system

Abstract

The system according to this embodiment aims to share information about failure cases and enable doctors and patients to find appropriate countermeasures. [Solution] The system according to the embodiment comprises an upload unit, an analysis unit, a search unit, a provision unit, and a matching unit. The upload unit uploads information related to failure cases. The analysis unit analyzes the information uploaded by the upload unit. The search unit searches for similar cases based on the information analyzed by the analysis unit. The provision unit provides the information retrieved by the search unit. The matching unit performs matching based on the information provided by the provision 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, the method 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 prior art, there is a problem that information on failure cases is not sufficiently shared, making it difficult for doctors and patients to find appropriate countermeasures.

[0005] The system according to the embodiment aims to share information on failure cases and enable doctors and patients to find appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an upload unit, an analysis unit, a search unit, a provision unit, and a matching unit. The upload unit uploads information about failure cases. The analysis unit analyzes the information uploaded by the upload unit. The search unit searches for similar cases based on the information analyzed by the analysis unit. The provision unit provides the information retrieved by the search unit. The matching unit performs matching based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for the sharing of information on failure cases, enabling doctors and patients to find appropriate countermeasures. [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) The failure case consultation system according to an embodiment of the present invention is a system that provides a mechanism for doctors and patients to consult with each other regarding failure cases. When a failure case occurs, both doctors and patients experience great distress. Doctors search for countermeasures, but there is little information on failure cases in textbooks and papers, and patients feel isolated because there is no information from people in the same situation. This service allows users to upload their medical history, search a pre-built database for similar cases, and provides treatment methods, explanation methods, and prognosis. The aim is to create a world where both doctors and patients can access information on failure cases and consult with each other. Specifically, it consists of the following steps: First, doctors, patients, and families who have experienced a failure case upload information on the progress, treatment, explanation, and prognosis to a generating AI to create a database. Next, doctors, patients, and families who have experienced a failure case consult with the generating AI and search the database. The generating AI searches for similar cases based on the uploaded information and provides treatment methods, explanation methods, and prognosis. Furthermore, if the doctor, patient, or family who provided the information agree, matching and information sharing via chat is possible. This allows doctors to obtain appropriate recovery methods, and patients to connect with people in the same situation and gain psychological reassurance. This system improves the quality of medical care and increases patient benefits. Doctors can learn about valuable failure cases not found in textbooks, avoiding repeating the same mistakes and obtaining appropriate recovery methods. Patients can also find psychological reassurance by finding others in similar situations and learning about their experiences. As a result, the failure case consultation system enables efficient uploading, analysis, searching, provision, and matching of information related to failure cases.

[0029] The failure case consultation system according to the embodiment comprises an upload unit, an analysis unit, a search unit, a provision unit, and a matching unit. The upload unit uploads information related to failure cases. For example, the upload unit uploads information on the progress, treatment, explanation, and prognosis from doctors, patients, and families who have experienced failure cases. The upload unit can upload information according to file format and data size limitations, for example. The analysis unit analyzes the information uploaded by the upload unit. For example, the analysis unit analyzes the uploaded information using a generative AI and identifies similar cases. For example, the analysis unit can analyze the information based on the algorithm used and the accuracy of the analysis. The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, the search unit searches for similar cases from a database using a generative AI. For example, the search unit can search for information based on search conditions and the database used. The provision unit provides the information retrieved by the search unit. For example, the provision unit provides the retrieved information to doctors and patients. For example, the provision unit can provide information based on the format of the information to be provided and the recipient. The matching unit performs matching based on the information provided by the information provider unit. For example, if the doctor, patient, or family member providing the information agrees, the matching unit will match the information and share it via chat. The matching unit can also match information based on matching conditions and the algorithm used. This enables the failure case consultation system to efficiently upload, analyze, search, provide, and match information related to failure cases.

[0030] The upload section uploads information related to failed cases. Specifically, it allows doctors, patients, and families who have experienced failed cases to upload information on the course of treatment, explanations, and prognosis. The upload section can upload information according to file format and data size limitations, for example. Doctors can upload documents and image files, including medical records, treatment plans, and detailed surgical progress. Patients and families can upload their experiences and impressions during treatment, changes in symptoms, and explanations from doctors as text or audio files. The upload section centrally manages this information and has checking functions to maintain data integrity. For example, it automatically checks whether the uploaded information conforms to the specified format and whether all necessary items are included. In addition, the upload section protects data using encryption technology and manages access rights to protect the confidentiality of the information. This allows the upload section to efficiently and securely collect diverse information on failed cases and integrate it into the system's overall database. Furthermore, the upload section is designed to allow for easy input and uploading of information through a user interface, and is designed to be intuitive for users to operate. This creates an environment where doctors, patients, and families can provide information without stress.

[0031] The analysis unit analyzes the information uploaded by the upload unit. For example, the analysis unit uses generative AI to analyze the uploaded information and identify similar cases. Specifically, the generative AI uses natural language processing technology to analyze text data and extract case characteristics and treatment progress. For image data, image recognition technology is used to analyze lesion sites and surgical details. The analysis unit integrates this data and uses algorithms to grasp the overall picture of the case. For example, the generative AI learns patterns related to the course of the case, treatment methods, and prognosis, and identifies similar past cases. To improve the accuracy of the analysis, the generative AI can continuously learn new data and update its algorithms. Furthermore, the analysis unit has a function to visualize the analysis results, displaying them in graphs and charts for easy understanding by doctors and patients. This allows the analysis unit to quickly and accurately analyze uploaded information and provide reference information for identifying similar cases and treatment. The analysis unit also has an anomaly detection function, which can detect unusual patterns or abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, the search unit uses generative AI to search for similar cases in the database. Specifically, the generative AI searches for similar cases in the database quickly and accurately based on the characteristics and treatment progress of the cases provided by the analysis unit. Search criteria include case type, treatment method, prognosis, patient age, and gender. The search unit uses algorithms to combine these criteria to provide the best possible search results. For example, the generative AI vectorizes the text data of cases and performs similarity calculations to identify the most similar cases. Similarly, it can extract features from image data and perform similarity calculations to find similar images. The search unit displays search results in a ranking format, allowing users to access the most relevant information. Furthermore, the search unit has a filtering function, allowing users to narrow down search results based on specific criteria. This enables the search unit to quickly and accurately provide users with the information they need and support them in obtaining treatment reference information and advice. The search unit also has a function to save search history, allowing users to access previously searched information again. This allows the search unit to improve user convenience and support efficient information retrieval.

[0033] The information provider provides the information retrieved by the search unit. For example, the information provider provides the retrieved information to doctors and patients. Specifically, the information provider displays the search results to the user in a visually easy-to-understand format. For example, it can display the search results in a list or card format, making it easy to check the overview and details of each case. The information provider also has a function that allows users to download the search results in PDF or Excel format, enabling them to access the necessary information offline. Furthermore, the information provider can provide customized information depending on the recipient. For example, it can provide doctors with detailed treatment progress and prognosis information, and patients with easy-to-understand explanations and advice. The information provider also diversifies the methods of information delivery, and can deliver information in a way that suits the user's preferences, such as email, SMS, and in-app notifications. This allows the information provider to quickly and appropriately provide the information users need and support them in obtaining reference information and advice for treatment. The information provider can also collect feedback on the information provided and use it to improve the quality of the information and the methods of delivery. This allows the information provider to always provide high-quality information based on the latest information and improve user satisfaction.

[0034] The matching unit performs matching based on information provided by the information provider unit. Specifically, if the doctor, patient, or family providing the information agrees, matching is performed and information is shared via chat. The matching unit can match information based on matching conditions and the algorithm used, for example. Specifically, it uses a generative AI to identify the optimal matching partner based on case characteristics, treatment progress, and prognosis information. The matching unit can also perform more accurate matching by considering the user's profile information and past matching history. For example, by prioritizing matching with doctors and patients who have experienced the same disease or treatment, more beneficial information exchange becomes possible. When a match is made, the matching unit provides a dedicated chat room so that users can safely share information with each other. The chat room supports various communication methods, including not only text messages but also image and file sharing and video calls. This allows users to exchange detailed information and consult with each other. Furthermore, the matching unit has a function to automatically record chat content for later reference. This allows users to review past interactions and reconfirm necessary information. The matching unit also encrypts chat content and restricts access to protect user privacy. This allows the matching unit to provide an environment where users can share information with confidence and to promote the exchange of useful information regarding failed cases.

[0035] The upload unit allows doctors, patients, and families who have experienced failed treatment cases to upload information on the course of treatment, treatment, explanations, and prognosis. For example, a doctor who has experienced a failed treatment case can upload details of the treatment. Patients can also upload their responses and progress during treatment. Furthermore, families can upload the doctor's explanations and prognosis information. This enriches the database by allowing users to upload detailed information about failed treatment cases. Some or all of the above processing in the upload unit may be performed using AI, for example, or not. For example, the upload unit can input information from doctors, patients, and families into the AI, which can then organize and classify the information.

[0036] The analysis unit can analyze uploaded information and identify similar cases. For example, the analysis unit can analyze uploaded information using generative AI. For instance, the generative AI can identify similar cases based on the uploaded information. Furthermore, the analysis unit can analyze information based on the algorithm used and the accuracy of the analysis. For example, the analysis unit can analyze information using natural language processing technology to identify similar cases. Additionally, the analysis unit can analyze information using machine learning algorithms to identify similar cases. This improves the accuracy of identifying similar cases by analyzing uploaded information. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input uploaded information into the generative AI and have the generative AI perform the identification of similar cases.

[0037] The search unit can search for similar cases from the database based on the analyzed information. For example, the search unit can use generative AI to search for similar cases from the database. The search unit can also search for information based on search criteria and the database used. For example, the search unit can search for similar cases based on specific symptoms or treatment methods. Furthermore, the search unit can search for similar cases by cross-referencing information within the database. This allows for the provision of appropriate information by searching for similar cases based on the analyzed information. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit can input the analyzed information into the generative AI and have the generative AI perform the search for similar cases.

[0038] The information provider can provide the retrieved information to doctors and patients. For example, the provider can provide the retrieved information to doctors. The provider can also provide the retrieved information to patients. Furthermore, the provider can provide information based on the format and recipient of the information. For example, the provider can send the retrieved information to doctors via email. The provider can also provide the retrieved information to patients through a web portal. This allows doctors and patients to obtain appropriate countermeasures by providing the retrieved information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the retrieved information into AI and have the AI ​​perform the information distribution.

[0039] The matching unit can match individuals and families who provide information with each other and facilitate information sharing via chat, provided they consent. For example, if the information-providing physician consents, the matching unit can match individuals with other physicians and facilitate information sharing via chat. Furthermore, if the patient consents, the matching unit can match patients with others in similar situations and facilitate information sharing via chat. Additionally, if the family consents, the matching unit can match families with other families and facilitate information sharing via chat. This allows physicians and patients to consult directly with each other through matching and information sharing. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the consent of the information provider into the AI ​​and entrust the matching process to the AI.

[0040] The upload unit can analyze the user's past upload history and select the optimal upload method during the upload process. For example, the upload unit can automatically display information that the user has frequently uploaded in the past as a candidate. The upload unit can also prioritize suggesting upload methods (such as voice or text) that the user has used in the past. Furthermore, the upload unit can predict and suggest upload methods to be used at specific times based on the user's past upload history. In this way, the optimal upload method can be provided by analyzing past upload history. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the user's past upload history into AI and have the AI ​​select the optimal upload method.

[0041] The upload unit can filter uploads based on the user's current health status and treatment status. For example, if the user is currently undergoing treatment, the upload unit will filter to upload only relevant information. Furthermore, if the user's health is deteriorating, the upload unit can simplify the upload content, requesting only the minimum necessary information. Additionally, if the user's health is good, the upload unit can prompt them to upload more detailed information. This allows for the upload of appropriate information by filtering according to the user's health status and treatment status. Some or all of the above processing in the upload unit may be performed using AI, or not. For example, the upload unit can input the user's health status and treatment status into the AI ​​and have the AI ​​perform the filtering.

[0042] The upload unit can prioritize uploading highly relevant information by considering the user's geographical location during the upload process. For example, if the user is in a specific region, the upload unit will prioritize uploading information related to that region. Furthermore, if the user is traveling, the upload unit can prioritize uploading information related to their travel destination. Additionally, if the user is at home, the upload unit can prioritize uploading information about their home area. This allows for the prioritization of highly relevant information by considering geographical location. Some or all of the above processing in the upload unit may be performed using AI, or without AI. For example, the upload unit can input the user's geographical location information into an AI and have the AI ​​select highly relevant information.

[0043] The upload unit can analyze the user's social media activity and upload relevant information during the upload process. For example, the upload unit can upload relevant information based on information the user has shared on social media. It can also analyze the content of the user's social media posts and upload relevant information. Furthermore, the upload unit can upload relevant information by referring to the activities of the user's social media followers and friends. This allows for the efficient uploading of relevant information by analyzing social media activity. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the user's social media activity into AI and have the AI ​​select relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. It can also perform a simplified analysis on general information. Furthermore, the analysis unit can focus its analysis on information of particular interest to the user. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the information into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to medical information. It can also apply a statistical analysis algorithm to patient progress information. Furthermore, it can apply a machine learning-based analysis algorithm to information about treatment methods. By applying an analysis algorithm appropriate to the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category of information into a generative AI and have the generative AI select and apply an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can simultaneously analyze information submitted within a specific period. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the information submission date into the generation AI and have the generation AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can evaluate the relevance of the information and perform the analysis in the optimal order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0048] The search unit can improve search accuracy by considering the interrelationships of information during a search. For example, the search unit can group related information and provide search results. The search unit can also evaluate the interrelationships of information and provide the most suitable search results. Furthermore, the search unit can prioritize displaying highly relevant information based on the interrelationships of information. This improves search accuracy by considering the interrelationships of information. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input the interrelationships of information into a generative AI and have the generative AI perform the search accuracy improvement.

[0049] The search unit can perform searches while considering the attribute information of the information provider. For example, the search unit can search for relevant information based on the information provider's field of expertise. The search unit can also prioritize displaying highly reliable information based on the information provider's years of experience. Furthermore, the search unit can search for highly relevant information based on the information provider's past posting history. In this way, by considering the attribute information of the information provider, highly reliable information can be provided. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input the information provider's attribute information into a generation AI and leave the execution of the search to the generation AI.

[0050] The search unit can perform searches while considering the geographical distribution of information. For example, the search unit can prioritize displaying relevant information based on the user's current location. The search unit can also evaluate the geographical distribution of information and provide optimal search results. Furthermore, the search unit can prioritize displaying information that is geographically close. In this way, by considering the geographical distribution of information, it is possible to provide highly relevant information. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input the geographical distribution of information into a generative AI and leave the execution of the search to the generative AI.

[0051] The search unit can improve the accuracy of searches by referring to related literature during the search process. For example, the search unit can supplement search results based on related literature. Furthermore, the search unit can prioritize displaying highly reliable information based on citation information in related literature. In addition, the search unit can analyze the content of related literature to provide optimal search results. This improves search accuracy by referring to related literature. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input related literature into a generative AI and entrust the execution of the search to the generative AI.

[0052] The information delivery unit can select the optimal delivery method by referring to the user's past information delivery history when providing information. For example, the information delivery unit can prioritize suggesting information delivery methods that the user has used in the past. The information delivery unit can also predict and suggest delivery methods to be used during specific time periods based on the user's past information delivery history. Furthermore, the information delivery unit can analyze the user's past information delivery history and suggest the most efficient delivery method. In this way, the optimal information delivery method can be provided by referring to past information delivery history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past information delivery history into AI and have the AI ​​select the optimal delivery method.

[0053] The information provider can customize the means of providing information based on the user's current health condition. For example, if the user's health condition is deteriorating, the provider can provide a simplified method of information provision. If the user's health condition is good, the provider can also provide a detailed method of information provision. Furthermore, the provider can select the most appropriate means of information provision according to the user's health condition. This allows for the provision of appropriate information by customizing the means of information provision according to the user's health condition. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's health condition into the AI ​​and have the AI ​​perform the customization of the means of information provision.

[0054] The information provider can select the most appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific region, the provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the provider can prioritize providing information related to their travel destination. Additionally, if the user is at home, the provider can prioritize providing information about their home area. This allows for the provision of highly relevant information by considering geographical location. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the user's geographical location into an AI and have the AI ​​select the most appropriate method of information delivery.

[0055] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the provider can provide relevant information based on information shared by the user on social media. The provider can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the provider can provide relevant information by referring to the activities of the user's social media followers and friends. In this way, relevant information can be efficiently provided by analyzing social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into AI and have the AI ​​propose methods for providing information.

[0056] The matching unit can select the optimal matching method by referring to the user's past matching history during the matching process. For example, the matching unit may prioritize suggesting matching methods that the user has used in the past. The matching unit can also predict and suggest matching methods to be used during specific time periods based on the user's past matching history. Furthermore, the matching unit can analyze the user's past matching history and suggest the most efficient matching method. In this way, the optimal matching method can be provided by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past matching history into an AI and have the AI ​​select the optimal matching method.

[0057] The matching unit can customize the matching method based on the user's current health status during the matching process. For example, if the user's health is deteriorating, the matching unit can provide a simplified matching method. Furthermore, if the user's health is good, the matching unit can provide a more detailed matching method. In addition, the matching unit can select the optimal matching method according to the user's health status. This allows for appropriate matching by customizing the matching method according to the user's health status. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's health status into the AI ​​and have the AI ​​customize the matching method.

[0058] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user is in a specific region, the matching unit will prioritize matching information related to that region. Furthermore, if the user is traveling, the matching unit can prioritize matching information related to their travel destination. Additionally, if the user is at home, the matching unit can prioritize matching information around their home. This allows for the provision of highly relevant information by considering geographical location information. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input the user's geographical location information into an AI and have the AI ​​select the optimal matching method.

[0059] The matching unit can analyze a user's social media activity and propose matching methods during the matching process. For example, the matching unit can match relevant information based on information shared by the user on social media. It can also analyze the content of a user's social media posts and match relevant information. Furthermore, the matching unit can refer to the activities of the user's social media followers and friends to match relevant information. This allows for efficient matching of relevant information by analyzing social media activity. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity into an AI and have the AI ​​propose matching methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The failure case consultation system can also include a feedback section. This feedback section allows doctors and patients to provide feedback on the information provided. For example, doctors can provide feedback on the effectiveness of the treatment methods offered, while patients can provide feedback on how helpful the information was. Furthermore, the feedback section can update the database based on the feedback, improving the accuracy of the information. This allows for continuous improvement of information quality through feedback.

[0062] The failure case consultation system can also include a notification function. This notification function can notify doctors and patients of important information and updates. For example, if a doctor uploads a new treatment method, other doctors with related cases can be notified. Similarly, if a patient uploads new progress information, other patients with the same case can be notified. Furthermore, the notification function can customize the frequency and method of notifications based on user settings. This allows for the timely sharing of important information.

[0063] The failure case consultation system can also be equipped with an evaluation unit. This evaluation unit can assess the reliability and usefulness of the information provided. For example, other physicians can evaluate the reliability of treatment methods provided by other physicians. Similarly, other patients can evaluate the usefulness of progress information provided by other patients. Furthermore, the evaluation unit can calculate a reliability score based on the evaluation results and provide it to the user. This allows for the priority provision of highly reliable information.

[0064] The failure case consultation system can also be equipped with a translation function. This translation function can translate uploaded information into multiple languages. For example, it can translate treatment methods provided by doctors in English into Japanese. It can also translate progress information provided by patients in Japanese into English. Furthermore, the translation function can use a specialized terminology dictionary to improve translation accuracy. This allows information to be shared among users who speak different languages.

[0065] The failure case consultation system can also be equipped with an alert function. This alert function can send alerts to doctors and patients when specific conditions are met. For example, if multiple failure cases are reported with the same treatment method, it can send an alert to the relevant doctors. It can also send an alert to the attending physician if an abnormality is detected in the patient's progress information. Furthermore, the alert function can change the notification method depending on the importance of the alert. This allows for the rapid sharing of important information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The upload section allows users to upload information about failed cases. For example, doctors, patients, and families who have experienced failed cases can upload information about the course of treatment, explanations, and prognosis. The upload section allows users to upload information according to file format and data size limitations. Step 2: The analysis unit analyzes the information uploaded by the upload unit. For example, it analyzes the uploaded information using a generative AI to identify similar cases. The analysis unit can analyze the information based on the algorithm used and the accuracy of the analysis. Step 3: The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, it may use a generative AI to search for similar cases in a database. The search unit can search for information based on search conditions and the database used. Step 4: The providing unit provides the information retrieved by the searching unit. For example, it provides the retrieved information to doctors or patients. The providing unit can provide information based on the format and recipient of the information to be provided. Step 5: The matching unit performs matching based on the information provided by the information provider. For example, if the doctor, patient, or family who provided the information agree, matching is performed and information is shared via chat. The matching unit can match information based on matching conditions and the algorithm used.

[0068] (Example of form 2) The failure case consultation system according to an embodiment of the present invention is a system that provides a mechanism for doctors and patients to consult with each other regarding failure cases. When a failure case occurs, both doctors and patients experience great distress. Doctors search for countermeasures, but there is little information on failure cases in textbooks and papers, and patients feel isolated because there is no information from people in the same situation. This service allows users to upload their medical history, search a pre-built database for similar cases, and provides treatment methods, explanation methods, and prognosis. The aim is to create a world where both doctors and patients can access information on failure cases and consult with each other. Specifically, it consists of the following steps: First, doctors, patients, and families who have experienced a failure case upload information on the progress, treatment, explanation, and prognosis to a generating AI to create a database. Next, doctors, patients, and families who have experienced a failure case consult with the generating AI and search the database. The generating AI searches for similar cases based on the uploaded information and provides treatment methods, explanation methods, and prognosis. Furthermore, if the doctor, patient, or family who provided the information agree, matching and information sharing via chat is possible. This allows doctors to obtain appropriate recovery methods, and patients to connect with people in the same situation and gain psychological reassurance. This system improves the quality of medical care and increases patient benefits. Doctors can learn about valuable failure cases not found in textbooks, avoiding repeating the same mistakes and obtaining appropriate recovery methods. Patients can also find psychological reassurance by finding others in similar situations and learning about their experiences. As a result, the failure case consultation system enables efficient uploading, analysis, searching, provision, and matching of information related to failure cases.

[0069] The failure case consultation system according to the embodiment comprises an upload unit, an analysis unit, a search unit, a provision unit, and a matching unit. The upload unit uploads information related to failure cases. For example, the upload unit uploads information on the progress, treatment, explanation, and prognosis from doctors, patients, and families who have experienced failure cases. The upload unit can upload information according to file format and data size limitations, for example. The analysis unit analyzes the information uploaded by the upload unit. For example, the analysis unit analyzes the uploaded information using a generative AI and identifies similar cases. For example, the analysis unit can analyze the information based on the algorithm used and the accuracy of the analysis. The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, the search unit searches for similar cases from a database using a generative AI. For example, the search unit can search for information based on search conditions and the database used. The provision unit provides the information retrieved by the search unit. For example, the provision unit provides the retrieved information to doctors and patients. For example, the provision unit can provide information based on the format of the information to be provided and the recipient. The matching unit performs matching based on the information provided by the information provider unit. For example, if the doctor, patient, or family member providing the information agrees, the matching unit will match the information and share it via chat. The matching unit can also match information based on matching conditions and the algorithm used. This enables the failure case consultation system to efficiently upload, analyze, search, provide, and match information related to failure cases.

[0070] The upload section uploads information related to failed cases. Specifically, it allows doctors, patients, and families who have experienced failed cases to upload information on the course of treatment, explanations, and prognosis. The upload section can upload information according to file format and data size limitations, for example. Doctors can upload documents and image files, including medical records, treatment plans, and detailed surgical progress. Patients and families can upload their experiences and impressions during treatment, changes in symptoms, and explanations from doctors as text or audio files. The upload section centrally manages this information and has checking functions to maintain data integrity. For example, it automatically checks whether the uploaded information conforms to the specified format and whether all necessary items are included. In addition, the upload section protects data using encryption technology and manages access rights to protect the confidentiality of the information. This allows the upload section to efficiently and securely collect diverse information on failed cases and integrate it into the system's overall database. Furthermore, the upload section is designed to allow for easy input and uploading of information through a user interface, and is designed to be intuitive for users to operate. This creates an environment where doctors, patients, and families can provide information without stress.

[0071] The analysis unit analyzes the information uploaded by the upload unit. For example, the analysis unit uses generative AI to analyze the uploaded information and identify similar cases. Specifically, the generative AI uses natural language processing technology to analyze text data and extract case characteristics and treatment progress. For image data, image recognition technology is used to analyze lesion sites and surgical details. The analysis unit integrates this data and uses algorithms to grasp the overall picture of the case. For example, the generative AI learns patterns related to the course of the case, treatment methods, and prognosis, and identifies similar past cases. To improve the accuracy of the analysis, the generative AI can continuously learn new data and update its algorithms. Furthermore, the analysis unit has a function to visualize the analysis results, displaying them in graphs and charts for easy understanding by doctors and patients. This allows the analysis unit to quickly and accurately analyze uploaded information and provide reference information for identifying similar cases and treatment. The analysis unit also has an anomaly detection function, which can detect unusual patterns or abnormal data early and issue warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, the search unit uses generative AI to search for similar cases in the database. Specifically, the generative AI searches for similar cases in the database quickly and accurately based on the characteristics and treatment progress of the cases provided by the analysis unit. Search criteria include case type, treatment method, prognosis, patient age, and gender. The search unit uses algorithms to combine these criteria to provide the best possible search results. For example, the generative AI vectorizes the text data of cases and performs similarity calculations to identify the most similar cases. Similarly, it can extract features from image data and perform similarity calculations to find similar images. The search unit displays search results in a ranking format, allowing users to access the most relevant information. Furthermore, the search unit has a filtering function, allowing users to narrow down search results based on specific criteria. This enables the search unit to quickly and accurately provide users with the information they need and support them in obtaining treatment reference information and advice. The search unit also has a function to save search history, allowing users to access previously searched information again. This allows the search unit to improve user convenience and support efficient information retrieval.

[0073] The information provider provides the information retrieved by the search unit. For example, the information provider provides the retrieved information to doctors and patients. Specifically, the information provider displays the search results to the user in a visually easy-to-understand format. For example, it can display the search results in a list or card format, making it easy to check the overview and details of each case. The information provider also has a function that allows users to download the search results in PDF or Excel format, enabling them to access the necessary information offline. Furthermore, the information provider can provide customized information depending on the recipient. For example, it can provide doctors with detailed treatment progress and prognosis information, and patients with easy-to-understand explanations and advice. The information provider also diversifies the methods of information delivery, and can deliver information in a way that suits the user's preferences, such as email, SMS, and in-app notifications. This allows the information provider to quickly and appropriately provide the information users need and support them in obtaining reference information and advice for treatment. The information provider can also collect feedback on the information provided and use it to improve the quality of the information and the methods of delivery. This allows the information provider to always provide high-quality information based on the latest information and improve user satisfaction.

[0074] The matching unit performs matching based on information provided by the information provider unit. Specifically, if the doctor, patient, or family providing the information agrees, matching is performed and information is shared via chat. The matching unit can match information based on matching conditions and the algorithm used, for example. Specifically, it uses a generative AI to identify the optimal matching partner based on case characteristics, treatment progress, and prognosis information. The matching unit can also perform more accurate matching by considering the user's profile information and past matching history. For example, by prioritizing matching with doctors and patients who have experienced the same disease or treatment, more beneficial information exchange becomes possible. When a match is made, the matching unit provides a dedicated chat room so that users can safely share information with each other. The chat room supports various communication methods, including not only text messages but also image and file sharing and video calls. This allows users to exchange detailed information and consult with each other. Furthermore, the matching unit has a function to automatically record chat content for later reference. This allows users to review past interactions and reconfirm necessary information. The matching unit also encrypts chat content and restricts access to protect user privacy. This allows the matching unit to provide an environment where users can share information with confidence and to promote the exchange of useful information regarding failed cases.

[0075] The upload unit allows doctors, patients, and families who have experienced failed treatment cases to upload information on the course of treatment, treatment, explanations, and prognosis. For example, a doctor who has experienced a failed treatment case can upload details of the treatment. Patients can also upload their responses and progress during treatment. Furthermore, families can upload the doctor's explanations and prognosis information. This enriches the database by allowing users to upload detailed information about failed treatment cases. Some or all of the above processing in the upload unit may be performed using AI, for example, or not. For example, the upload unit can input information from doctors, patients, and families into the AI, which can then organize and classify the information.

[0076] The analysis unit can analyze uploaded information and identify similar cases. For example, the analysis unit can analyze uploaded information using generative AI. For instance, the generative AI can identify similar cases based on the uploaded information. Furthermore, the analysis unit can analyze information based on the algorithm used and the accuracy of the analysis. For example, the analysis unit can analyze information using natural language processing technology to identify similar cases. Additionally, the analysis unit can analyze information using machine learning algorithms to identify similar cases. This improves the accuracy of identifying similar cases by analyzing uploaded information. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input uploaded information into the generative AI and have the generative AI perform the identification of similar cases.

[0077] The search unit can search for similar cases from the database based on the analyzed information. For example, the search unit can use generative AI to search for similar cases from the database. The search unit can also search for information based on search criteria and the database used. For example, the search unit can search for similar cases based on specific symptoms or treatment methods. Furthermore, the search unit can search for similar cases by cross-referencing information within the database. This allows for the provision of appropriate information by searching for similar cases based on the analyzed information. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit can input the analyzed information into the generative AI and have the generative AI perform the search for similar cases.

[0078] The information provider can provide the retrieved information to doctors and patients. For example, the provider can provide the retrieved information to doctors. The provider can also provide the retrieved information to patients. Furthermore, the provider can provide information based on the format and recipient of the information. For example, the provider can send the retrieved information to doctors via email. The provider can also provide the retrieved information to patients through a web portal. This allows doctors and patients to obtain appropriate countermeasures by providing the retrieved information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the retrieved information into AI and have the AI ​​perform the information distribution.

[0079] The matching unit can match individuals and families who provide information with each other and facilitate information sharing via chat, provided they consent. For example, if the information-providing physician consents, the matching unit can match individuals with other physicians and facilitate information sharing via chat. Furthermore, if the patient consents, the matching unit can match patients with others in similar situations and facilitate information sharing via chat. Additionally, if the family consents, the matching unit can match families with other families and facilitate information sharing via chat. This allows physicians and patients to consult directly with each other through matching and information sharing. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the consent of the information provider into the AI ​​and entrust the matching process to the AI.

[0080] The upload unit can estimate the user's emotions and adjust the upload timing based on the estimated emotions. For example, if the user is stressed, the upload unit can simplify the upload process and make it faster. If the user is relaxed, the upload unit can also provide detailed input options and allow them to customize the upload content. Furthermore, if the user is in a hurry, the upload unit can prioritize voice input and allow information to be uploaded quickly. This reduces the user's burden by adjusting the upload timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 upload unit may be performed using AI or not. For example, the upload unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and timing adjustment.

[0081] The upload unit can analyze the user's past upload history and select the optimal upload method during the upload process. For example, the upload unit can automatically display information that the user has frequently uploaded in the past as a candidate. The upload unit can also prioritize suggesting upload methods (such as voice or text) that the user has used in the past. Furthermore, the upload unit can predict and suggest upload methods to be used at specific times based on the user's past upload history. In this way, the optimal upload method can be provided by analyzing past upload history. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the user's past upload history into AI and have the AI ​​select the optimal upload method.

[0082] The upload unit can filter uploads based on the user's current health status and treatment status. For example, if the user is currently undergoing treatment, the upload unit will filter to upload only relevant information. Furthermore, if the user's health is deteriorating, the upload unit can simplify the upload content, requesting only the minimum necessary information. Additionally, if the user's health is good, the upload unit can prompt them to upload more detailed information. This allows for the upload of appropriate information by filtering according to the user's health status and treatment status. Some or all of the above processing in the upload unit may be performed using AI, or not. For example, the upload unit can input the user's health status and treatment status into the AI ​​and have the AI ​​perform the filtering.

[0083] The upload unit can estimate the user's emotions and determine the priority of information to upload based on the estimated emotions. For example, if the user is stressed, the upload unit will prioritize uploading important information. It can also prompt the user to upload more detailed information if they are relaxed. Furthermore, if the user is in a hurry, the upload unit can upload only the most important information. This allows for the priority uploading of important information by prioritizing it according to the user'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 upload unit may be performed using AI or not. For example, the upload unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0084] The upload unit can prioritize uploading highly relevant information by considering the user's geographical location during the upload process. For example, if the user is in a specific region, the upload unit will prioritize uploading information related to that region. Furthermore, if the user is traveling, the upload unit can prioritize uploading information related to their travel destination. Additionally, if the user is at home, the upload unit can prioritize uploading information about their home area. This allows for the prioritization of highly relevant information by considering geographical location. Some or all of the above processing in the upload unit may be performed using AI, or without AI. For example, the upload unit can input the user's geographical location information into an AI and have the AI ​​select highly relevant information.

[0085] The upload unit can analyze the user's social media activity and upload relevant information during the upload process. For example, the upload unit can upload relevant information based on information the user has shared on social media. It can also analyze the content of the user's social media posts and upload relevant information. Furthermore, the upload unit can upload relevant information by referring to the activities of the user's social media followers and friends. This allows for the efficient uploading of relevant information by analyzing social media activity. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the user's social media activity into AI and have the AI ​​select relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit will perform a detailed analysis on important information. It can also perform a simplified analysis on general information. Furthermore, the analysis unit can focus its analysis on information of particular interest to the user. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the information into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to medical information. It can also apply a statistical analysis algorithm to patient progress information. Furthermore, it can apply a machine learning-based analysis algorithm to information about treatment methods. By applying an analysis algorithm appropriate to the category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category of information into a generative AI and have the generative AI select and apply an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. 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-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent information. It can also postpone the analysis of older information. Furthermore, the analysis unit can simultaneously analyze information submitted within a specific period. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the information submission date into the generation AI and have the generation AI determine the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can evaluate the relevance of the information and perform the analysis in the optimal order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the information into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0092] The search unit can estimate the user's emotions and adjust the search criteria based on those emotions. For example, if the user is stressed, the search unit can provide simple and highly visible search results. If the user is relaxed, the search unit can provide detailed search results. Furthermore, if the user is in a hurry, the search unit can provide concise search results. By adjusting the search criteria according to the user's emotions, the system can provide the most suitable search results for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit can input user emotion data into the generative AI and have the generative AI adjust the search criteria.

[0093] The search unit can improve search accuracy by considering the interrelationships of information during a search. For example, the search unit can group related information and provide search results. The search unit can also evaluate the interrelationships of information and provide the most suitable search results. Furthermore, the search unit can prioritize displaying highly relevant information based on the interrelationships of information. This improves search accuracy by considering the interrelationships of information. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input the interrelationships of information into a generative AI and have the generative AI perform the search accuracy improvement.

[0094] The search unit can perform searches while considering the attribute information of the information provider. For example, the search unit can search for relevant information based on the information provider's field of expertise. The search unit can also prioritize displaying highly reliable information based on the information provider's years of experience. Furthermore, the search unit can search for highly relevant information based on the information provider's past posting history. In this way, by considering the attribute information of the information provider, highly reliable information can be provided. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input the information provider's attribute information into a generation AI and leave the execution of the search to the generation AI.

[0095] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit will prioritize displaying important information. If the user is relaxed, the search unit can also display search results containing detailed information. Furthermore, if the user is in a hurry, the search unit can display concise search results. In this way, by adjusting the display order of search results according to the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit can input user emotion data into the generative AI and have the generative AI adjust the display order of search results.

[0096] The search unit can perform searches while considering the geographical distribution of information. For example, the search unit can prioritize displaying relevant information based on the user's current location. The search unit can also evaluate the geographical distribution of information and provide optimal search results. Furthermore, the search unit can prioritize displaying information that is geographically close. In this way, by considering the geographical distribution of information, it is possible to provide highly relevant information. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input the geographical distribution of information into a generative AI and leave the execution of the search to the generative AI.

[0097] The search unit can improve the accuracy of searches by referring to related literature during the search process. For example, the search unit can supplement search results based on related literature. Furthermore, the search unit can prioritize displaying highly reliable information based on citation information in related literature. In addition, the search unit can analyze the content of related literature to provide optimal search results. This improves search accuracy by referring to related literature. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input related literature into a generative AI and entrust the execution of the search to the generative AI.

[0098] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of delivery. In this way, by adjusting the method of information delivery according to the user's emotions, the information provider can deliver the most appropriate information to the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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 information provider may be performed using AI or not. For example, the information provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the method of information delivery.

[0099] The information delivery unit can select the optimal delivery method by referring to the user's past information delivery history when providing information. For example, the information delivery unit can prioritize suggesting information delivery methods that the user has used in the past. The information delivery unit can also predict and suggest delivery methods to be used during specific time periods based on the user's past information delivery history. Furthermore, the information delivery unit can analyze the user's past information delivery history and suggest the most efficient delivery method. In this way, the optimal information delivery method can be provided by referring to past information delivery history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's past information delivery history into AI and have the AI ​​select the optimal delivery method.

[0100] The information provider can customize the means of providing information based on the user's current health condition. For example, if the user's health condition is deteriorating, the provider can provide a simplified method of information provision. If the user's health condition is good, the provider can also provide a detailed method of information provision. Furthermore, the provider can select the most appropriate means of information provision according to the user's health condition. This allows for the provision of appropriate information by customizing the means of information provision according to the user's health condition. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's health condition into the AI ​​and have the AI ​​perform the customization of the means of information provision.

[0101] The information delivery unit can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information delivery unit will prioritize providing important information. If the user is relaxed, the information delivery unit can also provide a delivery method that includes detailed information. Furthermore, if the user is in a hurry, the information delivery unit can provide a delivery method that gets straight to the point. In this way, by determining the priority of information delivery according to the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using 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 information delivery unit may be performed using AI or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI perform the determination of information delivery priorities.

[0102] The information provider can select the most appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific region, the provider can prioritize providing information related to that region. Furthermore, if the user is traveling, the provider can prioritize providing information related to their travel destination. Additionally, if the user is at home, the provider can prioritize providing information about their home area. This allows for the provision of highly relevant information by considering geographical location. Some or all of the above processing in the information provider may be performed using AI, or not. For example, the information provider can input the user's geographical location into an AI and have the AI ​​select the most appropriate method of information delivery.

[0103] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the provider can provide relevant information based on information shared by the user on social media. The provider can also analyze the content of the user's social media posts and provide relevant information. Furthermore, the provider can provide relevant information by referring to the activities of the user's social media followers and friends. In this way, relevant information can be efficiently provided by analyzing social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into AI and have the AI ​​propose methods for providing information.

[0104] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visual matching method. If the user is relaxed, the matching unit can also provide a matching method that includes detailed information. Furthermore, if the user is in a hurry, the matching unit can provide a concise matching method. By adjusting the matching method according to the user's emotions, the optimal match for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into the generative AI and have the generative AI adjust the matching method.

[0105] The matching unit can select the optimal matching method by referring to the user's past matching history during the matching process. For example, the matching unit may prioritize suggesting matching methods that the user has used in the past. The matching unit can also predict and suggest matching methods to be used during specific time periods based on the user's past matching history. Furthermore, the matching unit can analyze the user's past matching history and suggest the most efficient matching method. In this way, the optimal matching method can be provided by referring to past matching history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past matching history into an AI and have the AI ​​select the optimal matching method.

[0106] The matching unit can customize the matching method based on the user's current health status during the matching process. For example, if the user's health is deteriorating, the matching unit can provide a simplified matching method. Furthermore, if the user's health is good, the matching unit can provide a more detailed matching method. In addition, the matching unit can select the optimal matching method according to the user's health status. This allows for appropriate matching by customizing the matching method according to the user's health status. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's health status into the AI ​​and have the AI ​​customize the matching method.

[0107] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize matching important information. If the user is relaxed, the matching unit can also provide a matching method that includes detailed information. Furthermore, if the user is in a hurry, the matching unit can provide a concise matching method. This allows for prioritizing important information by determining matching priorities according to the user'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-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI determine the matching priorities.

[0108] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user is in a specific region, the matching unit will prioritize matching information related to that region. Furthermore, if the user is traveling, the matching unit can prioritize matching information related to their travel destination. Additionally, if the user is at home, the matching unit can prioritize matching information around their home. This allows for the provision of highly relevant information by considering geographical location information. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input the user's geographical location information into an AI and have the AI ​​select the optimal matching method.

[0109] The matching unit can analyze a user's social media activity and propose matching methods during the matching process. For example, the matching unit can match relevant information based on information shared by the user on social media. It can also analyze the content of a user's social media posts and match relevant information. Furthermore, the matching unit can refer to the activities of the user's social media followers and friends to match relevant information. This allows for efficient matching of relevant information by analyzing social media activity. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity into an AI and have the AI ​​propose matching methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The failure case consultation system can also include a feedback section. This feedback section allows doctors and patients to provide feedback on the information provided. For example, doctors can provide feedback on the effectiveness of the treatment methods offered, while patients can provide feedback on how helpful the information was. Furthermore, the feedback section can update the database based on the feedback, improving the accuracy of the information. This allows for continuous improvement of information quality through feedback.

[0112] The failure case consultation system can also include a notification function. This notification function can notify doctors and patients of important information and updates. For example, if a doctor uploads a new treatment method, other doctors with related cases can be notified. Similarly, if a patient uploads new progress information, other patients with the same case can be notified. Furthermore, the notification function can customize the frequency and method of notifications based on user settings. This allows for the timely sharing of important information.

[0113] The failure case consultation system can also be equipped with an evaluation unit. This evaluation unit can assess the reliability and usefulness of the information provided. For example, other physicians can evaluate the reliability of treatment methods provided by other physicians. Similarly, other patients can evaluate the usefulness of progress information provided by other patients. Furthermore, the evaluation unit can calculate a reliability score based on the evaluation results and provide it to the user. This allows for the priority provision of highly reliable information.

[0114] The failure case consultation system can also be equipped with a translation function. This translation function can translate uploaded information into multiple languages. For example, it can translate treatment methods provided by doctors in English into Japanese. It can also translate progress information provided by patients in Japanese into English. Furthermore, the translation function can use a specialized terminology dictionary to improve translation accuracy. This allows information to be shared among users who speak different languages.

[0115] The failure case consultation system can also be equipped with an alert function. This alert function can send alerts to doctors and patients when specific conditions are met. For example, if multiple failure cases are reported with the same treatment method, it can send an alert to the relevant doctors. It can also send an alert to the attending physician if an abnormality is detected in the patient's progress information. Furthermore, the alert function can change the notification method depending on the importance of the alert. This allows for the rapid sharing of important information.

[0116] The failure case consultation system can further adjust how information is displayed based on the user's emotions using an emotion estimation function. For example, if the user is stressed, simple and highly visible information can be displayed. If the user is relaxed, detailed information can be displayed. Furthermore, if the user is in a hurry, concise information can be displayed. This allows for the display of optimal information according to the user's emotions.

[0117] The failure case consultation system can further filter search results based on the user's emotions using an emotion estimation function. For example, if the user is stressed, highly reliable information can be prioritized. If the user is relaxed, search results containing detailed information can be displayed. Furthermore, if the user is in a hurry, concise search results can be displayed. This allows the system to provide optimal search results according to the user's emotions.

[0118] The failure case consultation system can further utilize emotion estimation functionality to adjust the way information is delivered based on the user's emotions. For example, if the user is stressed, it can provide a simple and highly visual information presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point information presentation. This allows for the provision of optimal information according to the user's emotions.

[0119] The failure case consultation system can further adjust its matching method based on the user's emotions using an emotion estimation function. For example, if the user is nervous, it can provide a simple and highly visual matching method. If the user is relaxed, it can provide a matching method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise matching method. This allows the system to provide the optimal match according to the user's emotions.

[0120] The failure case consultation system can further utilize emotion estimation to prioritize information based on the user's emotions. For example, if the user is stressed, important information can be provided preferentially. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, concise information can be provided. By prioritizing information according to the user's emotions, important information can be provided preferentially.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The upload section allows users to upload information about failed cases. For example, doctors, patients, and families who have experienced failed cases can upload information about the course of treatment, explanations, and prognosis. The upload section allows users to upload information according to file format and data size limitations. Step 2: The analysis unit analyzes the information uploaded by the upload unit. For example, it analyzes the uploaded information using a generative AI to identify similar cases. The analysis unit can analyze the information based on the algorithm used and the accuracy of the analysis. Step 3: The search unit searches for similar cases based on the information analyzed by the analysis unit. For example, it may use a generative AI to search for similar cases in a database. The search unit can search for information based on search conditions and the database used. Step 4: The providing unit provides the information retrieved by the searching unit. For example, it provides the retrieved information to doctors or patients. The providing unit can provide information based on the format and recipient of the information to be provided. Step 5: The matching unit performs matching based on the information provided by the information provider. For example, if the doctor, patient, or family who provided the information agree, matching is performed and information is shared via chat. The matching unit can match information based on matching conditions and the algorithm used.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the upload unit, analysis unit, search unit, provision unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the upload unit uploads information about failure cases using the receiving device 38 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information. The search unit is implemented in the identification processing unit 290 of the data processing unit 12 and searches for similar cases based on the analyzed information. The provision unit provides the retrieved information using the output device 40 of the smart device 14. The matching unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs matching based on the provided information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the upload unit, analysis unit, search unit, provision unit, and matching unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the upload unit uploads information about failure cases using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the uploaded information. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and searches for similar cases based on the analyzed information. The provision unit provides the retrieved information using, for example, the speaker 240 of the smart glasses 214. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs matching based on the provided information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the upload unit, analysis unit, search unit, provision unit, and matching unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the upload unit uploads information about failure cases using the microphone 238 of the headset terminal 314. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for similar cases based on the analyzed information. The provision unit provides the retrieved information using, for example, the speaker 240 of the headset terminal 314. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs matching based on the provided information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the upload unit, analysis unit, search unit, provision unit, and matching unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the upload unit uploads information about failure cases using the microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the uploaded information. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for similar cases based on the analyzed information. The provision unit provides the retrieved information using, for example, the speaker 240 of the robot 414. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs matching based on the provided information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) An upload section for uploading information about failure cases, An analysis unit analyzes the information uploaded by the aforementioned upload unit, A search unit searches for similar cases based on the information analyzed by the aforementioned analysis unit, A providing unit that provides the information retrieved by the aforementioned searching unit, The system includes a matching unit that performs matching based on the information provided by the aforementioned providing unit. A system characterized by the following features. (Note 2) The aforementioned upload unit, Doctors, patients, and families who have experienced failed cases can upload information about the course of treatment, explanations, and prognosis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The uploaded information is analyzed to identify similar cases. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned search unit, Search the database for similar cases based on the analyzed information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the searched information to doctors and patients. The system described in Appendix 1, characterized by the features described herein. (Note 6) The matching unit is If the doctor, patient, or family member providing the information agrees, they will be matched and the information will be shared via chat. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned upload unit, It estimates the user's emotions and adjusts the upload timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned upload unit, During the upload process, the system analyzes the user's past upload history and selects the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned upload unit, During upload, filtering is performed based on the user's current health status and treatment status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned upload unit, It estimates the user's emotions and prioritizes the information to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned upload unit, During uploads, the system prioritizes uploading highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned upload unit, During the upload process, the system analyzes the user's social media activity and uploads relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. 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 depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, consider the interrelationships between pieces of information to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When performing a search, the system takes into account the attribute information of the information provider. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned search unit, When performing a search, consider the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned search unit, When searching, refer to related literature to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the system selects the most suitable method of delivery by referring to the user's past information provision history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The matching unit is It estimates the user's emotions and adjusts the matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The matching unit is During the matching process, the system selects the optimal matching method by referring to the user's past matching history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The matching unit is During the matching process, the matching method is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The matching unit is During the matching process, the system selects the optimal matching method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The matching unit is During the matching process, we analyze the user's social media activity and suggest matching methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. An upload section for uploading information about failure cases, An analysis unit analyzes the information uploaded by the aforementioned upload unit, A search unit searches for similar cases based on the information analyzed by the aforementioned analysis unit, A providing unit that provides the information retrieved by the aforementioned searching unit, The system includes a matching unit that performs matching based on the information provided by the aforementioned providing unit. A system characterized by the following features.

2. The aforementioned upload unit, Doctors, patients, and families who have experienced failed cases can upload information about the course of treatment, explanations, and prognosis. The system according to feature 1.

3. The aforementioned analysis unit, The uploaded information is analyzed to identify similar cases. The system according to feature 1.

4. The aforementioned search unit, Search the database for similar cases based on the analyzed information. The system according to feature 1.

5. The aforementioned supply unit is, Provide the searched information to doctors and patients. The system according to feature 1.

6. The matching unit is If the doctor, patient, or family member providing the information agrees, they will be matched and the information will be shared via chat. The system according to feature 1.

7. The aforementioned upload unit, It estimates the user's emotions and adjusts the upload timing based on those emotions. The system according to feature 1.

8. The aforementioned upload unit, During the upload process, the system analyzes the user's past upload history and selects the optimal upload method. The system according to feature 1.

Images