provider can automatically share the provided information with the patient's caregivers. The information provider can also automatically share the provided information with the patient's family and caregivers. For example, the information provider can automatically share the provided information with the patient's family and caregivers. This makes it easier to support the patient by sharing the information with family and caregivers. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the provided information into a generating AI, and the generating AI can automatically share it with the patient's family and caregivers.

[0055] The information provider can automatically record the provided information in the patient's electronic medical record. For example, the information provider can automatically record the provided information in the patient's electronic medical record. For example, the information provider can automatically record the provided information in the patient's electronic medical record. The information provider can also automatically record the provided information in the patient's medical record. For example, the information provider can automatically record the provided information in the patient's medical record. The information provider can also automatically record the provided information in the patient's medical database. For example, the information provider can automatically record the provided information in the patient's medical database. This makes it easier to manage medical information by automatically recording the information in the electronic medical record. Some or all of the above processing in the information provider can be performed using AI, for example, or without AI. For example, the information provider can input the provided information into a generating AI, and the generating AI can automatically record it in the patient's electronic medical record.

[0056] The document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. For example, the document analysis unit can analyze the content of the consent form section by section and highlight the important parts. Furthermore, the document analysis unit can analyze the content of the consent form and highlight the parts that are important to the patient. For example, the document analysis unit can analyze the content of the consent form and highlight the parts that are important to the patient. Furthermore, the document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. For example, the document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. This makes the consent form easier for the patient to understand by highlighting the important parts. Some or all of the above processing in the document analysis unit may be performed using AI, or not. For example, the document analysis unit can input the content of the consent form into a generating AI, which can then analyze it section by section and highlight the important parts.

[0057] The document analysis unit can provide the document analysis results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the document analysis unit can provide the document analysis results concisely. For example, if the patient's level of understanding is low, the document analysis unit can provide the document analysis results concisely. The document analysis unit can also provide the document analysis results in detail if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the document analysis unit can provide the document analysis results in detail. The document analysis unit can also provide the document analysis results in a specialized manner if the patient's level of understanding is high. For example, if the patient's level of understanding is high, the document analysis unit can provide the document analysis results in a specialized manner. This allows for more appropriate information to be provided by providing the results in stages according to the patient's level of understanding. Some or all of the above processing in the document analysis unit may be performed using AI, for example, or without AI. For example, the document analysis unit can input patient understanding data into a generating AI, and the generating AI can provide the document analysis results in stages.

[0058] The document analysis unit can customize the document analysis results to match the patient's native language. For example, if the patient's native language is English, the document analysis unit can customize the document analysis results to English. Similarly, if the patient's native language is Spanish, the document analysis unit can customize the document analysis results to Spanish. Furthermore, if the patient's native language is Chinese, the document analysis unit can customize the document analysis results to Chinese. This customization to match the patient's native language enables the provision of more appropriate information. Some or all of the above-described processes in the document analysis unit may be performed using AI, or without AI. For example, the document analysis unit can input the patient's native language data into a generating AI, which can then customize the document analysis results.

[0059] The document analysis unit can automatically send the document analysis results to the patient's device. For example, the document analysis unit can automatically send the document analysis results to the patient's smartphone. The document analysis unit can also automatically send the document analysis results to the patient's tablet. The document analysis unit can also automatically send the document analysis results to the patient's computer. This makes it easier for patients to receive information by automatically sending the document analysis results. Some or all of the above processing in the document analysis unit may be performed using AI, for example, or without AI. For example, the document analysis unit can input the document analysis results into a generating AI, which can then automatically send them to the patient's device.

[0060] The document translation unit can dynamically update its medical terminology database to accommodate new medical information. For example, if a new medical term is added, the document translation unit can dynamically update the database. For example, if a new medical term is added, the document translation unit can dynamically update the database to reflect it in translations. For example, if a new treatment method is introduced, the document translation unit can dynamically update the database to reflect it in translations. For example, if new medical information is published, the document translation unit can dynamically update the database to utilize it in translations. In this way, by dynamically updating the medical terminology database, the unit can accommodate the latest medical information. Some or all of the above processes in the document translation unit may be performed using AI, for example, or not. For example, the document translation unit can input information on new medical terms and treatment methods into a generating AI, and have the generating AI perform the database update.

[0061] The document translation unit can provide translation results in multiple formats (text, audio, visual). For example, the document translation unit can provide translation results in text format. For example, the document translation unit can provide translation results in text format. The document translation unit can also provide translation results in audio format. For example, the document translation unit can provide translation results in visual format. For example, the document translation unit can provide translation results in visual format. By providing information in multiple formats, patients can receive information in a format that is easy for them to understand. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input translation results into a generating AI, and the generating AI can provide them in text, audio, and visual formats.

[0062] The document translation unit can provide translation results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the document translation unit can provide the translation results concisely. For example, if the patient's level of understanding is low, the document translation unit can provide the translation results concisely. The document translation unit can also provide translation results in detail if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the document translation unit can provide translation results in detail. The document translation unit can also provide translation results in a specialized manner if the patient's level of understanding is high. For example, if the patient's level of understanding is high, the document translation unit can provide translation results in a specialized manner. This allows for the provision of more appropriate information by providing results in stages according to the patient's level of understanding. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input patient understanding data into a generating AI, and the generating AI can provide translation results in stages.

[0063] The document translation unit can customize the translation results to match the patient's native language. For example, if the patient's native language is English, the document translation unit can customize the translation results to English. For example, if the patient's native language is English, the document translation unit can customize the translation results to English. For example, if the patient's native language is Spanish, the document translation unit can customize the translation results to Spanish. For example, if the patient's native language is Chinese, the document translation unit can customize the translation results to Chinese. For example, if the patient's native language is Chinese, the document translation unit can customize the translation results to Chinese. By customizing the results to match the patient's native language, it becomes possible to provide more appropriate information. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input the patient's native language data into a generating AI, and the generating AI can customize the translation results.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The medical explanation translation system can adjust the translation content by incorporating feedback from the patient's family and caregivers. For example, the system can collect feedback from the patient's family and reflect it in subsequent explanations. Furthermore, based on feedback from caregivers, it can focus on explaining aspects that the patient finds particularly difficult to understand. In addition, by using information provided by family and caregivers, the system can more accurately grasp the patient's comprehension level and emotional state, optimizing the translation content. This strengthens support not only for the patient but also for those around them.

[0066] The medical explanation translation system can customize the translation content by considering the patient's lifestyle and educational background. For example, the judgment unit can analyze the patient's lifestyle data and provide explanations using appropriate examples and metaphors. It can also consider the patient's educational background and either avoid using technical jargon or add detailed explanations. Furthermore, it can consider the patient's cultural background and use expressions appropriate to their culture. This enables the provision of appropriate information tailored to each patient's individual background.

[0067] The medical explanation translation system can dynamically update the translation based on the patient's real-time responses. For example, the evaluation unit analyzes the patient's facial expressions and response speed, and if understanding is lacking, it can either simplify the explanation or repeat it. If the patient understands, it can add more detailed information. Furthermore, it can highlight important points based on the patient's responses. This enables the provision of appropriate information in real time, deepening the patient's understanding.

[0068] The medical explanation translation system can assess comprehension by combining the patient's past medical history and current health status, and adjust the translation accordingly. For example, the assessment unit can refer to the patient's past medical records and assess comprehension by comparing them with their current health status. It can also optimize the translation based on the patient's past treatment history, taking into account their current health status. Furthermore, it can integrate the patient's past medical history and current health status to aid in assessing comprehension. This combination of past medical history and current health status improves the accuracy of comprehension assessment.

[0069] The medical explanation translation system can provide translation results in stages according to the patient's level of understanding. For example, if the patient's understanding is low, the translation unit can provide a concise translation. If the patient's understanding is moderate, it can provide a detailed translation. Furthermore, if the patient's understanding is high, it can provide a more specialized translation. This allows for the provision of more appropriate information by providing translations in stages according to the patient's level of understanding.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The assessment unit determines the patient's comprehension level. The assessment unit determines comprehension based on factors such as the patient's age, past medical history, and real-time responses. Specifically, it analyzes the patient's facial expressions and reaction speed to determine comprehension. Step 2: The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the judgment unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology, performing morphological analysis, grammatical analysis, and semantic analysis. Specifically, it analyzes the content of the doctor's explanation based on context and prioritizes extracting information of high importance. Step 3: The translation unit translates the explanations analyzed by the analysis unit. The translation unit improves translation accuracy by, for example, referring to a medical terminology database to translate technical terms into simpler language. The translation unit also dynamically updates the medical terminology database to accommodate new medical information. Step 4: The delivery unit provides the patient with the content translated by the translation unit. The delivery unit provides the translated content visually or audibly, for example. Specifically, it provides the translated content in text, audio, and visual formats and automatically sends it to the patient's device.

[0072] (Example of form 2) The medical explanation translation system according to an embodiment of the present invention is a system that provides translations tailored to the patient's level of understanding when the patient receives an explanation from a doctor during an outpatient visit to a hospital. By providing translations tailored to the patient's level of understanding, this medical explanation translation system can reduce the patient's anxiety and deepen their understanding. Similarly, it also has a function to automatically translate documents such as consent forms according to the patient's level of understanding. For example, when a doctor explains something to a patient, the AI ​​analyzes the content in real time and translates it according to the patient's level of understanding. This makes it easier for the patient to understand the doctor's explanation and reduces their anxiety. Furthermore, the AI ​​also analyzes documents such as consent forms and automatically translates them according to the patient's level of understanding. For example, if a doctor explains, "It's stage 4 cancer. There are options for radiation therapy and surgery, which treatment would you prefer?", this content is input into the AI. Next, the AI ​​analyzes the input explanation and translates it according to the patient's level of understanding. For example, for children and the elderly, technical terms are avoided and the explanation is given in simple language. Specifically, it is translated as, "The cancer is progressing. There are two treatment methods: radiation therapy and surgery. Which would you prefer?" Furthermore, the AI ​​analyzes documents such as consent forms and automatically translates them to match the patient's level of understanding. For example, if a consent form states, "This treatment carries risks," the AI ​​will translate it as, "This treatment is dangerous." This system makes it easier for patients to understand the doctor's explanations and the contents of consent forms, reducing anxiety. It also reduces the burden on accompanying family members. For example, if a doctor explains, "It will attack other parts of the body," the AI ​​will translate it as, "The cancer will spread to other parts of the body." In this way, by providing translations tailored to the other party's level of understanding through AI, misunderstandings between both parties are eliminated, leading to a reduction in anxiety. In addition, since documents such as consent forms are automatically translated, it becomes possible to provide information tailored to the patient's level of understanding. As a result, the medical explanation translation system can reduce patient anxiety and deepen their understanding by providing doctor's explanations tailored to the patient's level of understanding.

[0073] The medical explanation translation system according to this embodiment comprises a determination unit, an analysis unit, a translation unit, and a provision unit. The determination unit determines the patient's comprehension level. The determination unit determines comprehension level based, for example, on the patient's age, past medical history, and real-time reactions. For example, the determination unit can determine comprehension level based on the patient's age. The determination unit can also determine comprehension level based on the patient's past medical history. The determination unit can also determine comprehension level based on real-time reactions. For example, the determination unit analyzes the patient's facial expressions and reaction speed to determine comprehension level. The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the determination unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology. For example, the analysis unit can analyze the doctor's explanation using morphological analysis. The analysis unit can also analyze the doctor's explanation using grammatical analysis. The analysis unit can also analyze the doctor's explanation using semantic analysis. For example, the analysis unit analyzes the content of the doctor's explanation based on context and prioritizes extracting information of high importance. The translation unit translates the explanation content analyzed by the analysis unit. The translation unit performs translations by, for example, referring to a medical terminology database. For example, the translation unit can translate technical terms into simpler language by referring to a medical terminology database. The translation unit can also improve the accuracy of translations by referring to a medical terminology database. Furthermore, the translation unit can dynamically update the medical terminology database to accommodate new medical information. For example, the translation unit dynamically updates the database when new medical terms are added. The delivery unit provides the translated content to the patient. The delivery unit provides the translated content visually or audibly. For example, the delivery unit can provide the translated content in text format. Furthermore, the delivery unit can provide the translated content in audio format. Furthermore, the delivery unit can provide the translated content in visual format. For example, the delivery unit automatically sends the translated content to the patient's device. As a result, the medical explanation translation system according to this embodiment can reduce patient anxiety and deepen their understanding by providing a doctor's explanation tailored to the patient's level of comprehension.

[0074] The assessment unit determines the patient's comprehension level. For example, it determines comprehension based on the patient's age, past medical history, and real-time responses. Specifically, when determining comprehension based on the patient's age, it evaluates general comprehension trends for each age group based on a database. For example, elderly people and children are generally considered to have lower comprehension levels, so simpler explanations are required. When determining comprehension based on past medical history, it considers the treatments and diagnoses the patient has received in the past, as well as their understanding of medical terminology. For example, a patient who has previously received treatment for the same illness is judged to have knowledge about that illness, allowing for more specialized explanations. Furthermore, when determining comprehension based on real-time responses, it analyzes the patient's facial expressions and response speed. For example, the facial expressions the patient shows while listening to an explanation and their response speed to questions are detected by cameras and sensors, and analyzed by AI. The AI ​​uses facial recognition technology to evaluate the patient's emotional state, and if it is determined that the comprehension level is low, simpler explanations are required. In this way, the assessment unit can accurately determine the patient's comprehension level according to their individual circumstances and provide basic information for providing appropriate explanations.

[0075] The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the judgment unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology. Specifically, it analyzes the doctor's explanation using morphological analysis to clarify the meaning and role of each word. Morphological analysis is a technology that breaks down a sentence into individual words and determines the part of speech of each word. This allows for a detailed analysis of the doctor's explanation and the extraction of important information. It is also possible to analyze the doctor's explanation using grammatical analysis. Grammatical analysis is a technology that analyzes the structure of a sentence and clarifies the relationships between subjects, predicates, objects, etc. This allows for an understanding of the context of the doctor's explanation and the accurate grasp of important information. Furthermore, it is also possible to analyze the doctor's explanation using semantic analysis. Semantic analysis is a technology that analyzes the meaning of a sentence and prioritizes the extraction of information of high importance based on the context. For example, it is possible to extract important information and points to note for the patient from the doctor's explanation and provide them in an easy-to-understand format. This allows the analysis unit to appropriately analyze the doctor's explanation based on the comprehension level determined by the judgment unit, and prepare to provide the information in the most easily understandable format for the patient.

[0076] The translation department translates the explanations analyzed by the analysis department. The translation department performs translations by, for example, referring to a medical terminology database. Specifically, it can translate technical terms into simpler language by referring to the medical terminology database. For example, it can replace the technical term "hypertension" with a more general expression such as "a state of high blood pressure." The translation department can also improve the accuracy of translations by referring to the medical terminology database. For example, since the meaning of the same medical term can differ depending on the context, it can provide appropriate translations based on the context. Furthermore, the translation department can dynamically update the medical terminology database to accommodate new medical information. For example, if a new medical term is added, the database can be dynamically updated to provide translations based on the latest information. This allows the translation department to always provide accurate translations based on the latest medical information and present information in a way that is easy for patients to understand. The translation department can also improve translation accuracy using AI. AI can learn from past translation data and continuously improve translation accuracy. This allows the translation department to always provide highly accurate translations, deepening patients' understanding.

[0077] The information delivery unit provides patients with the content translated by the translation unit. The information delivery unit provides the translated content visually or audibly, for example. Specifically, the translated content can be provided in text format. For example, it can be displayed in text format on the patient's smartphone or tablet so that the patient can check it at any time. The information delivery unit can also provide the translated content in audio format. For example, using speech synthesis technology, the translated content can be played back as audio, providing information to patients with visual impairments or those who have difficulty reading text. Furthermore, the information delivery unit can provide the translated content in visual format. For example, medical explanations can be provided visually and clearly using illustrations and diagrams. This allows the information delivery unit to provide information in the most optimal format according to the patient's comprehension level and situation, deepening the patient's understanding. The information delivery unit can also automatically send the translated content to the patient's device. For example, the translated explanation can be automatically sent to the patient's smartphone after the consultation so that the patient can check it later. This allows the information delivery unit to make it easier for patients to review the doctor's explanation after the consultation, deepening their understanding. Furthermore, the information delivery unit can collect feedback from patients and continuously improve the accuracy and effectiveness of the provided content. For example, patients can evaluate their understanding and satisfaction with the information provided, and the content of the information provided can be reviewed based on that feedback. This allows the information provider to always provide the most optimal information for the patient and maximize the effectiveness of medical explanations.

[0078] The document analysis unit can analyze documents such as consent forms. The document analysis unit can analyze documents using, for example, text mining technology. For example, the document analysis unit can analyze the contents of a consent form using text mining technology. The document analysis unit can also analyze documents using natural language processing technology. For example, the document analysis unit can analyze the contents of a consent form using natural language processing technology. This allows the document analysis unit to provide information tailored to the patient's level of understanding by analyzing documents such as consent forms. Some or all of the above-described processes in the document analysis unit may be performed using, for example, AI, or without AI. For example, the document analysis unit can input the contents of a consent form into a generating AI and have the generating AI perform the analysis.

[0079] The document translation unit can translate the content of the consent form analyzed by the document analysis unit. The document translation unit performs translations by referring to, for example, a medical terminology database. For example, the document translation unit can refer to the medical terminology database to translate technical terms into simpler language. The document translation unit can also refer to the medical terminology database to improve the accuracy of the translation. Furthermore, the document translation unit can dynamically update the medical terminology database to accommodate new medical information. For example, the document translation unit dynamically updates the database when new medical terms are added. This allows the document translation unit to improve the patient's understanding by translating the content of the consent form to match the patient's comprehension level. Some or all of the above processes in the document translation unit may be performed using, for example, AI, or not using AI. For example, the document translation unit can input the content of the consent form into a generating AI and have the generating AI perform the translation.

[0080] The assessment unit can determine comprehension based on the patient's age, past medical history, and real-time responses. For example, the assessment unit can determine comprehension based on the patient's age. For example, the assessment unit can take the patient's age as input and determine comprehension based on age. The assessment unit can also determine comprehension based on the patient's past medical history. For example, the assessment unit can take the patient's past medical records as input and determine comprehension based on medical history. The assessment unit can also determine comprehension based on real-time responses. For example, the assessment unit can take the patient's facial expressions and reaction speed as input and determine comprehension based on real-time responses. This allows for a more accurate determination of comprehension by determining comprehension based on the patient's age, past medical history, and real-time responses. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the patient's age, medical history, and real-time response data into a generating AI, and have the generating AI perform the comprehension determination.

[0081] The analysis unit can analyze the doctor's explanation using natural language processing technology. For example, the analysis unit can analyze the doctor's explanation using morphological analysis. For example, the analysis unit can analyze the content of the doctor's explanation using morphological analysis technology. The analysis unit can also analyze the doctor's explanation using grammatical analysis. For example, the analysis unit can analyze the content of the doctor's explanation using grammatical analysis technology. The analysis unit can also analyze the doctor's explanation using semantic analysis. For example, the analysis unit can analyze the content of the doctor's explanation using semantic analysis technology. As a result, the accuracy of the analysis of the doctor's explanation is improved by using natural language processing technology. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the doctor's explanation into a generating AI and have the generating AI perform the analysis.

[0082] The translation unit can perform translations by referring to a database of medical terms. For example, the translation unit can translate technical terms into simpler terms by referring to a database of medical terms. The translation unit can also improve the accuracy of translations by referring to a database of medical terms. The translation unit can also dynamically update the database of medical terms to accommodate new medical information. For example, if a new medical term is added, the translation unit will dynamically update the database. This improves the accuracy of translations by referring to the database of medical terms. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the database of medical terms into a generating AI and have the generating AI perform the translation.

[0083] The delivery unit can provide the translated content to the patient visually or audibly. The delivery unit can, for example, provide the translated content in text format. The delivery unit can also provide the translated content in audio format. The delivery unit can also provide the translated content in visual format. This helps the patient understand the translated content by providing it visually or audibly. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the translated content into a generating AI, which can then provide it visually or audibly.

[0084] The judgment unit can estimate the patient's emotions and improve the accuracy of comprehension assessment based on the estimated emotions. For example, the judgment unit can estimate emotions by analyzing the patient's facial expressions. For example, the judgment unit can capture the patient's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The judgment unit can also estimate emotions by analyzing the patient's voice. For example, the judgment unit can record the patient's voice and estimate emotions using voice analysis technology. The judgment unit can also estimate emotions by analyzing the patient's biometric data. For example, the judgment unit can collect the patient's heart rate and skin electrical activity with sensors and estimate emotions using an emotion estimation algorithm. This improves the accuracy of comprehension assessment based on the patient's emotions, enabling the provision of more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the patient's facial expression data into a generating AI, which can then perform emotion estimation.

[0085] The assessment unit can determine comprehension by combining the patient's past medical history and current health status. For example, the assessment unit can refer to the patient's past medical records and determine comprehension by comparing them with the current health status. For example, the assessment unit can take the patient's past medical records as input and determine comprehension based on the current health status. The assessment unit can also determine comprehension by considering the current health status based on the patient's past treatment history. For example, the assessment unit can take the patient's past treatment history as input and determine comprehension based on the current health status. The assessment unit can also integrate the patient's past medical history and current health status to help determine comprehension. For example, the assessment unit can take the patient's past medical history and current health status as input and integrate them to determine comprehension. This improves the accuracy of comprehension determination by combining past medical history and current health status. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input data on the patient's past medical history and current health status into a generating AI and have the generating AI perform the comprehension determination.

[0086] The assessment unit can analyze the patient's real-time reaction speed and facial expressions and dynamically update their comprehension level. For example, the assessment unit can analyze the patient's facial expressions using a camera and reflect this in the assessment of comprehension level. For example, the assessment unit can capture the patient's facial expressions with a camera and analyze them using facial expression analysis technology. The assessment unit can also measure the patient's reaction speed with a sensor and use this to assess comprehension level. For example, the assessment unit can measure the patient's reaction speed with a sensor and determine comprehension level based on the reaction speed. The assessment unit can also analyze the patient's real-time reactions and dynamically update their comprehension level. For example, the assessment unit can take the patient's real-time reaction data as input and dynamically update their comprehension level. This makes it possible to dynamically update comprehension level by analyzing real-time reaction speed and facial expressions. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input the patient's real-time reaction data into a generating AI and have the generating AI perform the dynamic update of comprehension level.

[0087] The assessment unit can estimate the patient's emotions and adjust the comprehension assessment result based on the estimated emotions. For example, the assessment unit can analyze the patient's facial expressions to estimate emotions and adjust the comprehension assessment result. For example, the assessment unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the comprehension assessment result. The assessment unit can also analyze the patient's voice to estimate emotions and adjust the comprehension assessment result. For example, the assessment unit can record the patient's voice, estimate emotions using voice analysis technology, and adjust the comprehension assessment result. The assessment unit can also analyze the patient's biometric data to estimate emotions and adjust the comprehension assessment result. For example, the assessment unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the comprehension assessment result. This allows for the provision of more appropriate information by adjusting the comprehension assessment result based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the judgment unit may be performed using AI, or not using AI. For example, the judgment unit may input patient facial expression data into the generating AI, have the generating AI perform emotion estimation, and adjust the comprehension assessment result.

[0088] The assessment unit can determine comprehension by considering the patient's lifestyle and educational background. For example, the assessment unit can analyze the patient's lifestyle and reflect it in the assessment of comprehension. For example, the assessment unit can take the patient's lifestyle as input and determine comprehension based on that lifestyle. The assessment unit can also analyze the patient's educational background and use it in determining comprehension. For example, the assessment unit can take the patient's educational background as input and determine comprehension based on that educational background. The assessment unit can also integrate the patient's lifestyle and educational background to aid in determining comprehension. For example, the assessment unit can take the patient's lifestyle and educational background as input, integrate them, and determine comprehension. This improves the accuracy of comprehension assessment by considering lifestyle and educational background. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input data on the patient's lifestyle and educational background into a generating AI and have the generating AI perform the comprehension assessment.

[0089] The assessment unit can determine comprehension by incorporating feedback from the patient's family and caregivers. For example, the assessment unit can analyze feedback from the patient's family and reflect it in the assessment of comprehension. For example, the assessment unit can take feedback from the patient's family as input and determine comprehension based on that feedback. The assessment unit can also analyze feedback from the patient's caregivers and use it in determining comprehension. For example, the assessment unit can take feedback from the patient's caregivers as input and determine comprehension based on that feedback. The assessment unit can also integrate feedback from the patient's family and caregivers and use it to help determine comprehension. For example, the assessment unit can take feedback from the patient's family and caregivers as input, integrate it, and determine comprehension. This improves the accuracy of comprehension assessment by incorporating feedback from family and caregivers. Some or all of the above processing in the assessment unit may be performed using AI, for example, or without AI. For example, the assessment unit can input feedback data from the patient's family and caregivers into a generating AI and have the generating AI perform the comprehension assessment.

[0090] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, the analysis unit can analyze the patient's facial expressions to estimate emotions and adjust the presentation of the analysis results. For example, the analysis unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the presentation of the analysis results. The analysis unit can also analyze the patient's voice to estimate emotions and adjust the presentation of the analysis results. For example, the analysis unit can record the patient's voice, estimate emotions using voice analysis technology, and adjust the presentation of the analysis results. The analysis unit can also analyze the patient's biometric data to estimate emotions and adjust the presentation of the analysis results. For example, the analysis unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the presentation of the analysis results. By adjusting the presentation of the analysis results based on the patient's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input patient facial expression data into the generating AI, have the generating AI perform emotion estimation, and adjust the way the analysis results are expressed.

[0091] The analysis unit can analyze the physician's explanation based on context and prioritize extracting information of high importance. For example, the analysis unit can analyze the physician's explanation using contextual analysis techniques and prioritize extracting important information. Furthermore, the analysis unit can analyze the physician's explanation and highlight information that is important to the patient. For example, the analysis unit can analyze the physician's explanation and highlight information that is important to the patient. Furthermore, the analysis unit can analyze the physician's explanation based on context and extract information of high importance. This allows for the provision of important information to the patient by extracting information of high importance based on context. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the physician's explanation into a generating AI, have the generating AI perform contextual analysis, and extract important information.

[0092] The analysis unit can analyze the physician's explanation in multiple languages ​​and accommodate patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and accommodate patients with different cultural backgrounds. Furthermore, the analysis unit can analyze the physician's explanation in multiple languages ​​and provide it to patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and provide it to patients with different cultural backgrounds. Furthermore, the analysis unit can analyze the physician's explanation in multiple languages ​​and adapt it to patients with different cultural backgrounds. For example, the analysis unit can analyze the physician's explanation in multiple languages ​​and adapt it to patients with different cultural backgrounds. This allows for analysis in multiple languages, thus accommodating patients with different cultural backgrounds. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the physician's explanation into a generating AI, which can then perform the analysis in multiple languages.

[0093] The analysis unit can estimate the patient's emotions and determine the priority of the analysis results based on the estimated emotions. For example, the analysis unit can analyze the patient's facial expressions to estimate emotions and determine the priority of the analysis results. For example, the analysis unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and determine the priority of the analysis results. The analysis unit can also analyze the patient's voice to estimate emotions and determine the priority of the analysis results. For example, the analysis unit can record the patient's voice, estimate emotions using voice analysis technology, and determine the priority of the analysis results. The analysis unit can also analyze the patient's biometric data to estimate emotions and determine the priority of the analysis results. For example, the analysis unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and determine the priority of the analysis results. This allows for the provision of more appropriate information by determining the priority of the analysis results based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input patient facial expression data into the generating AI, have the generating AI perform emotion estimation, and determine the priority of the analysis results.

[0094] The analysis unit can analyze the doctor's explanation as audio data and convert it to text using speech recognition technology. For example, the analysis unit can analyze the doctor's explanation as audio data and convert it to text using speech recognition technology. The analysis unit can also analyze the doctor's explanation as audio data, convert it to text, and provide it to the patient. The analysis unit can also analyze the doctor's explanation as audio data, convert it to text using speech recognition technology, and save it. This makes it easier to record the doctor's explanation by converting the audio data to text. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the audio data of the doctor's explanation into a generating AI, have the generating AI perform speech recognition, and convert it to text.

[0095] The analysis unit can analyze the doctor's explanation as visual data and generate diagrams and illustrations. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams. The analysis unit can also analyze the doctor's explanation as visual data and generate illustrations. For example, the analysis unit can analyze the doctor's explanation as visual data and generate illustrations. The analysis unit can also analyze the doctor's explanation as visual data and generate diagrams and illustrations to provide to the patient. For example, the analysis unit can analyze the doctor's explanation as visual data and generate diagrams and illustrations to provide to the patient. This makes it easier for patients to understand visually by generating visual data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the doctor's explanation into a generation AI and have the generation AI generate visual data.

[0096] The translation unit can estimate the patient's emotions and adjust the tone and style of the translation based on those estimated emotions. For example, the translation unit can analyze the patient's facial expressions to estimate emotions and adjust the tone and style of the translation. For instance, it can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the tone and style of the translation. The translation unit can also analyze the patient's voice to estimate emotions and adjust the tone and style of the translation. For example, it can record the patient's voice, estimate emotions using voice analysis technology, and adjust the tone and style of the translation. Furthermore, the translation unit can analyze the patient's biometric data to estimate emotions and adjust the tone and style of the translation. For example, it can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the tone and style of the translation. This allows for more appropriate information to be provided by adjusting the tone and style of the translation based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit may input patient facial expression data into the generative AI, have the generative AI perform emotion estimation, and adjust the tone and style of the translation.

[0097] The translation unit can dynamically update its medical terminology database to accommodate new medical information. For example, if a new medical term is added, the translation unit can dynamically update the database. The translation unit can also dynamically update the database and reflect new treatments in its translations. The translation unit can also dynamically update its database and use it in translations when new medical information is published. This allows the translation unit to keep up with the latest medical information by dynamically updating its medical terminology database. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information on new medical terms and treatments into a generating AI, which can then perform the database update.

[0098] The translation unit can provide translation results in multiple formats (text, audio, visual). For example, the translation unit can provide translation results in text format. For example, the translation unit can provide translation results in text format. The translation unit can also provide translation results in audio format. For example, the translation unit can provide translation results in audio format. The translation unit can also provide translation results in visual format. For example, the translation unit can provide translation results in visual format. By providing information in multiple formats, patients can receive information in a format that is easy for them to understand. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input translation results into a generating AI, and the generating AI can provide them in text, audio, and visual formats.

[0099] The translation unit can estimate the patient's emotions and adjust the level of detail in the translation based on the estimated emotions. For example, the translation unit can analyze the patient's facial expressions to estimate emotions and adjust the level of detail in the translation. For example, the translation unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the level of detail in the translation. The translation unit can also analyze the patient's voice to estimate emotions and adjust the level of detail in the translation. For example, the translation unit can record the patient's voice, estimate emotions using voice analysis technology, and adjust the level of detail in the translation. The translation unit can also analyze the patient's biometric data to estimate emotions and adjust the level of detail in the translation. For example, the translation unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the level of detail in the translation. This allows for the provision of more appropriate information by adjusting the level of detail in the translation based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input patient facial expression data into a generating AI, have the generating AI perform emotion estimation, and adjust the level of detail in the translation.

[0100] The translation unit can provide translation results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the translation unit can provide concise translation results. For example, if the patient's level of understanding is low, the translation unit can provide concise translation results. The translation unit can also provide detailed translation results if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the translation unit can provide detailed translation results. For example, if the patient's level of understanding is high, the translation unit can provide specialized translation results. For example, if the patient's level of understanding is high, the translation unit can provide specialized translation results. This allows for more appropriate information to be provided by providing translation results in stages according to the patient's level of understanding. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input patient understanding data into a generating AI, and the generating AI can provide translation results in stages.

[0101] The translation unit can customize the translation results to match the patient's native language. For example, if the patient's native language is English, the translation unit can customize the translation results to English. For example, if the patient's native language is English, the translation unit can customize the translation results to English. Also, if the patient's native language is Spanish, the translation unit can customize the translation results to Spanish. For example, if the patient's native language is Chinese, the translation unit can customize the translation results to Chinese. This allows for the provision of more appropriate information by customizing the translation results to match the patient's native language. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the patient's native language data into a generating AI, and the generating AI can customize the translation results.

[0102] The information delivery unit can estimate the patient's emotions and adjust the delivery method based on the estimated emotions. For example, the information delivery unit can analyze the patient's facial expressions to estimate emotions and adjust the delivery method. For example, the information delivery unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the delivery method. The information delivery unit can also analyze the patient's voice to estimate emotions and adjust the delivery method. For example, the information delivery unit can record the patient's voice, estimate emotions using voice analysis technology, and adjust the delivery method. The information delivery unit can also analyze the patient's biometric data to estimate emotions and adjust the delivery method. For example, the information delivery unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the delivery method. This allows for more appropriate information delivery by adjusting the delivery method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input patient facial expression data into a generating AI, have the generating AI perform emotion estimation, and adjust the delivery method.

[0103] The delivery unit can automatically send the translated content to the patient's device. For example, the delivery unit can automatically send the translated content to the patient's smartphone. The delivery unit can also automatically send the translated content to the patient's tablet. For example, the delivery unit can automatically send the translated content to the patient's tablet. The delivery unit can also automatically send the translated content to the patient's computer. For example, the delivery unit can automatically send the translated content to the patient's computer. This makes it easier for the patient to receive the information by automatically sending the translated content. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the translated content into a generating AI, and the generating AI can automatically send it to the patient's device.

[0104] The service provider can collect patient feedback on the information provided and reflect it in the next service provision method. For example, the service provider can collect patient feedback on the information provided and reflect it in the next service provision method. For example, the service provider can collect patient feedback on the information provided and reflect it in the next service provision method. The service provider can also analyze patient feedback on the information provided and improve the next service provision method. For example, the service provider can analyze patient feedback on the information provided and improve the next service provision method. The service provider can also collect patient feedback on the information provided and use it to improve the next service provision method. For example, the service provider can collect patient feedback on the information provided and use it to improve the next service provision method. By collecting patient feedback, the service provider can improve the next service provision method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input patient feedback data into a generating AI, and the generating AI can improve the next service provision method.

[0105] The service provider can estimate the patient's emotions and determine the priority of the services offered based on those estimated emotions. For example, the service provider can analyze the patient's facial expressions to estimate emotions and determine the priority of the services offered. For example, the service provider can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and determine the priority of the services offered. The service provider can also analyze the patient's voice to estimate emotions and determine the priority of the services offered. For example, the service provider can record the patient's voice, estimate emotions using voice analysis technology, and determine the priority of the services offered. The service provider can also analyze the patient's biometric data to estimate emotions and determine the priority of the services offered. For example, the service provider can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and determine the priority of the services offered. This allows for more appropriate information to be provided by prioritizing the services offered based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the delivery unit may be performed using AI, or not using AI. For example, the delivery unit may input patient facial expression data into the generating AI, have the generating AI perform emotion estimation, and determine the priority of the content to be delivered.

[0106] The information provider can also share the provided information with the patient's family and caregivers. For example, the information provider can automatically share the provided information with the patient's family. For example, the information provider can automatically share the provided information with the patient's family. The information provider can also automatically share the provided information with the patient's caregivers. For example, the information provider can automatically share the provided information with the patient's caregivers. The information provider can also automatically share the provided information with the patient's family and caregivers. For example, the information provider can automatically share the provided information with the patient's family and caregivers. This makes it easier to support the patient by sharing the information with family and caregivers. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the provided information into a generating AI, and the generating AI can automatically share it with the patient's family and caregivers.

[0107] The information provider can automatically record the provided information in the patient's electronic medical record. For example, the information provider can automatically record the provided information in the patient's electronic medical record. For example, the information provider can automatically record the provided information in the patient's electronic medical record. The information provider can also automatically record the provided information in the patient's medical record. For example, the information provider can automatically record the provided information in the patient's medical record. The information provider can also automatically record the provided information in the patient's medical database. For example, the information provider can automatically record the provided information in the patient's medical database. This makes it easier to manage medical information by automatically recording the information in the electronic medical record. Some or all of the above processing in the information provider can be performed using AI, for example, or without AI. For example, the information provider can input the provided information into a generating AI, and the generating AI can automatically record it in the patient's electronic medical record.

[0108] The document analysis unit can estimate a patient's emotions and adjust the document analysis results based on the estimated emotions. For example, the document analysis unit can analyze a patient's facial expressions to estimate emotions and adjust the document analysis results. For example, the document analysis unit can capture a patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the document analysis results. The document analysis unit can also analyze a patient's voice to estimate emotions and adjust the document analysis results. For example, the document analysis unit can record a patient's voice, estimate emotions using voice analysis technology, and adjust the document analysis results. The document analysis unit can also analyze a patient's biometric data to estimate emotions and adjust the document analysis results. For example, the document analysis unit can collect a patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the document analysis results. This allows for the provision of more appropriate information by adjusting the document analysis results based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generation AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the document analysis unit may be performed using AI, or not using AI. For example, the document analysis unit can input patient facial expression data into the generating AI, have the generating AI perform emotion estimation, and adjust the document analysis results.

[0109] The document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. For example, the document analysis unit can analyze the content of the consent form section by section and highlight the important parts. Furthermore, the document analysis unit can analyze the content of the consent form and highlight the parts that are important to the patient. For example, the document analysis unit can analyze the content of the consent form and highlight the parts that are important to the patient. Furthermore, the document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. For example, the document analysis unit can analyze the content of the consent form section by section and highlight the most important parts. This makes the consent form easier for the patient to understand by highlighting the important parts. Some or all of the above processing in the document analysis unit may be performed using AI, or not. For example, the document analysis unit can input the content of the consent form into a generating AI, which can then analyze it section by section and highlight the important parts.

[0110] The document analysis unit can provide the document analysis results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the document analysis unit can provide the document analysis results concisely. For example, if the patient's level of understanding is low, the document analysis unit can provide the document analysis results concisely. The document analysis unit can also provide the document analysis results in detail if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the document analysis unit can provide the document analysis results in detail. The document analysis unit can also provide the document analysis results in a specialized manner if the patient's level of understanding is high. For example, if the patient's level of understanding is high, the document analysis unit can provide the document analysis results in a specialized manner. This allows for more appropriate information to be provided by providing the results in stages according to the patient's level of understanding. Some or all of the above processing in the document analysis unit may be performed using AI, for example, or without AI. For example, the document analysis unit can input patient understanding data into a generating AI, and the generating AI can provide the document analysis results in stages.

[0111] The document analysis unit can estimate a patient's emotions and determine the priority of document analysis results based on the estimated emotions. For example, the document analysis unit can analyze a patient's facial expressions to estimate emotions and determine the priority of document analysis results. For example, the document analysis unit can capture a patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and determine the priority of document analysis results. The document analysis unit can also analyze a patient's voice to estimate emotions and determine the priority of document analysis results. For example, the document analysis unit can record a patient's voice, estimate emotions using voice analysis technology, and determine the priority of document analysis results. Furthermore, the document analysis unit can analyze a patient's biometric data to estimate emotions and determine the priority of document analysis results. For example, the document analysis unit can collect a patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and determine the priority of document analysis results. This allows for the provision of more appropriate information by prioritizing document analysis results based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document analysis unit may be performed using AI or not using AI. For example, the document analysis unit can input patient facial expression data into a generative AI, have the generative AI perform emotion estimation, and determine the priority of the document analysis results.

[0112] The document analysis unit can customize the document analysis results to match the patient's native language. For example, if the patient's native language is English, the document analysis unit can customize the document analysis results to English. Similarly, if the patient's native language is Spanish, the document analysis unit can customize the document analysis results to Spanish. Furthermore, if the patient's native language is Chinese, the document analysis unit can customize the document analysis results to Chinese. This customization to match the patient's native language enables the provision of more appropriate information. Some or all of the above-described processes in the document analysis unit may be performed using AI, or without AI. For example, the document analysis unit can input the patient's native language data into a generating AI, which can then customize the document analysis results.

[0113] The document analysis unit can automatically send the document analysis results to the patient's device. For example, the document analysis unit can automatically send the document analysis results to the patient's smartphone. The document analysis unit can also automatically send the document analysis results to the patient's tablet. The document analysis unit can also automatically send the document analysis results to the patient's computer. This makes it easier for patients to receive information by automatically sending the document analysis results. Some or all of the above processing in the document analysis unit may be performed using AI, for example, or without AI. For example, the document analysis unit can input the document analysis results into a generating AI, which can then automatically send them to the patient's device.

[0114] The document translation unit can estimate the patient's emotions and adjust the tone and style of the translation based on the estimated emotions. For example, the document translation unit can analyze the patient's facial expressions to estimate emotions and adjust the tone and style of the translation. For example, the document translation unit can capture the patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the tone and style of the translation. The document translation unit can also analyze the patient's voice to estimate emotions and adjust the tone and style of the translation. For example, the document translation unit can record the patient's voice, estimate emotions using voice analysis technology, and adjust the tone and style of the translation. The document translation unit can also analyze the patient's biometric data to estimate emotions and adjust the tone and style of the translation. For example, the document translation unit can collect the patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the tone and style of the translation. This allows for the provision of more appropriate information by adjusting the tone and style of the translation based on the patient's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the document translation unit may be performed using AI, for example, or not using AI. For example, the document translation unit may input patient facial expression data into the generative AI, have the generative AI perform emotion estimation, and adjust the tone and style of the translation.

[0115] The document translation unit can dynamically update its medical terminology database to accommodate new medical information. For example, if a new medical term is added, the document translation unit can dynamically update the database. For example, if a new medical term is added, the document translation unit can dynamically update the database to reflect it in translations. For example, if a new treatment method is introduced, the document translation unit can dynamically update the database to reflect it in translations. For example, if new medical information is published, the document translation unit can dynamically update the database to utilize it in translations. In this way, by dynamically updating the medical terminology database, the unit can accommodate the latest medical information. Some or all of the above processes in the document translation unit may be performed using AI, for example, or not. For example, the document translation unit can input information on new medical terms and treatment methods into a generating AI, and have the generating AI perform the database update.

[0116] The document translation unit can provide translation results in multiple formats (text, audio, visual). For example, the document translation unit can provide translation results in text format. For example, the document translation unit can provide translation results in text format. The document translation unit can also provide translation results in audio format. For example, the document translation unit can provide translation results in visual format. For example, the document translation unit can provide translation results in visual format. By providing information in multiple formats, patients can receive information in a format that is easy for them to understand. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input translation results into a generating AI, and the generating AI can provide them in text, audio, and visual formats.

[0117] The document translation unit can estimate a patient's emotions and adjust the level of detail in the translation based on the estimated emotions. For example, the document translation unit can analyze a patient's facial expressions to estimate emotions and adjust the level of detail in the translation. For example, the document translation unit can capture a patient's facial expressions with a camera, estimate emotions using an emotion estimation algorithm, and adjust the level of detail in the translation. The document translation unit can also analyze a patient's voice to estimate emotions and adjust the level of detail in the translation. For example, the document translation unit can record a patient's voice, estimate emotions using voice analysis technology, and adjust the level of detail in the translation. The document translation unit can also analyze a patient's biometric data to estimate emotions and adjust the level of detail in the translation. For example, the document translation unit can collect a patient's heart rate and skin electrical activity with sensors, estimate emotions using an emotion estimation algorithm, and adjust the level of detail in the translation. By adjusting the level of detail in the translation based on the patient's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the document translation unit may be performed using AI, or not using AI. For example, the document translation unit may input patient facial expression data into the generative AI, have the generative AI perform emotion estimation, and adjust the level of detail of the translation.

[0118] The document translation unit can provide translation results in stages according to the patient's level of understanding. For example, if the patient's level of understanding is low, the document translation unit can provide the translation results concisely. For example, if the patient's level of understanding is low, the document translation unit can provide the translation results concisely. The document translation unit can also provide translation results in detail if the patient's level of understanding is moderate. For example, if the patient's level of understanding is moderate, the document translation unit can provide translation results in detail. The document translation unit can also provide translation results in a specialized manner if the patient's level of understanding is high. For example, if the patient's level of understanding is high, the document translation unit can provide translation results in a specialized manner. This allows for the provision of more appropriate information by providing results in stages according to the patient's level of understanding. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input patient understanding data into a generating AI, and the generating AI can provide translation results in stages.

[0119] The document translation unit can customize the translation results to match the patient's native language. For example, if the patient's native language is English, the document translation unit can customize the translation results to English. For example, if the patient's native language is English, the document translation unit can customize the translation results to English. For example, if the patient's native language is Spanish, the document translation unit can customize the translation results to Spanish. For example, if the patient's native language is Chinese, the document translation unit can customize the translation results to Chinese. For example, if the patient's native language is Chinese, the document translation unit can customize the translation results to Chinese. By customizing the results to match the patient's native language, it becomes possible to provide more appropriate information. Some or all of the above processing in the document translation unit may be performed using AI, for example, or without AI. For example, the document translation unit can input the patient's native language data into a generating AI, and the generating AI can customize the translation results.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The medical explanation translation system can adjust the translation content considering not only the patient's comprehension level but also their emotional state. For example, the assessment unit analyzes the patient's facial expressions and voice to estimate their emotions, and if the patient is feeling anxious or tense, it can explain things in a gentler tone. If the patient is relaxed, it can provide more detailed information. Furthermore, if the patient is confused, it can repeatedly emphasize important points. This enables the provision of appropriate information tailored to the patient's emotional state, thereby deepening their understanding.

[0122] The medical explanation translation system can adjust the translation content by incorporating feedback from the patient's family and caregivers. For example, the system can collect feedback from the patient's family and reflect it in subsequent explanations. Furthermore, based on feedback from caregivers, it can focus on explaining aspects that the patient finds particularly difficult to understand. In addition, by using information provided by family and caregivers, the system can more accurately grasp the patient's comprehension level and emotional state, optimizing the translation content. This strengthens support not only for the patient but also for those around them.

[0123] The medical explanation translation system can customize the translation content by considering the patient's lifestyle and educational background. For example, the judgment unit can analyze the patient's lifestyle data and provide explanations using appropriate examples and metaphors. It can also consider the patient's educational background and either avoid using technical jargon or add detailed explanations. Furthermore, it can consider the patient's cultural background and use expressions appropriate to their culture. This enables the provision of appropriate information tailored to each patient's individual background.

[0124] The medical explanation translation system can dynamically update the translation based on the patient's real-time responses. For example, the evaluation unit analyzes the patient's facial expressions and response speed, and if understanding is lacking, it can either simplify the explanation or repeat it. If the patient understands, it can add more detailed information. Furthermore, it can highlight important points based on the patient's responses. This enables the provision of appropriate information in real time, deepening the patient's understanding.

[0125] A medical explanation translation system can estimate a patient's emotions and adjust the tone and style of the translation based on those estimated emotions. For example, the translation unit can analyze the patient's facial expressions to estimate their emotions and, if the patient is feeling anxious, can provide explanations in a gentler tone. If the patient is relaxed, it can provide detailed information. Furthermore, if the patient is confused, it can repeatedly emphasize important points. This enables the provision of appropriate information tailored to the patient's emotions, thereby deepening their understanding.

[0126] The medical explanation translation system can assess comprehension by combining the patient's past medical history and current health status, and adjust the translation accordingly. For example, the assessment unit can refer to the patient's past medical records and assess comprehension by comparing them with their current health status. It can also optimize the translation based on the patient's past treatment history, taking into account their current health status. Furthermore, it can integrate the patient's past medical history and current health status to aid in assessing comprehension. This combination of past medical history and current health status improves the accuracy of comprehension assessment.

[0127] A medical explanation translation system can estimate a patient's emotions and adjust its delivery method based on those emotions. For example, the delivery unit can analyze the patient's facial expressions to estimate their emotions and, if the patient is feeling anxious, can deliver the explanation in a gentle tone. If the patient is relaxed, it can provide detailed information. Furthermore, if the patient is confused, it can repeatedly emphasize important points. This enables the delivery of appropriate information tailored to the patient's emotions, thereby deepening their understanding.

[0128] The medical explanation translation system can provide translation results in stages according to the patient's level of understanding. For example, if the patient's understanding is low, the translation unit can provide a concise translation. If the patient's understanding is moderate, it can provide a detailed translation. Furthermore, if the patient's understanding is high, it can provide a more specialized translation. This allows for the provision of more appropriate information by providing translations in stages according to the patient's level of understanding.

[0129] The medical explanation translation system can estimate a patient's emotions and prioritize the content provided based on those emotions. For example, the system can analyze the patient's facial expressions to estimate their emotions and, if the patient is feeling anxious, prioritize providing important information. If the patient is relaxed, it can provide detailed information. Furthermore, if the patient is confused, it can repeatedly emphasize key points. This enables the provision of appropriate information tailored to the patient's emotions, thereby deepening their understanding.

[0130] The medical explanation translation system can estimate a patient's emotions and adjust the document analysis results based on those estimated emotions. For example, the document analysis unit can analyze the patient's facial expressions to estimate their emotions, and if the patient is feeling anxious, it can prioritize highlighting important information. If the patient is relaxed, it can provide detailed information. Furthermore, if the patient is confused, it can repeatedly emphasize important points. This enables the provision of appropriate information tailored to the patient's emotions, thereby deepening their understanding.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The assessment unit determines the patient's comprehension level. The assessment unit determines comprehension based on factors such as the patient's age, past medical history, and real-time responses. Specifically, it analyzes the patient's facial expressions and reaction speed to determine comprehension. Step 2: The analysis unit analyzes the doctor's explanation based on the comprehension level determined by the judgment unit. The analysis unit analyzes the doctor's explanation using, for example, natural language processing technology, performing morphological analysis, grammatical analysis, and semantic analysis. Specifically, it analyzes the content of the doctor's explanation based on context and prioritizes extracting information of high importance. Step 3: The translation unit translates the explanations analyzed by the analysis unit. The translation unit improves translation accuracy by, for example, referring to a medical terminology database to translate technical terms into simpler language. The translation unit also dynamically updates the medical terminology database to accommodate new medical information. Step 4: The delivery unit provides the patient with the content translated by the translation unit. The delivery unit provides the translated content visually or audibly, for example. Specifically, it provides the translated content in text, audio, and visual formats and automatically sends it to the patient's device.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0136] Each of the multiple elements described above, including the determination unit, analysis unit, translation unit, provision unit, and document analysis unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the determination unit is implemented by the control unit 46A of the smart device 14 and determines comprehension based on the patient's age, past medical history, and real-time responses. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the doctor's explanation using natural language processing technology. The translation unit is implemented by the specific processing unit 290 of the data processing device 12 and performs translation by referring to a database of medical terms. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the translated content visually or audibly. The document analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes documents such as consent forms using text mining technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the determination unit, analysis unit, translation unit, provision unit, and document analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the determination unit is implemented by the control unit 46A of the smart glasses 214 and determines comprehension based on the patient's age, past medical history, and real-time responses. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the doctor's explanation using natural language processing technology. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs translation by referring to a database of medical terms. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the translated content visually or audibly. The document analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes documents such as consent forms using text mining technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the determination unit, analysis unit, translation unit, provision unit, and document analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the determination unit is implemented by the control unit 46A of the headset terminal 314 and determines comprehension based on the patient's age, past medical history, and real-time responses. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the doctor's explanation using natural language processing technology. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs translation by referring to a database of medical terms. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the translated content visually or audibly. The document analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes documents such as consent forms using text mining technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the determination unit, analysis unit, translation unit, provision unit, and document analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the determination unit is implemented by the control unit 46A of the robot 414 and determines comprehension based on the patient's age, past medical history, and real-time responses. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the doctor's explanation using natural language processing technology. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs translation by referring to a database of medical terms. The provision unit is implemented by the control unit 46A of the robot 414 and provides the translated content visually or audibly. The document analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes documents such as consent forms using text mining technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A judgment unit that assesses the patient's comprehension level, An analysis unit analyzes the doctor's explanation based on the level of understanding determined by the aforementioned determination unit, A translation unit that translates the explanatory content analyzed by the aforementioned analysis unit, The system comprises a provisioning unit that provides the content translated by the translation unit to the patient. A system characterized by the following features. (Note 2) It includes a document analysis unit for analyzing documents such as consent forms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned document analysis unit, It includes a document translation department that analyzes the content of consent forms and translates them in a way that is appropriate for the patient's understanding. The system described in Appendix 2, characterized by the features described herein. (Note 4) The determination unit, Comprehension is assessed based on the patient's age, past medical history, and real-time responses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the doctor's explanation using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, Translate by referring to a medical terminology database. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the translated content to the patient visually or audibly. The system described in Appendix 1, characterized by the features described herein. (Note 8) The determination unit, It estimates the patient's emotions and improves the accuracy of comprehension assessments based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The determination unit, Comprehension is assessed by combining the patient's past medical history and current health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The determination unit, Analyzes the patient's reaction speed and facial expressions in real time to dynamically update comprehension. The system described in Appendix 1, characterized by the features described herein. (Note 11) The determination unit, The system estimates the patient's emotions and adjusts the comprehension assessment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The determination unit, Assessing comprehension level by considering the patient's lifestyle and educational background. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, Comprehension is assessed by incorporating feedback from the patient's family and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the presentation of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system analyzes the doctor's explanation based on context and prioritizes extracting the most important information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system analyzes the doctor's explanation in multiple languages ​​to accommodate patients with different cultural backgrounds. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the patient's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The doctor's explanation is analyzed as audio data and converted into text using speech recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system analyzes the doctor's explanation as visual data and generates diagrams and illustrations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned translation department, The system estimates the patient's emotions and adjusts the tone and style of the translation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned translation department, The medical terminology database is dynamically updated to accommodate new medical information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned translation department, Provide translation results in multiple formats. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned translation department, The system estimates the patient's emotions and adjusts the level of detail in the translation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned translation department, Translation results are provided in stages according to the patient's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned translation department, Customize the translation results to match the patient's native language. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the patient's emotions and adjusts the delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The translated content is automatically sent to the patient's device. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We collect patient feedback on the information provided and use it to improve how we provide it in the future. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes the services provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The information provided will also be shared with the patient's family and caregivers. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The provided information is automatically recorded in the patient's electronic medical record. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned document analysis unit, The system estimates the patient's emotions and adjusts the document analysis results based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned document analysis unit, The consent form's contents are analyzed section by section, and the most important parts are highlighted. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned document analysis unit, The results of the document analysis will be provided in stages according to the patient's level of understanding. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned document analysis unit, The system estimates the patient's emotions and prioritizes the document analysis results based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned document analysis unit, Customize the document analysis results to match the patient's native language. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned document analysis unit, The document analysis results are automatically sent to the patient's device. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned document translation department, The system estimates the patient's emotions and adjusts the tone and style of the translation based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned document translation department, The medical terminology database is dynamically updated to accommodate new medical information. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned document translation department, Provide translation results in multiple formats. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned document translation department, The system estimates the patient's emotions and adjusts the level of detail in the translation based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned document translation department, Translation results are provided in stages according to the patient's level of understanding. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned document translation department, Customize the translation results to match the patient's native language. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A judgment unit that assesses the patient's comprehension level, An analysis unit analyzes the doctor's explanation based on the level of understanding determined by the aforementioned determination unit, A translation unit that translates the explanatory content analyzed by the aforementioned analysis unit, The system comprises a provisioning unit that provides the content translated by the translation unit to the patient. A system characterized by the following features.

2. It includes a document analysis unit for analyzing documents such as consent forms. The system according to feature 1.

3. The aforementioned document analysis unit, It includes a document translation department that analyzes the content of consent forms and translates them in a way that is appropriate for the patient's understanding. The system according to feature 2.

4. The determination unit, Comprehension is assessed based on the patient's age, past medical history, and real-time responses. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the doctor's explanation using natural language processing technology. The system according to feature 1.

6. The aforementioned translation department, Translate by referring to a medical terminology database. The system according to feature 1.

7. The aforementioned supply unit is, Provide the translated content to the patient visually or audibly. The system according to feature 1.

8. The determination unit, It estimates the patient's emotions and improves the accuracy of comprehension assessments based on the estimated emotions. The system according to feature 1.

9. The determination unit, Comprehension is assessed by combining the patient's past medical history and current health status. The system according to feature 1.

10. The determination unit, Analyzes the patient's reaction speed and facial expressions in real time to dynamically update comprehension. The system according to feature 1.

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