system

Abstract

The system according to the embodiment aims to analyze anonymized medical data and provide useful information to medical institutions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects anonymized treatment or test data. The analysis unit analyzes the data collected by the collection unit using a generation AI and compares it with a database or pathology data. The provision unit provides the analysis results obtained by the analysis unit to a medical institution.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently anonymize and analyze medical data and provide useful information to medical institutions, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze anonymized medical data and provide useful information to medical institutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects anonymized treatment or test data. The analysis unit analyzes the data collected by the collection unit using a generation AI and compares it with a database or pathology data. The provision unit provides the analysis results obtained by the analysis unit to a medical institution. [Effects of the Invention]

[0007] The system according to the embodiment can analyze anonymized medical data and provide useful information to medical institutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A medical data analysis system according to an embodiment of the present invention is a platform that collects treatment and test data held by each medical institution in a form that removes personal information and then uses a generative AI to compare it with databases and pathology data to improve the probability of a disease. This medical data analysis system collects treatment and test data from each medical institution and collects only anonymized data by removing personal information. For example, personal information such as the patient's name and address is deleted, and only treatment details and test results are collected. The collected data is then input into a generative AI. The generative AI analyzes the collected data and compares it with databases and pathology data. For example, it compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The generative AI's analysis results are provided to medical institutions. Based on the provided analysis results, medical institutions can more accurately assess a patient's condition and provide appropriate treatment. For example, if the generative AI estimates a high probability of a specific disease, the medical institution can prioritize testing and treatment for that disease. This platform enables medical institutions to more accurately assess a patient's condition and provide appropriate treatment promptly. Furthermore, because personal information is removed, patient privacy is also protected. This allows the medical data analysis system to collect and anonymize treatment and examination data held by medical institutions, and analyze it using generative AI to improve the probability of disease.

[0029] A medical data analysis system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects anonymized treatment or test data. For example, the collection unit collects anonymized data from each medical institution, from which personal information has been removed. For example, the collection unit deletes personal information such as the patient's name, address, and phone number, and collects only the treatment details and test results. The collection unit can also scan handwritten data and convert it into digital data. The analysis unit analyzes the collected data using a generation AI and compares it with a database or pathology data. For example, the analysis unit compares treatment data and pathology data related to a specific disease and estimates the probability of the disease. The generation AI analyzes the data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit executes an algorithm for the generation AI to estimate the probability of a specific disease. The provision unit provides the analysis results obtained by the analysis unit to the medical institution. For example, the provision unit provides the analysis results obtained by the generation AI to the medical institution, providing information for the medical institution to provide appropriate treatment. The provision unit can provide the analysis results to the medical institution via email or a web portal, for example. As a result, the medical data analysis system according to the embodiment can improve the probability of a disease by collecting anonymized data and analyzing it using a generating AI.

[0030] The collection unit can collect anonymized data from each medical institution from which personal information has been removed. The anonymized data includes data from which personal information, such as a patient's name, address, and telephone number, has been removed. For example, the collection unit executes a process to remove personal information when collecting the anonymized data from each medical institution. For example, the collection unit removes the patient's name and address and collects only the treatment details and test results. The collection unit can also implement security protocols for collecting the anonymized data. For example, the collection unit can encrypt the data and perform access control to ensure data security. This allows patient privacy to be protected by collecting anonymized data from which personal information has been removed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can automate the removal of personal information using AI to efficiently collect anonymized data.

[0031] The analysis unit can analyze data collected by the generation AI and compare treatment data and pathology data related to a specific disease. The analysis unit, for example, analyzes data collected using the generation AI. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The generation AI analyzes data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit, for example, executes an algorithm for the generation AI to estimate the probability of a specific disease. For example, the generation AI compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The analysis unit, for example, executes an algorithm for the generation AI to estimate the probability of a specific disease. In this way, by analyzing data using the generation AI and comparing treatment data and pathology data related to a specific disease, the probability of the disease can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automate data analysis using AI to efficiently estimate the probability of the disease.

[0032] The providing unit can provide the analysis results by the generating AI to a medical institution and provide information for the medical institution to provide treatment. The providing unit, for example, provides the analysis results by the generating AI to a medical institution. For example, the providing unit can provide the analysis results to a medical institution via email or a web portal. The providing unit, for example, provides the analysis results to a medical institution and provides information for the medical institution to provide appropriate treatment. For example, if the generating AI estimates a high probability of a specific disease, the providing unit provides that information to the medical institution so that the medical institution can prioritize testing and treatment for that disease. In this way, by providing the analysis results by the generating AI, the medical institution can obtain information for providing appropriate treatment. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can automate the provision of analysis results using AI and quickly provide information to medical institutions.

[0033] The collection unit can delete personal information such as the patient's name, address, and telephone number, and collect only the treatment details and test results. The collection unit, for example, deletes personal information such as the patient's name, address, and telephone number. For example, the collection unit deletes the patient's name and address, and collects only the treatment details and test results. The collection unit, for example, executes a process to delete personal information. For example, the collection unit anonymizes data and deletes personal information. In this way, by deleting personal information, it is possible to collect necessary data while protecting the patient's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate the deletion of personal information using AI and efficiently collect anonymized data.

[0034] The analysis unit can estimate the probability of a specific disease using a generative AI. The analysis unit, for example, estimates the probability of a specific disease using a generative AI. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. The generative AI analyzes data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit, for example, executes an algorithm for the generative AI to estimate the probability of a specific disease. For example, the generative AI compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. In this way, estimating the probability of a specific disease using a generative AI can more accurately grasp the condition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automate data analysis using AI to efficiently estimate the probability of the condition.

[0035] The collection unit can analyze the data collection frequency of each medical institution and set a collection schedule based on the collection frequency. The collection unit, for example, analyzes the data collection frequency of each medical institution. For example, the collection unit analyzes the data provision frequency of each medical institution and sets an optimal collection schedule. The collection unit can, for example, analyze the data provision frequency for each medical department of the medical institution and adjust the collection schedule. The collection unit can also set an optimal collection schedule based on the medical institution's past data provision history. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. This enables efficient data collection by analyzing the data collection frequency and setting an optimal collection schedule. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the data collection frequency using AI and automatically set a collection schedule.

[0036] The collection unit can filter data based on a specific medical department or treatment method at a medical institution when collecting data. For example, the collection unit filters data based on a specific medical department or treatment method at a medical institution when collecting data. For example, the collection unit filters data to collect only data related to a specific medical department. The collection unit can also filter data to collect only data related to a specific treatment method. Furthermore, the collection unit can filter data to collect only data related to a specific disease. For example, the collection unit filters data based on a specific medical department or treatment method to efficiently collect only necessary data. In this way, by filtering data based on a specific medical department or treatment method, only necessary data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate data filtering using AI to efficiently collect data.

[0037] The collection unit can prioritize collecting highly relevant data based on the geographical location information of medical institutions when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of medical institutions when collecting data. For example, the collection unit prioritizes collecting data from nearby medical institutions based on the geographical location information of the medical institutions. The collection unit can also prioritize collecting data from medical institutions that are geographically relevant. Furthermore, the collection unit can postpone data from medical institutions that are geographically distant. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of the medical institutions. This enables efficient data collection by prioritizing the collection of highly relevant data based on the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze geographical location information using AI and automatically collect highly relevant data.

[0038] The collection unit can analyze the medical institution's past data provision history when collecting data, thereby improving the efficiency of collection. The collection unit, for example, analyzes the medical institution's past data provision history when collecting data. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. The collection unit can, for example, determine the priority of data collection based on the past data provision history. The collection unit can also optimize the collection schedule by referring to the past data provision history. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. In this way, efficient data collection is possible by analyzing the past data provision history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the past data provision history using AI to automatically improve the efficiency of collection.

[0039] The analysis unit can perform a detailed analysis of the correlation between the treatment data and pathology data for a specific disease during analysis. The analysis unit, for example, performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease during analysis. For example, the analysis unit performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease. The analysis unit can estimate the probability of a disease state, for example, based on the correlation between the treatment data and pathology data. The analysis unit can also prioritize analysis of data with a strong correlation. For example, the analysis unit performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease and estimates the probability of a disease state. This allows for a more accurate estimation of the probability of a disease state by performing a detailed analysis of the correlation between the treatment data and pathology data for a specific disease. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automatically analyze the correlation between data using AI to efficiently estimate the probability of a disease state.

[0040] The analysis unit can apply different analysis algorithms depending on the type and format of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and format of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a time series analysis algorithm to time series data. For example, the analysis unit can apply a natural language processing algorithm to text data, an image analysis algorithm to image data, and a time series analysis algorithm to time series data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the type and format of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can automatically apply different analysis algorithms depending on the type and format of data using AI.

[0041] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit determines the analysis priority based on, for example, the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. Furthermore, the analysis unit can postpone analysis of older data. Furthermore, the analysis unit can set the analysis priority based on the time of data collection. For example, the analysis unit prioritizes analysis of the most recent data and postpones analysis of older data. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the time of data collection using AI and automatically determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the relevance of the data using AI and automatically adjust the order of analysis.

[0043] The providing unit can select the optimal information provision method by referring to the medical institution's past treatment results when providing information. The providing unit, for example, refers to the medical institution's past treatment results when providing information. For example, the providing unit selects the optimal information provision method based on the medical institution's past treatment results. The providing unit can determine the priority of information provision by referring to the past treatment results, for example. The providing unit can also adjust the information provision method based on the past treatment results. For example, the providing unit selects the optimal information provision method based on the medical institution's past treatment results. In this way, the optimal information provision method can be selected by referring to the past treatment results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze past treatment results using AI and automatically select the optimal information provision method.

[0044] The providing unit can customize the information based on a specific medical department or treatment method at the medical institution when providing the information. The providing unit, for example, customizes the information based on a specific medical department or treatment method at the medical institution when providing the information. For example, the providing unit prioritizes providing information related to a specific medical department. The providing unit can also prioritize providing information related to a specific treatment method. Furthermore, the providing unit can prioritize providing information related to a specific disease. For example, the providing unit customizes the information based on a specific medical department or treatment method to provide information useful to the medical institution. In this way, by customizing the information based on a specific medical department or treatment method, it is possible to provide information useful to the medical institution. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can automatically customize the information based on a specific medical department or treatment method using AI.

[0045] The providing unit can select the optimal information provision method based on the geographical location information of the medical institution at the time of providing the information. For example, the providing unit selects the optimal information provision method based on the geographical location information of the medical institution at the time of providing the information. For example, the providing unit prioritizes providing information related to nearby medical institutions based on the geographical location information of the medical institution. The providing unit can also prioritize providing information related to medical institutions that are highly geographically relevant. Furthermore, the providing unit can postpone information related to medical institutions that are geographically distant. For example, the providing unit selects the optimal information provision method based on the geographical location information of the medical institution. In this way, highly relevant information can be provided by selecting the optimal information provision method based on the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can analyze the geographical location information using AI and automatically select the optimal information provision method.

[0046] The providing unit can analyze the social media activity of the medical institution and suggest a means of providing information at the time of providing the information. The providing unit, for example, analyzes the social media activity of the medical institution at the time of providing the information. For example, the providing unit suggests the optimal means of providing information based on the social media activity of the medical institution. The providing unit can determine the priority of information provision based on the social media activity, for example. The providing unit can also adjust the method of providing information with reference to the social media activity. For example, the providing unit analyzes the social media activity of the medical institution and suggests the optimal means of providing information. In this way, the optimal means of providing information can be suggested by analyzing the social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze social media activity using AI and automatically suggest the optimal means of providing information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When collecting data from medical institutions, the collection unit can analyze the data provision frequency for each medical department at each medical institution and optimize the collection schedule. For example, the collection unit analyzes the data provision frequency for each medical department, such as internal medicine, surgery, and dermatology, and adjusts the collection schedule. The collection unit can also set an efficient collection method based on the medical institution's past data provision history. For example, the collection unit analyzes the past data provision history and sets an optimal collection schedule. This enables efficient data collection by analyzing the data collection frequency and setting an optimal collection schedule. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the data collection frequency using AI and automatically set the collection schedule.

[0049] When analyzing data collected by the generation AI, the analysis unit can apply different analysis algorithms depending on the type and format of the data. For example, the analysis unit can apply a natural language processing algorithm to text data and an image analysis algorithm to image data. It can also apply a time series analysis algorithm to time series data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the type and format of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can automatically apply different analysis algorithms depending on the type and format of the data using AI.

[0050] When providing the analysis results by the generation AI to a medical institution, the providing unit can customize the information based on the medical institution's specific medical department or treatment method. For example, the providing unit can prioritize providing information about a specific medical department. Also, the providing unit can prioritize providing information about a specific treatment method. Furthermore, the providing unit can prioritize providing information about a specific disease. In this way, by customizing the information based on a specific medical department or treatment method, it is possible to provide useful information to the medical institution. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can automatically customize the information based on a specific medical department or treatment method using AI.

[0051] When collecting data, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the medical institution. For example, the collection unit can prioritize collection of data from nearby medical institutions based on the geographical location information of the medical institution. Also, data from medical institutions that are geographically related can be prioritized. Furthermore, data from medical institutions that are geographically distant can be postponed. This enables efficient data collection by prioritizing collection of highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze geographical location information using AI and automatically collect highly relevant data.

[0052] During analysis, the analysis unit can perform a detailed analysis of the correlation between treatment data and pathology data for a specific disease. For example, the analysis unit performs a detailed analysis of the correlation between treatment data and pathology data for a specific disease. The analysis unit can estimate the probability of a disease state based on the correlation between the treatment data and pathology data. The analysis unit can also prioritize analysis of data with a strong correlation. This allows for a more accurate estimation of the probability of a disease state by performing a detailed analysis of the correlation between treatment data and pathology data for a specific disease. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can automatically analyze the correlation between data using AI to efficiently estimate the probability of a disease state.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection department collects anonymized treatment or test data. For example, the collection department collects anonymized data from each medical institution, removing personal information. Specifically, the department removes personal information such as the patient's name, address, and phone number, and collects only the treatment details and test results. The collection department can also scan handwritten data and convert it into digital data. Step 2: The analysis unit analyzes the collected data using the generation AI and compares it with a database or pathology data. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. The generation AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI, and executes an algorithm to estimate the probability of a specific disease. Step 3: The providing unit provides the analysis results obtained by the analysis unit to the medical institution. For example, the providing unit provides the analysis results obtained by the generation AI to the medical institution, providing information that the medical institution needs to provide appropriate treatment. The providing unit can provide the analysis results to the medical institution via email or a web portal.

[0055] (Example 2) A medical data analysis system according to an embodiment of the present invention is a platform that collects treatment and test data held by each medical institution in a form that removes personal information and then uses a generative AI to compare it with databases and pathology data to improve the probability of a disease. This medical data analysis system collects treatment and test data from each medical institution and collects only anonymized data by removing personal information. For example, personal information such as the patient's name and address is deleted, and only treatment details and test results are collected. The collected data is then input into a generative AI. The generative AI analyzes the collected data and compares it with databases and pathology data. For example, it compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The generative AI's analysis results are provided to medical institutions. Based on the provided analysis results, medical institutions can more accurately assess a patient's condition and provide appropriate treatment. For example, if the generative AI estimates a high probability of a specific disease, the medical institution can prioritize testing and treatment for that disease. This platform enables medical institutions to more accurately assess a patient's condition and provide appropriate treatment promptly. Furthermore, because personal information is removed, patient privacy is also protected. This allows the medical data analysis system to collect and anonymize treatment and examination data held by medical institutions, and analyze it using generative AI to improve the probability of disease.

[0056] A medical data analysis system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects anonymized treatment or test data. For example, the collection unit collects anonymized data from each medical institution, from which personal information has been removed. For example, the collection unit deletes personal information such as the patient's name, address, and phone number, and collects only the treatment details and test results. The collection unit can also scan handwritten data and convert it into digital data. The analysis unit analyzes the collected data using a generation AI and compares it with a database or pathology data. For example, the analysis unit compares treatment data and pathology data related to a specific disease and estimates the probability of the disease. The generation AI analyzes the data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit executes an algorithm for the generation AI to estimate the probability of a specific disease. The provision unit provides the analysis results obtained by the analysis unit to the medical institution. For example, the provision unit provides the analysis results obtained by the generation AI to the medical institution, providing information for the medical institution to provide appropriate treatment. The provision unit can provide the analysis results to the medical institution via email or a web portal, for example. As a result, the medical data analysis system according to the embodiment can improve the probability of a disease by collecting anonymized data and analyzing it using a generating AI.

[0057] The collection unit can collect anonymized data from each medical institution from which personal information has been removed. The anonymized data includes data from which personal information, such as a patient's name, address, and telephone number, has been removed. For example, the collection unit executes a process to remove personal information when collecting the anonymized data from each medical institution. For example, the collection unit removes the patient's name and address and collects only the treatment details and test results. The collection unit can also implement security protocols for collecting the anonymized data. For example, the collection unit can encrypt the data and perform access control to ensure data security. This allows patient privacy to be protected by collecting anonymized data from which personal information has been removed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can automate the removal of personal information using AI to efficiently collect anonymized data.

[0058] The analysis unit can analyze data collected by the generation AI and compare treatment data and pathology data related to a specific disease. The analysis unit, for example, analyzes data collected using the generation AI. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The generation AI analyzes data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit, for example, executes an algorithm for the generation AI to estimate the probability of a specific disease. For example, the generation AI compares treatment data and pathology data related to a specific disease to estimate the probability of the disease. The analysis unit, for example, executes an algorithm for the generation AI to estimate the probability of a specific disease. In this way, by analyzing data using the generation AI and comparing treatment data and pathology data related to a specific disease, the probability of the disease can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automate data analysis using AI to efficiently estimate the probability of the disease.

[0059] The providing unit can provide the analysis results by the generating AI to a medical institution and provide information for the medical institution to provide treatment. The providing unit, for example, provides the analysis results by the generating AI to a medical institution. For example, the providing unit can provide the analysis results to a medical institution via email or a web portal. The providing unit, for example, provides the analysis results to a medical institution and provides information for the medical institution to provide appropriate treatment. For example, if the generating AI estimates a high probability of a specific disease, the providing unit provides that information to the medical institution so that the medical institution can prioritize testing and treatment for that disease. In this way, by providing the analysis results by the generating AI, the medical institution can obtain information for providing appropriate treatment. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can automate the provision of analysis results using AI and quickly provide information to medical institutions.

[0060] The collection unit can delete personal information such as the patient's name, address, and telephone number, and collect only the treatment details and test results. The collection unit, for example, deletes personal information such as the patient's name, address, and telephone number. For example, the collection unit deletes the patient's name and address, and collects only the treatment details and test results. The collection unit, for example, executes a process to delete personal information. For example, the collection unit anonymizes data and deletes personal information. In this way, by deleting personal information, it is possible to collect necessary data while protecting the patient's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate the deletion of personal information using AI and efficiently collect anonymized data.

[0061] The analysis unit can estimate the probability of a specific disease using a generative AI. The analysis unit, for example, estimates the probability of a specific disease using a generative AI. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. The generative AI analyzes data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit, for example, executes an algorithm for the generative AI to estimate the probability of a specific disease. For example, the generative AI compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. In this way, estimating the probability of a specific disease using a generative AI can more accurately grasp the condition. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automate data analysis using AI to efficiently estimate the probability of the condition.

[0062] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using emotion recognition technology. The collection unit, for example, adjusts the timing of data collection based on the user's emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more data. Furthermore, if the user is in a hurry, the collection unit can adjust the timing of data collection to collect data quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can use AI to estimate a user's emotions and automatically adjust the timing of data collection.

[0063] The collection unit can analyze the data collection frequency of each medical institution and set a collection schedule based on the collection frequency. The collection unit, for example, analyzes the data collection frequency of each medical institution. For example, the collection unit analyzes the data provision frequency of each medical institution and sets an optimal collection schedule. The collection unit can, for example, analyze the data provision frequency for each medical department of the medical institution and adjust the collection schedule. The collection unit can also set an optimal collection schedule based on the medical institution's past data provision history. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. This enables efficient data collection by analyzing the data collection frequency and setting an optimal collection schedule. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the data collection frequency using AI and automatically set a collection schedule.

[0064] The collection unit can filter data based on a specific medical department or treatment method at a medical institution when collecting data. For example, the collection unit filters data based on a specific medical department or treatment method at a medical institution when collecting data. For example, the collection unit filters data to collect only data related to a specific medical department. The collection unit can also filter data to collect only data related to a specific treatment method. Furthermore, the collection unit can filter data to collect only data related to a specific disease. For example, the collection unit filters data based on a specific medical department or treatment method to efficiently collect only necessary data. In this way, by filtering data based on a specific medical department or treatment method, only necessary data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automate data filtering using AI to efficiently collect data.

[0065] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using emotion recognition technology. The collection unit, for example, determines the priority of data to be collected based on the user's emotions. For example, when the user is stressed, the collection unit can prioritize collecting important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can estimate the user's emotions using AI and automatically determine the priority of data.

[0066] The collection unit can prioritize collecting highly relevant data based on the geographical location information of medical institutions when collecting data. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of medical institutions when collecting data. For example, the collection unit prioritizes collecting data from nearby medical institutions based on the geographical location information of the medical institutions. The collection unit can also prioritize collecting data from medical institutions that are geographically relevant. Furthermore, the collection unit can postpone data from medical institutions that are geographically distant. For example, the collection unit prioritizes collecting highly relevant data based on the geographical location information of the medical institutions. This enables efficient data collection by prioritizing the collection of highly relevant data based on the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze geographical location information using AI and automatically collect highly relevant data.

[0067] The collection unit can analyze the medical institution's past data provision history when collecting data, thereby improving the efficiency of collection. The collection unit, for example, analyzes the medical institution's past data provision history when collecting data. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. The collection unit can, for example, determine the priority of data collection based on the past data provision history. The collection unit can also optimize the collection schedule by referring to the past data provision history. For example, the collection unit analyzes the medical institution's past data provision history and sets an efficient collection method. In this way, efficient data collection is possible by analyzing the past data provision history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the past data provision history using AI to automatically improve the efficiency of collection.

[0068] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using emotion recognition technology. The analysis unit, for example, adjusts the presentation method of the analysis based on the user's emotion. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. The analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a concise analysis result when the user is in a hurry. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can use AI to estimate the user's emotions and automatically adjust the way the analysis is presented.

[0069] The analysis unit can perform a detailed analysis of the correlation between the treatment data and pathology data for a specific disease during analysis. The analysis unit, for example, performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease during analysis. For example, the analysis unit performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease. The analysis unit can estimate the probability of a disease state, for example, based on the correlation between the treatment data and pathology data. The analysis unit can also prioritize analysis of data with a strong correlation. For example, the analysis unit performs a detailed analysis of the correlation between the treatment data and pathology data for a specific disease and estimates the probability of a disease state. This allows for a more accurate estimation of the probability of a disease state by performing a detailed analysis of the correlation between the treatment data and pathology data for a specific disease. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can automatically analyze the correlation between data using AI to efficiently estimate the probability of a disease state.

[0070] The analysis unit can apply different analysis algorithms depending on the type and format of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the type and format of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a time series analysis algorithm to time series data. For example, the analysis unit can apply a natural language processing algorithm to text data, an image analysis algorithm to image data, and a time series analysis algorithm to time series data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the type and format of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can automatically apply different analysis algorithms depending on the type and format of data using AI.

[0071] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using emotion recognition technology. The analysis unit, for example, adjusts the display method of the analysis results based on the user's emotion. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotion, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can use AI to estimate the user's emotions and automatically adjust how the analysis results are displayed.

[0072] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit determines the analysis priority based on, for example, the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. Furthermore, the analysis unit can postpone analysis of older data. Furthermore, the analysis unit can set the analysis priority based on the time of data collection. For example, the analysis unit prioritizes analysis of the most recent data and postpones analysis of older data. In this way, by determining the analysis priority based on the time of data collection, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the time of data collection using AI and automatically determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Furthermore, the analysis unit can postpone analysis of less relevant data. Furthermore, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the relevance of the data using AI and automatically adjust the order of analysis.

[0074] The providing unit can estimate the user's emotion and adjust the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit estimates the user's emotion using emotion recognition technology. The providing unit, for example, adjusts the presentation method of the information to be provided based on the user's emotion. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that is concise. This allows the information to be provided in a way that is easy for the user to understand by adjusting the presentation method of the information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can estimate the user's emotion using AI and automatically adjust the presentation method of the information.

[0075] The providing unit can select the optimal information provision method by referring to the medical institution's past treatment results when providing information. The providing unit, for example, refers to the medical institution's past treatment results when providing information. For example, the providing unit selects the optimal information provision method based on the medical institution's past treatment results. The providing unit can determine the priority of information provision by referring to the past treatment results, for example. The providing unit can also adjust the information provision method based on the past treatment results. For example, the providing unit selects the optimal information provision method based on the medical institution's past treatment results. In this way, the optimal information provision method can be selected by referring to the past treatment results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze past treatment results using AI and automatically select the optimal information provision method.

[0076] The providing unit can customize the information based on a specific medical department or treatment method at the medical institution when providing the information. The providing unit, for example, customizes the information based on a specific medical department or treatment method at the medical institution when providing the information. For example, the providing unit prioritizes providing information related to a specific medical department. The providing unit can also prioritize providing information related to a specific treatment method. Furthermore, the providing unit can prioritize providing information related to a specific disease. For example, the providing unit customizes the information based on a specific medical department or treatment method to provide information useful to the medical institution. In this way, by customizing the information based on a specific medical department or treatment method, it is possible to provide information useful to the medical institution. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can automatically customize the information based on a specific medical department or treatment method using AI.

[0077] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit estimates the user's emotions using emotion recognition technology. The providing unit, for example, determines the priority of information to be provided based on the user's emotions. For example, if the user is nervous, the providing unit can prioritize providing information of high importance. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. This allows important information to be prioritized by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can estimate the user's emotions using AI and automatically determine the priority of information.

[0078] The providing unit can select the optimal information provision method based on the geographical location information of the medical institution at the time of providing the information. For example, the providing unit selects the optimal information provision method based on the geographical location information of the medical institution at the time of providing the information. For example, the providing unit prioritizes providing information related to nearby medical institutions based on the geographical location information of the medical institution. The providing unit can also prioritize providing information related to medical institutions that are highly geographically relevant. Furthermore, the providing unit can postpone information related to medical institutions that are geographically distant. For example, the providing unit selects the optimal information provision method based on the geographical location information of the medical institution. In this way, highly relevant information can be provided by selecting the optimal information provision method based on the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can analyze the geographical location information using AI and automatically select the optimal information provision method.

[0079] The providing unit can analyze the social media activity of the medical institution and suggest a means of providing information at the time of providing the information. The providing unit, for example, analyzes the social media activity of the medical institution at the time of providing the information. For example, the providing unit suggests the optimal means of providing information based on the social media activity of the medical institution. The providing unit can determine the priority of information provision based on the social media activity, for example. The providing unit can also adjust the method of providing information with reference to the social media activity. For example, the providing unit analyzes the social media activity of the medical institution and suggests the optimal means of providing information. In this way, the optimal means of providing information can be suggested by analyzing the social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze social media activity using AI and automatically suggest the optimal means of providing information. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects anonymized treatment or examination data from each medical institution. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and compares it with a database or pathology data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the analysis results to the medical institution. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects anonymized treatment or examination data from each medical institution. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and compares it with a database or pathology data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the analysis results to the medical institution. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects anonymized treatment or test data from each medical institution. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and compares it with a database or pathology data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the analysis results to the medical institution. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects anonymized treatment or examination data from each medical institution. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI and compares it with a database or pathology data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the analysis results to the medical institution.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] When collecting data from medical institutions, the collection unit can analyze the data provision frequency for each medical department at each medical institution and optimize the collection schedule. For example, the collection unit analyzes the data provision frequency for each medical department, such as internal medicine, surgery, and dermatology, and adjusts the collection schedule. The collection unit can also set an efficient collection method based on the medical institution's past data provision history. For example, the collection unit analyzes the past data provision history and sets an optimal collection schedule. This enables efficient data collection by analyzing the data collection frequency and setting an optimal collection schedule. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the data collection frequency using AI and automatically set the collection schedule.

[0082] When analyzing data collected by the generation AI, the analysis unit can apply different analysis algorithms depending on the type and format of the data. For example, the analysis unit can apply a natural language processing algorithm to text data and an image analysis algorithm to image data. It can also apply a time series analysis algorithm to time series data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the type and format of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can automatically apply different analysis algorithms depending on the type and format of the data using AI.

[0083] When providing the analysis results by the generation AI to a medical institution, the providing unit can customize the information based on the medical institution's specific medical department or treatment method. For example, the providing unit can prioritize providing information about a specific medical department. Also, the providing unit can prioritize providing information about a specific treatment method. Furthermore, the providing unit can prioritize providing information about a specific disease. In this way, by customizing the information based on a specific medical department or treatment method, it is possible to provide useful information to the medical institution. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can automatically customize the information based on a specific medical department or treatment method using AI.

[0084] When collecting data, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the medical institution. For example, the collection unit can prioritize collection of data from nearby medical institutions based on the geographical location information of the medical institution. Also, data from medical institutions that are geographically related can be prioritized. Furthermore, data from medical institutions that are geographically distant can be postponed. This enables efficient data collection by prioritizing collection of highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze geographical location information using AI and automatically collect highly relevant data.

[0085] During analysis, the analysis unit can perform a detailed analysis of the correlation between treatment data and pathology data for a specific disease. For example, the analysis unit performs a detailed analysis of the correlation between treatment data and pathology data for a specific disease. The analysis unit can estimate the probability of a disease state based on the correlation between the treatment data and pathology data. The analysis unit can also prioritize analysis of data with a strong correlation. This allows for a more accurate estimation of the probability of a disease state by performing a detailed analysis of the correlation between treatment data and pathology data for a specific disease. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can automatically analyze the correlation between data using AI to efficiently estimate the probability of a disease state.

[0086] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions using emotion recognition technology. The collection unit adjusts the timing of data collection based on the user's emotions. For example, if the user is stressed, the frequency of data collection can be reduced to reduce the user's burden. Alternatively, if the user is relaxed, the frequency of data collection can be increased to collect more data. Furthermore, if the user is in a hurry, the timing of data collection can be adjusted to collect data quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate the user's emotions using AI and automatically adjust the timing of data collection.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit estimates the user's emotions using emotion recognition technology. The analysis unit adjusts the presentation method of the analysis based on the user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions using AI and automatically adjust the presentation method of the analysis.

[0088] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit estimates the user's emotions using emotion recognition technology. The providing unit adjusts the presentation method of the information to be provided based on the user's emotions. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that is concise can be provided. This allows the information to be provided in a way that is easy for the user to understand by adjusting the presentation method of the information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can estimate the user's emotions using AI and automatically adjust the presentation method of the information.

[0089] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions using emotion recognition technology. The collection unit prioritizes the data to be collected based on the user's emotions. For example, if the user is stressed, important data can be collected first. Also, if the user is relaxed, detailed data can be collected first. Furthermore, if the user is in a hurry, data that can be collected quickly can be collected first. This allows important data to be collected first by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without AI. For example, the collection unit can estimate the user's emotions using AI and automatically prioritize data.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using emotion recognition technology. The analysis unit adjusts the display method of the analysis results based on the user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the user's emotions using AI and automatically adjust the display method of the analysis results.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The collection department collects anonymized treatment or test data. For example, the collection department collects anonymized data from each medical institution, removing personal information. Specifically, the department removes personal information such as the patient's name, address, and phone number, and collects only the treatment details and test results. The collection department can also scan handwritten data and convert it into digital data. Step 2: The analysis unit analyzes the collected data using the generation AI and compares it with a database or pathology data. For example, the analysis unit compares treatment data and pathology data related to a specific disease to estimate the probability of the condition. The generation AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI, and executes an algorithm to estimate the probability of a specific disease. Step 3: The providing unit provides the analysis results obtained by the analysis unit to the medical institution. For example, the providing unit provides the analysis results obtained by the generation AI to the medical institution, providing information that the medical institution needs to provide appropriate treatment. The providing unit can provide the analysis results to the medical institution via email or a web portal.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects de-identified treatment or testing data; an analysis unit that analyzes the data collected by the collection unit using a generation AI and compares it with a database or pathology data; a providing unit that provides the analysis results obtained by the analysis unit to a medical institution. A system characterized by:

2. The collecting unit Collect anonymized data from each medical institution, removing personal information. The system of claim 1 .

3. The analysis unit Analyze the data collected by generative AI and compare treatment data and pathology data for specific diseases. The system of claim 1 .

4. The providing unit The analysis results of the generated AI are provided to medical institutions, providing them with information for treatment. The system of claim 1 .

5. The collecting unit Remove personal information such as patient names, addresses, and phone numbers, and collect only treatment details and test results. The system of claim 1 .

6. The analysis unit Generative AI estimates the probability of certain diseases The system of claim 1 .

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

8. The collecting unit Analyze the data collection frequency of each medical institution and set a collection schedule based on the frequency. The system of claim 1 .

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