system

Abstract

The system according to this embodiment aims to summarize videos recorded by patients so that doctors can understand them quickly and accurately. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a reading unit, and a formatting unit. The reception unit records a video. The analysis unit analyzes the video recorded by the reception unit. The generation unit generates a summary based on the video analyzed by the analysis unit. The reading unit reads aloud the summary generated by the generation unit. The formatting unit creates a medical examination format based on the summary generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a doctor to quickly and accurately understand a video recorded by a patient, and there is a risk of reducing the efficiency of diagnosis.

[0005] The system according to the embodiment aims to summarize a video recorded by a patient so that a doctor can quickly and accurately understand it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a reading unit, and a formatting unit. The reception unit records a video. The analysis unit analyzes the video recorded by the reception unit. The generation unit generates a summary based on the video analyzed by the analysis unit. The reading unit reads aloud the summary generated by the generation unit. The formatting unit creates a medical examination format based on the summary generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can summarize videos recorded by patients, enabling doctors to understand them quickly and accurately. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) An AI system according to an embodiment of the present invention is a system that summarizes videos recorded by a patient or their family members into content that is easy for a doctor to understand and acts as a bridge between them. This AI system allows the patient or their family members to record the affected area while describing their symptoms. For example, they might record symptoms such as "a dull ache," "nausea," or "drowsiness." They might also record things related to the symptoms, such as their complexion, the condition of a rash, tremors, vomit, or urine. In the case of an injury, they might record the condition of the affected area and the object of the injury, such as a fall down stairs, being hit by a ball, or being bitten by an insect. These videos are analyzed by the AI ​​and summarized for the doctor before the patient arrives at the hospital. Since the patient may be unable to speak after arrival, a function to read the summary aloud is also included. Furthermore, in a second phase, it is possible to have the hospital automatically generate a consultation format via communication before the patient arrives. This AI system is particularly targeted at foreigners with limited Japanese language skills, tourists visiting Japan, young people unable to describe their symptoms, elderly people with cognitive impairment, and patients suffering from severe symptoms who are unable to communicate. This eliminates the need for one-on-one verbal explanations to doctors, allowing for accurate information to be provided even if memories are vague. It also makes it easier for doctors to understand the circumstances at the time of the incident, bridging language barriers and nuance differences. This system acts as a bridge between the person who knows the details of the incident (the patient) and the person providing treatment (the doctor), resolving misunderstandings. Furthermore, it can bridge the time gap between the onset of the incident and treatment. In addition, it utilizes generative AI technologies such as summarization, translation, video analysis, sentiment analysis, and language analysis. This allows the AI ​​system to summarize patient videos into content that is easy for doctors to understand, bridging the gap between the two.

[0029] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a reading unit, and a formatting unit. The reception unit records videos recorded by the patient or their related parties. Videos recorded by the patient or their related parties include, for example, videos in which they talk about the condition or symptoms of the affected area. The reception unit can record videos using, for example, a smartphone or tablet. The reception unit also has a function to upload the recorded videos to the cloud. The analysis unit analyzes the videos recorded by the reception unit. The analysis unit analyzes the content of the videos and extracts symptoms and the condition of the affected area. The analysis unit can analyze the content of the videos using AI. The generation unit generates a summary based on the videos analyzed by the analysis unit. The generation unit generates the summary using generation AI. The generation unit summarizes the content of the videos and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the summary generated by the generation unit. The reading unit can read the summary aloud using AI. The formatting unit creates a consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate a consultation format using AI. This allows the AI ​​system according to the embodiment to summarize patient videos into content that is easy for doctors to understand and to provide a bridge between the two.

[0030] The reception desk records videos made by the patient or their family members. These videos may include, for example, videos in which the patient or their family members describe the condition of the affected area or their symptoms. The reception desk can record videos using, for example, smartphones or tablets. Specifically, the patient can use their smartphone camera to take detailed images of the affected area and record a video in which they verbally describe their symptoms, the degree of pain, and when the condition started. This allows for the provision of detailed information even when a doctor cannot directly examine the patient. The reception desk also has a function to upload the recorded videos to the cloud. The upload to the cloud is encrypted to ensure security and protect the patient's privacy. Furthermore, the reception desk has a function to notify the patient when the video upload is complete, allowing the patient or their family members to check the status of the video transmission. This allows the reception desk to efficiently and securely collect patient videos and quickly move on to the next analysis step.

[0031] The analysis unit analyzes the videos recorded by the reception unit. For example, the analysis unit analyzes the content of the video and extracts information about symptoms and the condition of the affected area. The analysis unit can use AI to analyze the content of the video. Specifically, the AI ​​uses image recognition technology to analyze the condition of the affected area frame by frame of the video, detecting changes in color, the degree of swelling, and the presence or absence of bleeding. It also uses speech recognition technology to convert the patient's description into text and extract information such as details of the symptoms, the time of onset, and the degree of pain. Furthermore, it uses natural language processing technology to analyze the extracted text data and identify important keywords and phrases. In this way, the analysis unit can integrate the visual and audio information obtained from the video and gain a detailed understanding of the patient's symptoms and the condition of the affected area. Based on this information, the analysis unit provides important data for doctors to make diagnoses.

[0032] The generation unit generates summaries based on videos analyzed by the analysis unit. The generation unit uses a generation AI to generate summaries. Specifically, the generation AI generates summaries in a format that is easy for doctors to understand, based on text and image data provided by the analysis unit. For example, the generation AI concisely summarizes the patient's symptoms and the condition of the affected area, highlighting important points. The generation AI also adjusts the summary to prioritize the inclusion of information necessary for doctors to make a diagnosis. In this way, the generation unit supports doctors in quickly understanding the patient's situation and making an appropriate diagnosis. Furthermore, to improve the accuracy of the summaries, the generation unit can refer to past diagnostic data and medical literature to generate optimal summaries. This allows the generation unit to always provide high-quality summaries that reflect the latest medical knowledge.

[0033] The reading unit reads aloud the summaries generated by the generation unit. The reading unit uses AI to read the summaries aloud. Specifically, the reading unit employs speech synthesis technology to read the generated summaries in a natural voice. Speech synthesis technology converts text data into speech data, allowing for natural intonation and pronunciation. This improves diagnostic efficiency, as doctors can not only visually review the summaries but also listen to them aloud. Furthermore, the reading unit has functions to adjust reading speed and volume, allowing for customization according to the doctor's preferences. This enables the reading unit to support doctors in acquiring information most efficiently. The reading unit is also multilingual, capable of reading summaries in different languages, making it useful in international medical settings.

[0034] The formatting unit creates the consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate the consultation format using AI. Specifically, the formatting unit organizes the information necessary for the consultation based on the generated summary and converts it into a standardized format. For example, it automatically creates a consultation format that includes basic patient information, detailed symptoms, condition of the affected area, and past medical history. This eliminates the need for doctors to manually create consultation formats, allowing them to focus on diagnosis. Furthermore, the formatting unit can link the consultation format with the electronic medical record system, enabling rapid recording of consultation results. This improves the efficiency of consultations and reduces the workload in the medical field. The formatting unit can also customize the consultation format, flexibly responding to the needs of doctors. In this way, the formatting unit can support doctors in conducting optimal consultations and improve the treatment outcomes for patients.

[0035] The reception desk can record a video of the patient or their family members describing their symptoms while recording the affected area. For example, the reception desk can record the affected area while the patient describes symptoms such as "soreness," "nausea," or "dizziness." The reception desk can also record things related to the symptoms, such as the patient's complexion, the condition of a rash, the condition of tremors, vomit, or urine. This allows for the recording of a video of the patient or their family members describing their symptoms while recording the affected area. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can convert what the patient says into text using speech recognition technology and save it along with the recorded content.

[0036] The reception area can record things related to the symptoms. For example, the reception area may record the patient's complexion, the condition of their rash, the degree of tremors, vomit, urine, etc. By recording these symptom-related things, the reception area can help doctors get a more accurate understanding of the patient's condition. This allows for the recording of things related to the symptoms. Some or all of the above processing in the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract the parts related to the symptoms.

[0037] The reception area can record the details of the injury. For example, the reception area can record the injured area of ​​the patient. For example, it can record details such as bleeding, fractures, and bruises. By recording these details of the injury, the reception area can help doctors understand the patient's injury more accurately. This allows for the recording of the injury. Some or all of the above processing at the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract details of the injury.

[0038] The reception area can record the object of the injury. For example, the reception area can record the object that caused the patient's injury. For example, it could record a fall down the stairs, being hit by a ball, or being bitten by an insect. By recording these objects of injury, the reception area can help doctors more accurately understand the cause of the patient's injury. This allows the object of the injury to be recorded. Some or all of the above processing in the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract the object of the injury.

[0039] The reading unit can read aloud the generated summary. The reading unit can, for example, read the generated summary aloud. The reading unit can read the summary aloud using AI. This allows the generated summary to be read aloud. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can read the generated summary aloud using speech synthesis technology.

[0040] The formatting unit can generate the consultation format via communication before the patient arrives. For example, the formatting unit can automatically generate the consultation format based on the generated summary and send it to the hospital. The formatting unit can automatically generate the consultation format using AI. This allows the consultation format to be generated via communication before the patient arrives. Some or all of the above-described processes in the formatting unit may be performed using AI or not. For example, the formatting unit can automatically generate the consultation format based on the generated summary and send it to the hospital's electronic medical record system.

[0041] The reception desk can analyze the patient's past recording history and select the optimal recording method. For example, the reception desk can analyze patterns in videos previously recorded by the patient and start recording in a similar manner. The reception desk can also evaluate the quality of videos previously recorded by the patient and suggest the optimal camera settings. The reception desk can also analyze the content of videos previously recorded by the patient and present necessary information in advance. This allows the reception desk to analyze the patient's past recording history and select the optimal recording method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past recording history into the AI ​​and have the AI ​​select the optimal recording method.

[0042] The reception desk can filter the recording content based on the patient's current health condition and symptoms. For example, if the patient has a high fever, the reception desk can instruct the system to record the thermometer reading. If the patient complains of a rash, the reception desk can also instruct the system to focus on recording the area of ​​the rash. If the patient complains of difficulty breathing, the reception desk can also instruct the system to record the patient's breathing. This allows the system to filter the recording content based on the patient's current health condition and symptoms. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the patient's health condition and symptoms into the AI ​​and have the AI ​​perform the filtering of the recording content.

[0043] The reception desk can prioritize recording highly relevant content by considering the patient's geographical location during recording. For example, if the patient is at high altitude, the reception desk can instruct the system to record symptoms of altitude sickness. If the patient is at the beach, the reception desk can also instruct the system to record symptoms of sunburn or heatstroke. If the patient is in an urban area, the reception desk can also instruct the system to record the circumstances of a traffic accident. This allows the system to prioritize recording highly relevant content by considering the patient's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's geographical location into the AI ​​and have the AI ​​select highly relevant content.

[0044] The reception desk can analyze the patient's social media activity during recording and record relevant content. For example, the reception desk can suggest recording content based on symptoms the patient has posted on social media. The reception desk can also determine recording content by referring to health information the patient has previously posted. The reception desk can also adjust the recording content based on advice from healthcare professionals the patient follows. This allows for the analysis of the patient's social media activity and the recording of relevant content. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's social media activity into AI and have the AI ​​select relevant content.

[0045] The analysis unit can apply different analysis algorithms based on the video content during analysis. For example, if the patient's symptoms are diverse, the analysis unit can combine multiple analysis algorithms. If the patient's symptoms are concentrated in a specific area, the analysis unit can apply an analysis algorithm specialized for that area. If the patient's symptoms change over time, the analysis unit can apply an analysis algorithm along the time axis. This allows the analysis unit to apply different analysis algorithms based on the video content. 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 video content into AI and have the AI ​​select the optimal analysis algorithm.

[0046] The analysis unit can customize the analysis method according to the video's shooting environment and conditions during analysis. For example, if the video was shot in a dark environment, the analysis unit can adjust the brightness before analysis. If the video was shot in a noisy environment, the analysis unit can remove noise before analysis. If the video was shot in a fast-moving environment, the analysis unit can correct for motion before analysis. This allows the analysis method to be customized according to the video's shooting environment and conditions. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the video's shooting environment and conditions into the AI ​​and have the AI ​​select the optimal analysis method.

[0047] The analysis unit can determine the priority of analysis based on the video's shooting date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent video and provide results quickly. The analysis unit can prioritize the latest information, postponing older videos. If the shooting date is important, the analysis unit can prioritize the analysis of videos shot at a specific time. This allows the analysis priority to be determined based on the video's shooting date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video's shooting date into the AI ​​and have the AI ​​determine the analysis priority.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the video during the analysis process. For example, the analysis unit can refer to medical literature related to the video content to supplement the analysis results. The analysis unit can refer to research papers related to the video content to improve the analysis algorithm. The analysis unit can refer to guidelines related to the video content to scrutinize the analysis results. This allows the analysis unit to improve the accuracy of its analysis by referring to relevant literature related to the video. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video content into the AI ​​and have the AI ​​refer to relevant literature.

[0049] The generation unit can adjust the level of detail in the summary based on the importance of the video during summary generation. For example, if a video contains important symptoms, the generation unit can generate a detailed summary. If a video contains minor symptoms, the generation unit can generate a concise summary. If a video contains urgent symptoms, the generation unit can generate a summary that can be quickly understood. This allows the level of detail in the summary to be adjusted based on the importance of the video. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the video into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0050] The generation unit can apply different summarization algorithms depending on the video category when generating summaries. For example, in the case of a video about a disease, the generation unit can apply a summary algorithm specialized for that disease. In the case of a video about an injury, the generation unit can apply a summary algorithm specialized for that injury. In the case of a video about other symptoms, the generation unit can apply a summary algorithm appropriate to those symptoms. This allows different summarization algorithms to be applied depending on the video category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the video category into the AI ​​and have the AI ​​select the optimal summarization algorithm.

[0051] The generation unit can determine the priority of summaries based on the video shooting dates when generating summaries. For example, the generation unit can prioritize summarizing the most recent videos and provide results quickly. The generation unit can prioritize the latest information, postponing older videos. If the shooting date is important, the generation unit can prioritize summarizing videos shot at a specific time. This allows the generation unit to determine the priority of summaries based on the video shooting dates. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the video shooting dates into the AI ​​and have the AI ​​determine the priority of summaries.

[0052] The generation unit can adjust the order of summaries based on the relevance of the videos during summary generation. For example, the generation unit can generate a summary first for videos containing important symptoms. For videos containing minor symptoms, it can generate a summary later. For videos containing urgent symptoms, it can generate a summary quickly. This allows the order of summaries to be adjusted based on the relevance of the videos. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the videos into the AI ​​and have the AI ​​adjust the order of the summaries.

[0053] The reading unit can apply different reading algorithms based on the content of the summary during reading. For example, if the summary contains important symptoms, the reading unit will read it in detail. If the summary contains minor symptoms, the reading unit can read it concisely. If the summary contains urgent symptoms, the reading unit can read it quickly. This allows for the application of different reading algorithms based on the content of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into an AI and have the AI ​​select the optimal reading algorithm.

[0054] The reading unit can adjust the level of detail in its reading according to the importance of the summary. For example, if the summary contains important symptoms, the reading unit will read it in detail. If the summary contains minor symptoms, the reading unit can read it concisely. If the summary contains urgent symptoms, the reading unit can read it quickly. This allows the level of detail in the reading unit to be adjusted according to the importance of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the importance of the summary into the AI ​​and have the AI ​​adjust the level of detail in the reading.

[0055] The reading unit can determine the reading priority based on when the summaries were filmed. For example, the reading unit may prioritize reading the most recent summaries. It can also prioritize the latest information, delaying older summaries. If the filming date is important, the reading unit can prioritize reading summaries filmed at a specific time. This allows the reading priority to be determined based on when the summaries were filmed. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the filming dates of the summaries into the AI ​​and have the AI ​​determine the reading priority.

[0056] The reading unit can improve the accuracy of its reading by referring to relevant literature related to the summary during the reading process. For example, the reading unit can refer to medical literature related to the content of the summary to reinforce the reading. The reading unit can refer to research papers related to the content of the summary to improve its reading algorithm. The reading unit can refer to guidelines related to the content of the summary to refine the reading. This allows the reading unit to improve the accuracy of its reading by referring to relevant literature related to the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into AI and have the AI ​​refer to relevant literature.

[0057] The formatting unit can apply different formatting algorithms based on the content of the summary when generating a medical examination format. For example, if the summary contains important symptoms, the formatting unit can generate a detailed format. If the summary contains minor symptoms, the formatting unit can generate a concise format. If the summary contains urgent symptoms, the formatting unit can generate a format that can be quickly understood. This allows for the application of different formatting algorithms based on the content of the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the content of the summary into AI and have the AI ​​select the optimal formatting algorithm.

[0058] The formatting unit can adjust the level of detail in the format according to the importance of the summary when generating the consultation format. For example, the formatting unit can generate a detailed format if the summary contains important symptoms. If the summary contains minor symptoms, the formatting unit can generate a concise format. If the summary contains urgent symptoms, the formatting unit can generate a format that can be quickly understood. This allows the level of detail in the format to be adjusted according to the importance of the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the importance of the summary into the AI ​​and have the AI ​​adjust the level of detail in the format.

[0059] The formatting unit can determine the priority of the format based on the timing of the summary's capture when generating the examination format. For example, the formatting unit can prioritize the most recent summary in the format. The formatting unit can prioritize the latest information, putting older summaries aside. If the timing of the capture is important, the formatting unit can prioritize the capture of summaries taken at a specific time. This allows the formatting unit to determine the priority of the format based on the timing of the summary's capture. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the timing of the summary's capture into the AI ​​and have the AI ​​determine the formatting priority.

[0060] The formatting unit can improve the accuracy of the format by referring to relevant literature for the summary when generating the consultation format. For example, the formatting unit can supplement the format content by referring to medical literature related to the content of the summary. The formatting unit can improve the formatting algorithm by referring to research papers related to the content of the summary. The formatting unit can refine the format content by referring to guidelines related to the content of the summary. This allows for improved formatting accuracy by referring to relevant literature for the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the content of the summary into AI and have the AI ​​refer to relevant literature.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze the patient's past recording history and select the optimal recording method. For example, it can analyze patterns in videos the patient has recorded in the past and start recording using a similar method. It can also evaluate the quality of videos the patient has recorded in the past and suggest the optimal camera settings. It can also analyze the content of videos the patient has recorded in the past and present necessary information in advance. This allows the reception desk to analyze the patient's past recording history and select the optimal recording method. Some or all of the above processes at the reception desk may be performed using AI or not. For example, the reception desk can input past recording history into the AI ​​and have the AI ​​select the optimal recording method.

[0063] The reception desk can filter the recording content based on the patient's current health condition and symptoms. For example, if the patient has a high fever, it can instruct the system to record the thermometer reading. If the patient complains of a rash, it can instruct the system to focus on recording the area of ​​the rash. If the patient complains of difficulty breathing, it can instruct the system to record their breathing. This allows the system to filter the recording content based on the patient's current health condition and symptoms. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the patient's health condition and symptoms into the AI ​​and have the AI ​​perform the filtering of the recording content.

[0064] The analysis unit can apply different analysis algorithms based on the video content during analysis. For example, if the patient's symptoms are diverse, multiple analysis algorithms can be combined. If the patient's symptoms are concentrated in a specific area, an analysis algorithm specialized for that area can be applied. If the patient's symptoms change over time, an analysis algorithm aligned with the time axis can be applied. This allows different analysis algorithms to be applied based on the video content. 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 video content into the AI ​​and have the AI ​​select the optimal analysis algorithm.

[0065] The generation unit can adjust the level of detail in the summary based on the importance of the video during summary generation. For example, a detailed summary can be generated for videos containing important symptoms. A concise summary can be generated for videos containing minor symptoms. A quickly understandable summary can be generated for videos containing urgent symptoms. This allows the level of detail in the summary to be adjusted based on the importance of the video. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the video into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0066] The reading unit can apply different reading algorithms based on the content of the summary during reading. For example, if the summary contains important symptoms, it can read in detail. If the summary contains minor symptoms, it can read concisely. If the summary contains urgent symptoms, it can read quickly. This allows for the application of different reading algorithms based on the content of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into the AI ​​and have the AI ​​select the optimal reading algorithm.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk records videos made by the patient or their family members. These videos may include, for example, a video describing the condition of the affected area or symptoms. The reception desk has the ability to record videos using a smartphone or tablet and upload the recorded videos to the cloud. Step 2: The analysis unit analyzes the video recorded by the reception unit. The analysis unit analyzes the content of the video and extracts symptoms and the condition of the affected area. The analysis unit can use AI to analyze the content of the video. Step 3: The generation unit generates a summary based on the video analyzed by the analysis unit. The generation unit uses a generation AI to generate a summary, summarizing the video content and converting it into a format that is easy for doctors to understand. Step 4: The reading unit reads aloud the summary generated by the generation unit. The reading unit can use AI to read the summary aloud. Step 5: The formatting unit creates the consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate the consultation format using AI.

[0069] (Example of form 2) An AI system according to an embodiment of the present invention is a system that summarizes videos recorded by a patient or their family members into content that is easy for a doctor to understand and acts as a bridge between them. This AI system allows the patient or their family members to record the affected area while describing their symptoms. For example, they might record symptoms such as "a dull ache," "nausea," or "drowsiness." They might also record things related to the symptoms, such as their complexion, the condition of a rash, tremors, vomit, or urine. In the case of an injury, they might record the condition of the affected area and the object of the injury, such as a fall down stairs, being hit by a ball, or being bitten by an insect. These videos are analyzed by the AI ​​and summarized for the doctor before the patient arrives at the hospital. Since the patient may be unable to speak after arrival, a function to read the summary aloud is also included. Furthermore, in a second phase, it is possible to have the hospital automatically generate a consultation format via communication before the patient arrives. This AI system is particularly targeted at foreigners with limited Japanese language skills, tourists visiting Japan, young people unable to describe their symptoms, elderly people with cognitive impairment, and patients suffering from severe symptoms who are unable to communicate. This eliminates the need for one-on-one verbal explanations to doctors, allowing for accurate information to be provided even if memories are vague. It also makes it easier for doctors to understand the circumstances at the time of the incident, bridging language barriers and nuance differences. This system acts as a bridge between the person who knows the details of the incident (the patient) and the person providing treatment (the doctor), resolving misunderstandings. Furthermore, it can bridge the time gap between the onset of the incident and treatment. In addition, it utilizes generative AI technologies such as summarization, translation, video analysis, sentiment analysis, and language analysis. This allows the AI ​​system to summarize patient videos into content that is easy for doctors to understand, bridging the gap between the two.

[0070] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a reading unit, and a formatting unit. The reception unit records videos recorded by the patient or their related parties. Videos recorded by the patient or their related parties include, for example, videos in which they talk about the condition or symptoms of the affected area. The reception unit can record videos using, for example, a smartphone or tablet. The reception unit also has a function to upload the recorded videos to the cloud. The analysis unit analyzes the videos recorded by the reception unit. The analysis unit analyzes the content of the videos and extracts symptoms and the condition of the affected area. The analysis unit can analyze the content of the videos using AI. The generation unit generates a summary based on the videos analyzed by the analysis unit. The generation unit generates the summary using generation AI. The generation unit summarizes the content of the videos and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the summary generated by the generation unit. The reading unit can read the summary aloud using AI. The formatting unit creates a consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate a consultation format using AI. This allows the AI ​​system according to the embodiment to summarize patient videos into content that is easy for doctors to understand and to provide a bridge between the two.

[0071] The reception desk records videos made by the patient or their family members. These videos may include, for example, videos in which the patient or their family members describe the condition of the affected area or their symptoms. The reception desk can record videos using, for example, smartphones or tablets. Specifically, the patient can use their smartphone camera to take detailed images of the affected area and record a video in which they verbally describe their symptoms, the degree of pain, and when the condition started. This allows for the provision of detailed information even when a doctor cannot directly examine the patient. The reception desk also has a function to upload the recorded videos to the cloud. The upload to the cloud is encrypted to ensure security and protect the patient's privacy. Furthermore, the reception desk has a function to notify the patient when the video upload is complete, allowing the patient or their family members to check the status of the video transmission. This allows the reception desk to efficiently and securely collect patient videos and quickly move on to the next analysis step.

[0072] The analysis unit analyzes the videos recorded by the reception unit. For example, the analysis unit analyzes the content of the video and extracts information about symptoms and the condition of the affected area. The analysis unit can use AI to analyze the content of the video. Specifically, the AI ​​uses image recognition technology to analyze the condition of the affected area frame by frame of the video, detecting changes in color, the degree of swelling, and the presence or absence of bleeding. It also uses speech recognition technology to convert the patient's description into text and extract information such as details of the symptoms, the time of onset, and the degree of pain. Furthermore, it uses natural language processing technology to analyze the extracted text data and identify important keywords and phrases. In this way, the analysis unit can integrate the visual and audio information obtained from the video and gain a detailed understanding of the patient's symptoms and the condition of the affected area. Based on this information, the analysis unit provides important data for doctors to make diagnoses.

[0073] The generation unit generates summaries based on videos analyzed by the analysis unit. The generation unit uses a generation AI to generate summaries. Specifically, the generation AI generates summaries in a format that is easy for doctors to understand, based on text and image data provided by the analysis unit. For example, the generation AI concisely summarizes the patient's symptoms and the condition of the affected area, highlighting important points. The generation AI also adjusts the summary to prioritize the inclusion of information necessary for doctors to make a diagnosis. In this way, the generation unit supports doctors in quickly understanding the patient's situation and making an appropriate diagnosis. Furthermore, to improve the accuracy of the summaries, the generation unit can refer to past diagnostic data and medical literature to generate optimal summaries. This allows the generation unit to always provide high-quality summaries that reflect the latest medical knowledge.

[0074] The reading unit reads aloud the summaries generated by the generation unit. The reading unit uses AI to read the summaries aloud. Specifically, the reading unit employs speech synthesis technology to read the generated summaries in a natural voice. Speech synthesis technology converts text data into speech data, allowing for natural intonation and pronunciation. This improves diagnostic efficiency, as doctors can not only visually review the summaries but also listen to them aloud. Furthermore, the reading unit has functions to adjust reading speed and volume, allowing for customization according to the doctor's preferences. This enables the reading unit to support doctors in acquiring information most efficiently. The reading unit is also multilingual, capable of reading summaries in different languages, making it useful in international medical settings.

[0075] The formatting unit creates the consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate the consultation format using AI. Specifically, the formatting unit organizes the information necessary for the consultation based on the generated summary and converts it into a standardized format. For example, it automatically creates a consultation format that includes basic patient information, detailed symptoms, condition of the affected area, and past medical history. This eliminates the need for doctors to manually create consultation formats, allowing them to focus on diagnosis. Furthermore, the formatting unit can link the consultation format with the electronic medical record system, enabling rapid recording of consultation results. This improves the efficiency of consultations and reduces the workload in the medical field. The formatting unit can also customize the consultation format, flexibly responding to the needs of doctors. In this way, the formatting unit can support doctors in conducting optimal consultations and improve the treatment outcomes for patients.

[0076] The reception desk can record a video of the patient or their family members describing their symptoms while recording the affected area. For example, the reception desk can record the affected area while the patient describes symptoms such as "soreness," "nausea," or "dizziness." The reception desk can also record things related to the symptoms, such as the patient's complexion, the condition of a rash, the condition of tremors, vomit, or urine. This allows for the recording of a video of the patient or their family members describing their symptoms while recording the affected area. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can convert what the patient says into text using speech recognition technology and save it along with the recorded content.

[0077] The reception area can record things related to the symptoms. For example, the reception area may record the patient's complexion, the condition of their rash, the degree of tremors, vomit, urine, etc. By recording these symptom-related things, the reception area can help doctors get a more accurate understanding of the patient's condition. This allows for the recording of things related to the symptoms. Some or all of the above processing in the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract the parts related to the symptoms.

[0078] The reception area can record the details of the injury. For example, the reception area can record the injured area of ​​the patient. For example, it can record details such as bleeding, fractures, and bruises. By recording these details of the injury, the reception area can help doctors understand the patient's injury more accurately. This allows for the recording of the injury. Some or all of the above processing at the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract details of the injury.

[0079] The reception area can record the object of the injury. For example, the reception area can record the object that caused the patient's injury. For example, it could record a fall down the stairs, being hit by a ball, or being bitten by an insect. By recording these objects of injury, the reception area can help doctors more accurately understand the cause of the patient's injury. This allows the object of the injury to be recorded. Some or all of the above processing in the reception area may be performed using AI or not. For example, the reception area can input the recorded video into AI and automatically extract the object of the injury.

[0080] The reading unit can read aloud the generated summary. The reading unit can, for example, read the generated summary aloud. The reading unit can read the summary aloud using AI. This allows the generated summary to be read aloud. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can read the generated summary aloud using speech synthesis technology.

[0081] The formatting unit can generate the consultation format via communication before the patient arrives. For example, the formatting unit can automatically generate the consultation format based on the generated summary and send it to the hospital. The formatting unit can automatically generate the consultation format using AI. This allows the consultation format to be generated via communication before the patient arrives. Some or all of the above-described processes in the formatting unit may be performed using AI or not. For example, the formatting unit can automatically generate the consultation format based on the generated summary and send it to the hospital's electronic medical record system.

[0082] The reception desk can estimate the patient's emotions and adjust the recording start time based on the estimated emotions. For example, if the patient is nervous, the reception desk can wait until the patient relaxes before starting recording. If the patient is anxious, the reception desk can start recording immediately and edit it later. If the patient is calm, the reception desk can start recording before asking for detailed explanations. This allows the recording start time to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 reception desk may be performed using AI or not. For example, the reception desk can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the patient's emotion estimation.

[0083] The reception desk can analyze the patient's past recording history and select the optimal recording method. For example, the reception desk can analyze patterns in videos previously recorded by the patient and start recording in a similar manner. The reception desk can also evaluate the quality of videos previously recorded by the patient and suggest the optimal camera settings. The reception desk can also analyze the content of videos previously recorded by the patient and present necessary information in advance. This allows the reception desk to analyze the patient's past recording history and select the optimal recording method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past recording history into the AI ​​and have the AI ​​select the optimal recording method.

[0084] The reception desk can filter the recording content based on the patient's current health condition and symptoms. For example, if the patient has a high fever, the reception desk can instruct the system to record the thermometer reading. If the patient complains of a rash, the reception desk can also instruct the system to focus on recording the area of ​​the rash. If the patient complains of difficulty breathing, the reception desk can also instruct the system to record the patient's breathing. This allows the system to filter the recording content based on the patient's current health condition and symptoms. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the patient's health condition and symptoms into the AI ​​and have the AI ​​perform the filtering of the recording content.

[0085] The reception desk can estimate the patient's emotions and prioritize the content to be recorded based on the estimated emotions. For example, if the patient is feeling anxious, the reception desk may first record a reassuring message. If the patient is complaining of pain, the reception desk may also prioritize recording the painful area. If the patient is confused, the reception desk may start recording with a simple explanation. This allows the reception desk to prioritize the content to be recorded based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0086] The reception desk can prioritize recording highly relevant content by considering the patient's geographical location during recording. For example, if the patient is at high altitude, the reception desk can instruct the system to record symptoms of altitude sickness. If the patient is at the beach, the reception desk can also instruct the system to record symptoms of sunburn or heatstroke. If the patient is in an urban area, the reception desk can also instruct the system to record the circumstances of a traffic accident. This allows the system to prioritize recording highly relevant content by considering the patient's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's geographical location into the AI ​​and have the AI ​​select highly relevant content.

[0087] The reception desk can analyze the patient's social media activity during recording and record relevant content. For example, the reception desk can suggest recording content based on symptoms the patient has posted on social media. The reception desk can also determine recording content by referring to health information the patient has previously posted. The reception desk can also adjust the recording content based on advice from healthcare professionals the patient follows. This allows for the analysis of the patient's social media activity and the recording of relevant content. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the patient's social media activity into AI and have the AI ​​select relevant content.

[0088] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is tense, the analysis unit can increase the accuracy of the analysis to provide more detailed information. If the patient is relaxed, the analysis unit can maintain the accuracy of the analysis at a normal level. If the patient is anxious, the analysis unit can perform the analysis quickly and provide the results sooner. This allows the accuracy of the analysis to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0089] The analysis unit can apply different analysis algorithms based on the video content during analysis. For example, if the patient's symptoms are diverse, the analysis unit can combine multiple analysis algorithms. If the patient's symptoms are concentrated in a specific area, the analysis unit can apply an analysis algorithm specialized for that area. If the patient's symptoms change over time, the analysis unit can apply an analysis algorithm along the time axis. This allows the analysis unit to apply different analysis algorithms based on the video content. 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 video content into AI and have the AI ​​select the optimal analysis algorithm.

[0090] The analysis unit can customize the analysis method according to the video's shooting environment and conditions during analysis. For example, if the video was shot in a dark environment, the analysis unit can adjust the brightness before analysis. If the video was shot in a noisy environment, the analysis unit can remove noise before analysis. If the video was shot in a fast-moving environment, the analysis unit can correct for motion before analysis. This allows the analysis method to be customized according to the video's shooting environment and conditions. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the video's shooting environment and conditions into the AI ​​and have the AI ​​select the optimal analysis method.

[0091] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is tense, the analysis unit can provide a simple and highly visible display method. If the patient is relaxed, the analysis unit can provide a display method that includes detailed information. If the patient is anxious, the analysis unit can provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0092] The analysis unit can determine the priority of analysis based on the video's shooting date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent video and provide results quickly. The analysis unit can prioritize the latest information, postponing older videos. If the shooting date is important, the analysis unit can prioritize the analysis of videos shot at a specific time. This allows the analysis priority to be determined based on the video's shooting date. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video's shooting date into the AI ​​and have the AI ​​determine the analysis priority.

[0093] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the video during the analysis process. For example, the analysis unit can refer to medical literature related to the video content to supplement the analysis results. The analysis unit can refer to research papers related to the video content to improve the analysis algorithm. The analysis unit can refer to guidelines related to the video content to scrutinize the analysis results. This allows the analysis unit to improve the accuracy of its analysis by referring to relevant literature related to the video. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the video content into the AI ​​and have the AI ​​refer to relevant literature.

[0094] The generation unit can estimate the patient's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the patient is tense, the generation unit can generate a concise and easy-to-understand summary. If the patient is relaxed, the generation unit can generate a summary that includes detailed information. If the patient is anxious, the generation unit can generate a summary that can be quickly understood. This allows the way the summary is presented to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input patient facial expression data captured by a camera into the generation AI and have the generation AI perform the estimation of the patient's emotions.

[0095] The generation unit can adjust the level of detail in the summary based on the importance of the video during summary generation. For example, if a video contains important symptoms, the generation unit can generate a detailed summary. If a video contains minor symptoms, the generation unit can generate a concise summary. If a video contains urgent symptoms, the generation unit can generate a summary that can be quickly understood. This allows the level of detail in the summary to be adjusted based on the importance of the video. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the video into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0096] The generation unit can apply different summarization algorithms depending on the video category when generating summaries. For example, in the case of a video about a disease, the generation unit can apply a summary algorithm specialized for that disease. In the case of a video about an injury, the generation unit can apply a summary algorithm specialized for that injury. In the case of a video about other symptoms, the generation unit can apply a summary algorithm appropriate to those symptoms. This allows different summarization algorithms to be applied depending on the video category. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the video category into the AI ​​and have the AI ​​select the optimal summarization algorithm.

[0097] The generation unit can estimate the patient's emotions and adjust the length of the summary based on the estimated emotions. For example, if the patient is tense, the generation unit can generate a short, concise summary. If the patient is relaxed, the generation unit can generate a longer summary with more detailed explanations. If the patient is anxious, the generation unit can generate a short, easily understandable summary. This allows the length of the summary to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input patient facial expression data captured by a camera into the generation AI and have the generation AI perform the patient's emotion estimation.

[0098] The generation unit can determine the priority of summaries based on the video shooting dates when generating summaries. For example, the generation unit can prioritize summarizing the most recent videos and provide results quickly. The generation unit can prioritize the latest information, postponing older videos. If the shooting date is important, the generation unit can prioritize summarizing videos shot at a specific time. This allows the generation unit to determine the priority of summaries based on the video shooting dates. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the video shooting dates into the AI ​​and have the AI ​​determine the priority of summaries.

[0099] The generation unit can adjust the order of summaries based on the relevance of the videos during summary generation. For example, the generation unit can generate a summary first for videos containing important symptoms. For videos containing minor symptoms, it can generate a summary later. For videos containing urgent symptoms, it can generate a summary quickly. This allows the order of summaries to be adjusted based on the relevance of the videos. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the relevance of the videos into the AI ​​and have the AI ​​adjust the order of the summaries.

[0100] The reading unit can estimate the patient's emotions and adjust the tone and speed of reading based on the estimated emotions. For example, if the patient is nervous, the reading unit can read slowly in a calm tone. If the patient is relaxed, the reading unit can read in a bright tone. If the patient is anxious, the reading unit can read quickly and concisely. This allows the tone and speed of reading to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input patient facial expression data captured by a camera into the generative AI and have the generative AI perform the estimation of the patient's emotions.

[0101] The reading unit can apply different reading algorithms based on the content of the summary during reading. For example, if the summary contains important symptoms, the reading unit will read it in detail. If the summary contains minor symptoms, the reading unit can read it concisely. If the summary contains urgent symptoms, the reading unit can read it quickly. This allows for the application of different reading algorithms based on the content of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into an AI and have the AI ​​select the optimal reading algorithm.

[0102] The reading unit can adjust the level of detail in its reading according to the importance of the summary. For example, if the summary contains important symptoms, the reading unit will read it in detail. If the summary contains minor symptoms, the reading unit can read it concisely. If the summary contains urgent symptoms, the reading unit can read it quickly. This allows the level of detail in the reading unit to be adjusted according to the importance of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the importance of the summary into the AI ​​and have the AI ​​adjust the level of detail in the reading.

[0103] The reading unit can estimate the patient's emotions and adjust the reading order based on the estimated emotions. For example, if the patient is nervous, the reading unit can read important information first. If the patient is relaxed, the reading unit can postpone reading detailed information. If the patient is anxious, the reading unit can read information that can be quickly understood first. This allows the reading order to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 reading unit may be performed using AI or not. For example, the reading unit can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0104] The reading unit can determine the reading priority based on when the summaries were filmed. For example, the reading unit may prioritize reading the most recent summaries. It can also prioritize the latest information, delaying older summaries. If the filming date is important, the reading unit can prioritize reading summaries filmed at a specific time. This allows the reading priority to be determined based on when the summaries were filmed. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the filming dates of the summaries into the AI ​​and have the AI ​​determine the reading priority.

[0105] The reading unit can improve the accuracy of its reading by referring to relevant literature related to the summary during the reading process. For example, the reading unit can refer to medical literature related to the content of the summary to reinforce the reading. The reading unit can refer to research papers related to the content of the summary to improve its reading algorithm. The reading unit can refer to guidelines related to the content of the summary to refine the reading. This allows the reading unit to improve the accuracy of its reading by referring to relevant literature related to the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into AI and have the AI ​​refer to relevant literature.

[0106] The formatting unit can estimate the patient's emotions and adjust the display method of the consultation format based on the estimated emotions. For example, if the patient is nervous, the formatting unit can provide a simple and highly visible format. If the patient is relaxed, the formatting unit can provide a format that includes detailed information. If the patient is anxious, the formatting unit can provide a format that gets straight to the point. This allows the display method of the consultation format to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input patient facial expression data captured by a camera into the generative AI and have the generative AI perform the estimation of the patient's emotions.

[0107] The formatting unit can apply different formatting algorithms based on the content of the summary when generating a medical examination format. For example, if the summary contains important symptoms, the formatting unit can generate a detailed format. If the summary contains minor symptoms, the formatting unit can generate a concise format. If the summary contains urgent symptoms, the formatting unit can generate a format that can be quickly understood. This allows for the application of different formatting algorithms based on the content of the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the content of the summary into AI and have the AI ​​select the optimal formatting algorithm.

[0108] The formatting unit can adjust the level of detail in the format according to the importance of the summary when generating the consultation format. For example, the formatting unit can generate a detailed format if the summary contains important symptoms. If the summary contains minor symptoms, the formatting unit can generate a concise format. If the summary contains urgent symptoms, the formatting unit can generate a format that can be quickly understood. This allows the level of detail in the format to be adjusted according to the importance of the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the importance of the summary into the AI ​​and have the AI ​​adjust the level of detail in the format.

[0109] The formatting unit can estimate the patient's emotions and adjust the order of the consultation format based on the estimated emotions. For example, if the patient is nervous, the formatting unit can display important information first. If the patient is relaxed, the formatting unit can display detailed information later. If the patient is anxious, the formatting unit can display information that can be quickly understood first. This allows the order of the consultation format to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 formatting unit may be performed using AI or not. For example, the formatting unit can input patient facial expression data captured by a camera into the generative AI and have the generative AI perform the estimation of the patient's emotions.

[0110] The formatting unit can determine the priority of the format based on the timing of the summary's capture when generating the examination format. For example, the formatting unit can prioritize the most recent summary in the format. The formatting unit can prioritize the latest information, putting older summaries aside. If the timing of the capture is important, the formatting unit can prioritize the capture of summaries taken at a specific time. This allows the formatting unit to determine the priority of the format based on the timing of the summary's capture. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the timing of the summary's capture into the AI ​​and have the AI ​​determine the formatting priority.

[0111] The formatting unit can improve the accuracy of the format by referring to relevant literature for the summary when generating the consultation format. For example, the formatting unit can supplement the format content by referring to medical literature related to the content of the summary. The formatting unit can improve the formatting algorithm by referring to research papers related to the content of the summary. The formatting unit can refine the format content by referring to guidelines related to the content of the summary. This allows for improved formatting accuracy by referring to relevant literature for the summary. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input the content of the summary into AI and have the AI ​​refer to relevant literature.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can estimate the patient's emotions and adjust the recording start time based on the estimated emotions. For example, if the patient is nervous, the reception desk can wait until the patient is relaxed before starting the recording. If the patient is anxious, the recording can start immediately and be edited later. If the patient is calm, the recording can start before asking for detailed explanations. This allows the recording start time to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the patient's emotion estimation.

[0114] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the patient is tense, the accuracy of the analysis can be increased to provide more detailed information. If the patient is relaxed, the accuracy of the analysis can be kept at a normal level. If the patient is anxious, the analysis can be performed quickly and the results can be provided sooner. This allows the accuracy of the analysis to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0115] The generation unit can estimate the patient's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the patient is tense, it can generate a concise and easy-to-understand summary. If the patient is relaxed, it can generate a summary that includes detailed information. If the patient is anxious, it can generate a summary that can be quickly understood. This allows the way the summary is presented to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input patient facial expression data captured by a camera into the generative AI and have the generative AI perform the estimation of the patient's emotions.

[0116] The reading unit can estimate the patient's emotions and adjust the tone and speed of the reading based on the estimated emotions. For example, if the patient is nervous, it can read slowly in a calm tone. If the patient is relaxed, it can read in a bright tone. If the patient is anxious, it can read quickly and concisely. This allows the tone and speed of the reading to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input patient facial expression data captured by a camera into a generative AI and have the generative AI perform the estimation of the patient's emotions.

[0117] The formatting unit can estimate the patient's emotions and adjust the display method of the consultation format based on the estimated emotions. For example, if the patient is nervous, a simple and highly visible format can be provided. If the patient is relaxed, a format containing detailed information can be provided. If the patient is anxious, a format that gets straight to the point can be provided. This allows the display method of the consultation format to be adjusted based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the formatting unit may be performed using AI or not. For example, the formatting unit can input patient facial expression data captured by a camera into the generative AI and have the generative AI perform the estimation of the patient's emotions.

[0118] The reception desk can analyze the patient's past recording history and select the optimal recording method. For example, it can analyze patterns in videos the patient has recorded in the past and start recording using a similar method. It can also evaluate the quality of videos the patient has recorded in the past and suggest the optimal camera settings. It can also analyze the content of videos the patient has recorded in the past and present necessary information in advance. This allows the reception desk to analyze the patient's past recording history and select the optimal recording method. Some or all of the above processes at the reception desk may be performed using AI or not. For example, the reception desk can input past recording history into the AI ​​and have the AI ​​select the optimal recording method.

[0119] The reception desk can filter the recording content based on the patient's current health condition and symptoms. For example, if the patient has a high fever, it can instruct the system to record the thermometer reading. If the patient complains of a rash, it can instruct the system to focus on recording the area of ​​the rash. If the patient complains of difficulty breathing, it can instruct the system to record their breathing. This allows the system to filter the recording content based on the patient's current health condition and symptoms. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input the patient's health condition and symptoms into the AI ​​and have the AI ​​perform the filtering of the recording content.

[0120] The analysis unit can apply different analysis algorithms based on the video content during analysis. For example, if the patient's symptoms are diverse, multiple analysis algorithms can be combined. If the patient's symptoms are concentrated in a specific area, an analysis algorithm specialized for that area can be applied. If the patient's symptoms change over time, an analysis algorithm aligned with the time axis can be applied. This allows different analysis algorithms to be applied based on the video content. 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 video content into the AI ​​and have the AI ​​select the optimal analysis algorithm.

[0121] The generation unit can adjust the level of detail in the summary based on the importance of the video during summary generation. For example, a detailed summary can be generated for videos containing important symptoms. A concise summary can be generated for videos containing minor symptoms. A quickly understandable summary can be generated for videos containing urgent symptoms. This allows the level of detail in the summary to be adjusted based on the importance of the video. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the importance of the video into the AI ​​and have the AI ​​adjust the level of detail in the summary.

[0122] The reading unit can apply different reading algorithms based on the content of the summary during reading. For example, if the summary contains important symptoms, it can read in detail. If the summary contains minor symptoms, it can read concisely. If the summary contains urgent symptoms, it can read quickly. This allows for the application of different reading algorithms based on the content of the summary. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input the content of the summary into the AI ​​and have the AI ​​select the optimal reading algorithm.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk records videos made by the patient or their family members. These videos may include, for example, a video describing the condition of the affected area or symptoms. The reception desk has the ability to record videos using a smartphone or tablet and upload the recorded videos to the cloud. Step 2: The analysis unit analyzes the video recorded by the reception unit. The analysis unit analyzes the content of the video and extracts symptoms and the condition of the affected area. The analysis unit can use AI to analyze the content of the video. Step 3: The generation unit generates a summary based on the video analyzed by the analysis unit. The generation unit uses a generation AI to generate a summary, summarizing the video content and converting it into a format that is easy for doctors to understand. Step 4: The reading unit reads aloud the summary generated by the generation unit. The reading unit can use AI to read the summary aloud. Step 5: The formatting unit creates the consultation format based on the summary generated by the generation unit. The formatting unit can automatically generate the consultation format using AI.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, reading unit, and formatting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit records a video of the patient using the camera 42 and microphone 38B of the smart device 14 and uploads it to the data processing unit 12. The analysis unit analyzes the video using the specific processing unit 290 of the data processing unit 12 and extracts symptoms and the condition of the affected area. The generation unit generates a summary using the specific processing unit 290 of the data processing unit 12 and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the generated summary using the control unit 46A of the smart device 14. The formatting unit automatically generates a consultation format using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, reading unit, and formatting unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit records a video of the patient using the camera 42 and microphone 238 of the smart glasses 214 and uploads it to the data processing unit 12. The analysis unit analyzes the video using the specific processing unit 290 of the data processing unit 12 and extracts symptoms and the condition of the affected area. The generation unit generates a summary using the specific processing unit 290 of the data processing unit 12 and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the generated summary using the control unit 46A of the smart glasses 214. The formatting unit automatically generates the examination format using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, reading unit, and formatting unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit records a video of the patient using the camera 42 and microphone 238 of the headset terminal 314 and uploads it to the data processing unit 12. The analysis unit analyzes the video using the specific processing unit 290 of the data processing unit 12 and extracts symptoms and the condition of the affected area. The generation unit generates a summary using the specific processing unit 290 of the data processing unit 12 and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the generated summary using the control unit 46A of the headset terminal 314. The formatting unit automatically generates the examination format using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, reading unit, and formatting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit records a video of the patient using the camera 42 and microphone 238 of the robot 414 and uploads it to the data processing unit 12. The analysis unit analyzes the video using the specific processing unit 290 of the data processing unit 12 and extracts symptoms and the condition of the affected area. The generation unit generates a summary using the specific processing unit 290 of the data processing unit 12 and converts it into a format that is easy for doctors to understand. The reading unit reads aloud the summary generated by the control unit 46A of the robot 414. The formatting unit automatically generates a consultation format using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The reception desk that records the video, An analysis unit analyzes the video recorded by the reception unit, A generation unit generates a summary based on the video analyzed by the analysis unit, A reading unit reads out the summary generated by the generation unit, The system includes a formatting unit that sets up a medical examination format based on the summary generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The patient or a person related to them records a video of themselves talking about their symptoms while recording the affected area. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Record anything related to the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Record the injury. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Record the injury. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reading unit, Read the generated summary aloud The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned formatting unit is Launch the consultation format via communication before the patient arrives. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the patient's emotions and adjusts the recording start time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the patient's past recording history and select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During recording, the recording content is filtered based on the patient's current health status and symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the patient's emotions and prioritizes the content to be recorded based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During recording, the system prioritizes recording highly relevant content, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During recording, analyze the patient's social media activity and record relevant content. 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 accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the content of the video. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis method is customized according to the video's shooting environment and circumstances. 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 adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the video was filmed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the video to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system estimates the patient's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a summary, adjust the level of detail in the summary based on the importance of the video. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating summaries, different summarization algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is Estimate the patient's emotions and adjust the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating summaries, the priority of summaries is determined based on when the videos were filmed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating summaries, the order of the summaries is adjusted based on the relevance of the videos. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reading unit, The system estimates the patient's emotions and adjusts the tone and speed of the reading based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reading unit, When reading aloud, different reading algorithms are applied based on the content of the summary. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reading unit, When reading aloud, adjust the level of detail in the summary according to its importance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reading unit, The system estimates the patient's emotions and adjusts the reading order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reading unit, When reading aloud, the priority of reading is determined based on when the summary was filmed. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reading unit, When reading aloud, referencing related literature in the summary improves the accuracy of the reading. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned formatting unit is The system estimates the patient's emotions and adjusts how the consultation format is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned formatting unit is When generating the consultation format, different formatting algorithms are applied based on the content of the summary. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned formatting unit is When generating the consultation format, adjust the level of detail in the format according to the importance of the summary. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned formatting unit is The system estimates the patient's emotions and adjusts the order of the consultation format based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned formatting unit is When generating the consultation format, the format priority is determined based on when the summary was taken. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned formatting unit is When generating the consultation format, we improve the accuracy of the format by referring to relevant literature in the summary. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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. The reception desk that records the video, An analysis unit analyzes the video recorded by the reception unit, A generation unit generates a summary based on the video analyzed by the analysis unit, A reading unit reads out the summary generated by the generation unit, The system includes a formatting unit that sets up a medical examination format based on the summary generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is The patient or a person related to them records a video of themselves talking about their symptoms while recording the affected area. The system according to feature 1.

3. The aforementioned reception unit is Record anything related to the symptoms. The system according to feature 1.

4. The aforementioned reception unit is Record the injury. The system according to feature 1.

5. The aforementioned reception unit is Record the injury. The system according to feature 1.

6. The aforementioned reading unit, Read the generated summary aloud The system according to feature 1.

7. The aforementioned formatting unit is Launch the consultation format via communication before the patient arrives. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the patient's emotions and adjusts the recording start time based on the estimated emotions. The system according to feature 1.

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