System

Abstract

An object of a system according to an embodiment is to unify medical record data in different formats for respective hospitals and improve the interoperability of medical information.SOLUTION: A system includes a data analysis unit, a conversion unit, and a display unit. The data-analyzing part converts the medical chart in a format different for each hospital into a unified format by using the generation AI4. The conversion part converts the medical chart data analyzed by the data analysis part into a unified format. The display unit displays the unified format data converted by the conversion unit according to the preference of the doctor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to standardize medical record data, which had different formats from hospital to hospital, and there were issues with the interoperability of medical information.

[0005] The system according to the embodiment aims to unify medical record data in different formats from hospital to hospital and improve the interoperability of medical information. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a conversion unit, and a display unit. The data analysis unit uses a generation AI4 to convert medical record data, which varies from hospital to hospital, into a unified format. The conversion unit converts the medical record data analyzed by the data analysis unit into the unified format. The display unit displays the unified format data converted by the conversion unit according to the doctor's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can unify medical record data in different formats from hospital to hospital, thereby improving the interoperability of medical information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The data unification system according to an embodiment of the present invention uses generation AI 4 to convert medical record data in different formats from hospital to hospital into a unified format, allowing doctors to view the data in their preferred format. By converting medical record data in different formats into a unified format and allowing doctors to view the data in their preferred format, the data unification system can enhance data interoperability between medical systems and improve the efficiency of medical information utilization.

[0029] The data unification system according to the embodiment includes a data analysis unit, a conversion unit, and a display unit. The data analysis unit uses a generation AI4 to analyze medical record data in different formats for each hospital. For example, the data analysis unit analyzes medical record data in PDF format. The data analysis unit can also analyze medical record data in Excel format. The data analysis unit can also analyze medical record data in text format. For example, the data analysis unit converts PDF format medical record data into text data and analyzes its content. For Excel format medical record data, the data analysis unit analyzes cell content and extracts necessary information. For text format medical record data, the data analysis unit uses natural language processing technology. The conversion unit converts the medical record data analyzed by the data analysis unit into a unified format. For example, the conversion unit converts PDF format medical record data into XML format. The conversion unit can also convert Excel format medical record data into XML format. The conversion unit can also convert text format medical record data into XML format. For example, when converting PDF format medical record data into XML format, the conversion unit tags the content while preserving the document structure. For Excel format medical record data, the cell content is converted into XML tags. The text-format medical record data is converted into XML format by analyzing the document structure using natural language processing technology. The display unit displays the unified format data converted by the conversion unit according to the doctor's preferences. For example, the display unit performs a text-based display. The display unit can also perform a graphical display. The display unit can also provide a customizable display format. For example, if the doctor prefers a text-based display, the display unit displays the unified format data in text format. If the doctor prefers a graphical display, the display unit displays the unified format data graphically. By providing a customizable display format, doctors can view the medical record data in a format that suits their preferences. As a result, the data unification system according to the embodiment converts medical record data in different formats into a unified format and allows doctors to view the data in a format of their choice, thereby enhancing data interoperability between medical systems and improving the efficiency of medical information utilization.

[0030] The data analysis unit can analyze the contents of the medical record data and standardize medical terminology. The data analysis unit, for example, uses the generation AI4 to analyze the contents of the medical record data and standardize the medical terminology used at each hospital. For example, different terms that describe the same medical condition are unified to maintain data consistency. When standardizing medical terminology, the data analysis unit uses the generation AI4 to analyze the medical record data of each hospital and automatically generate terminology conversion rules. For example, abbreviations used at a specific hospital are converted into standard terms. The data analysis unit also facilitates data exchange between different hospitals by using the generation AI4 to analyze the medical record data and standardize medical terminology. For example, when converting medical record data from different hospitals into a unified format, inconsistencies in terminology are resolved. This makes it possible to resolve inconsistencies in terminology between different hospitals.

[0031] The data analysis unit can evaluate the reliability of medical record data and automatically filter out unreliable data. For example, the data analysis unit introduces an algorithm in which the generation AI4 analyzes medical record data and evaluates the reliability of the data. For example, it calculates a reliability score based on the consistency and accuracy of the data. The data analysis unit also adds a function to automatically filter out unreliable data, and the generation AI4 excludes unreliable data during data conversion. For example, it detects and excludes typographical errors and incomplete data. The data analysis unit also improves the quality of the converted data by having the generation AI4 evaluate the reliability of the medical record data and filter out unreliable data. For example, excluding unreliable data increases the accuracy of the unified format data. This can improve the quality of the data.

[0032] The conversion unit can convert not only medical record data but also medical image data into a unified format and manage them in an integrated manner. The conversion unit, for example, uses generation AI4 to convert medical record data and medical image data into a unified format and build a system for integrated management. For example, it converts X-ray images and MRI images into a standard format. The conversion unit also analyzes medical image data and develops an algorithm that generation AI4 uses to convert it into a unified format. For example, it converts image data of different formats into a unified format. The conversion unit also maintains data consistency by having generation AI4 convert medical record data and medical image data into a unified format and manage them in an integrated manner, for example, by integrating medical record data and image data into a single database. This allows data consistency to be maintained.

[0033] The conversion unit can automatically translate medical record data written in different languages, thereby achieving international standardization of medical data. The conversion unit, for example, uses the generation AI4 to build a system that automatically translates medical record data written in different languages. For example, it translates medical record data into English, French, Chinese, etc. The conversion unit also introduces an automatic translation function, and the generation AI4 converts medical record data in different languages ​​into a unified format. For example, it converts the translated data into a standard format. The conversion unit also automatically translates medical record data in different languages ​​using the generation AI4, thereby achieving international standardization of medical data. For example, it converts the translated data into a unified format and stores it in an international database. This can improve the efficiency of data utilization.

[0034] The display unit can learn the doctor's past browsing history and medical practice style and automatically suggest the optimal display format. For example, the display unit constructs a system in which the generation AI4 analyzes the doctor's past browsing history and automatically suggests the optimal display format. For example, it prioritizes displaying frequently viewed information. The display unit also learns the doctor's medical practice style and the generation AI4 develops an algorithm that suggests the optimal display format. For example, it provides a customized display according to the medical department or specialty. The display unit also learns the doctor's past browsing history and medical practice style and automatically suggests the optimal display format. For example, it provides a display format that suits the doctor's preferences. This improves convenience for doctors.

[0035] The display unit can customize the display according to the doctor's medical department or specialty and emphasize specialized information. The display unit, for example, constructs a system in which the generation AI4 analyzes the doctor's medical department or specialty and customizes the display. For example, specialized information is emphasized. Furthermore, in order to customize the display according to the doctor's specialty, the generation AI4 analyzes the data and adjusts the display format. For example, technical terms and related information are emphasized. Furthermore, the display unit, for example, customizes the display according to the doctor's medical department or specialty and emphasizes specialized information. For example, data related to the specialty is displayed preferentially. This improves convenience for doctors.

[0036] The display unit can provide a display format optimized for the device used by the doctor. For example, the generation AI4 analyzes the device used by the doctor and builds a system that provides an optimized display format. For example, it proposes a layout suitable for tablets and smartphones. In addition, the generation AI4 analyzes data and adjusts the display format to provide a display format optimized for the device. For example, it displays according to the screen size and resolution. In addition, the generation AI4 provides a display format optimized for the device used by the doctor. For example, it proposes an interface suitable for touch operation. This can improve convenience for doctors.

[0037] The display unit can provide an interactive display format that allows doctors to share information with other medical staff in real time. For example, the display unit constructs a system in which the generation AI4 provides an interactive display format that allows doctors and other medical staff to share information in real time. For example, a collaborative editing function is added. In addition, in order to provide an interactive display format, the display unit allows the generation AI4 to analyze data and adjust the display format. For example, real-time information update and comment functions are added. In addition, the display unit provides an interactive display format in which the generation AI4 allows doctors and other medical staff to share information in real time. For example, a chat function or a video call function is integrated. This can improve convenience for doctors.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The data analysis unit can analyze the contents of the medical record data and extract information about the patient's lifestyle and environment. For example, using generation AI4, it can extract information such as the patient's eating habits, exercise habits, and living environment from the medical record data. The data analysis unit can also analyze the patient's lifestyle and environmental information to evaluate health risks. For example, it can evaluate unhealthy eating habits or a continued lack of exercise as factors that increase health risks. The data analysis unit can also provide individualized health advice based on the patient's lifestyle and environmental information. For example, it can suggest dietary improvements or exercise plans. This can support the patient's health management.

[0040] The data analysis unit can analyze the contents of the medical record data, learn the doctor's treatment patterns, and improve the efficiency of medical treatment. For example, it can use generative AI4 to extract the doctor's treatment patterns from the medical record data and optimize the flow of treatment. The data analysis unit can also make suggestions to improve the efficiency of medical treatment based on the doctor's treatment patterns. For example, it can propose shortening treatment times and improving the medical process. The data analysis unit can also continuously monitor the doctor's treatment patterns and provide feedback to improve the efficiency of medical treatment. For example, it can track changes in treatment patterns and provide advice on efficiency. This can improve the efficiency of the doctor's treatment.

[0041] When converting medical record data, the conversion unit can propose a data display format that takes into account the doctor's medical practice style. For example, the generation AI4 analyzes the medical record data and introduces an algorithm that learns the doctor's medical practice style. For example, it analyzes the medical practice style based on past medical history. The conversion unit also proposes a data display format that takes into account the doctor's medical practice style. For example, it provides a display format that suits a specific medical department or specialty. The conversion unit also learns the doctor's medical practice style through the generation AI4 and proposes a data display format that suits the medical practice style. For example, it provides a customized display that suits the medical practice style. This can improve convenience for doctors.

[0042] The display unit can learn the doctor's past browsing history and medical practice style, and automatically suggest the optimal display format. For example, a system can be constructed in which the generation AI4 analyzes the doctor's past browsing history and automatically suggests the optimal display format. For example, frequently viewed information can be displayed preferentially. The display unit can also learn the doctor's medical practice style, and the generation AI4 can develop an algorithm to suggest the optimal display format. For example, a customized display can be provided according to the medical department or specialty. The display unit can also learn the doctor's past browsing history and medical practice style, and automatically suggest the optimal display format. For example, a display format can be provided that suits the doctor's preferences. This can improve convenience for doctors.

[0043] The display unit can customize the display according to the doctor's medical department or specialty and emphasize specialized information. For example, the generation AI4 analyzes the doctor's medical department or specialty and builds a system that customizes the display. For example, specialized information is highlighted. Furthermore, in order to customize the display according to the doctor's specialty, the generation AI4 analyzes the data and adjusts the display format. For example, technical terms and related information are highlighted. Furthermore, the display unit customizes the display according to the doctor's medical department or specialty, and emphasizes specialized information. For example, data related to the specialty is displayed preferentially. This improves convenience for doctors.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The data analysis unit uses the generation AI 4 to analyze medical record data in different formats for each hospital. For example, it analyzes medical record data in PDF, Excel, and text formats, and extracts the necessary information using a method appropriate for each format. PDF format medical record data is converted to text data, Excel format medical record data is analyzed for cell content, and text format medical record data is analyzed using natural language processing technology. Step 2: The conversion unit converts the medical record data analyzed by the data analysis unit into a unified format. For example, medical record data in PDF, Excel, or text format can be converted into XML format, and the content is tagged while preserving the document structure in a manner appropriate for each format. PDF format medical record data is converted into XML format while preserving the document structure, Excel format medical record data has its cell contents converted into XML tags, and text format medical record data is converted into XML format using natural language processing technology. Step 3: The display unit displays the unified format data converted by the conversion unit according to the doctor's preference. For example, a text-based display, a graphical display, or a customizable display format is provided. If the doctor prefers a text-based display, the display is in text format, and if the doctor prefers a graphical display, the display is in graphical format. By providing a customizable display format, the doctor can view the medical record data in a format that suits his or her preference.

[0046] (Example 2) The data unification system according to an embodiment of the present invention uses generation AI 4 to convert medical record data in different formats from hospital to hospital into a unified format, allowing doctors to view the data in their preferred format. By converting medical record data in different formats into a unified format and allowing doctors to view the data in their preferred format, the data unification system can enhance data interoperability between medical systems and improve the efficiency of medical information utilization.

[0047] The data unification system according to the embodiment includes a data analysis unit, a conversion unit, and a display unit. The data analysis unit uses a generation AI4 to analyze medical record data in different formats for each hospital. For example, the data analysis unit analyzes medical record data in PDF format. The data analysis unit can also analyze medical record data in Excel format. The data analysis unit can also analyze medical record data in text format. For example, the data analysis unit converts PDF format medical record data into text data and analyzes its content. For Excel format medical record data, the data analysis unit analyzes cell content and extracts necessary information. For text format medical record data, the data analysis unit uses natural language processing technology. The conversion unit converts the medical record data analyzed by the data analysis unit into a unified format. For example, the conversion unit converts PDF format medical record data into XML format. The conversion unit can also convert Excel format medical record data into XML format. The conversion unit can also convert text format medical record data into XML format. For example, when converting PDF format medical record data into XML format, the conversion unit tags the content while preserving the document structure. For Excel format medical record data, the cell content is converted into XML tags. The text-format medical record data is converted into XML format by analyzing the document structure using natural language processing technology. The display unit displays the unified format data converted by the conversion unit according to the doctor's preferences. For example, the display unit performs a text-based display. The display unit can also perform a graphical display. The display unit can also provide a customizable display format. For example, if the doctor prefers a text-based display, the display unit displays the unified format data in text format. If the doctor prefers a graphical display, the display unit displays the unified format data graphically. By providing a customizable display format, doctors can view the medical record data in a format that suits their preferences. As a result, the data unification system according to the embodiment converts medical record data in different formats into a unified format and allows doctors to view the data in a format of their choice, thereby enhancing data interoperability between medical systems and improving the efficiency of medical information utilization.

[0048] The data analysis unit can analyze the contents of the medical record data and standardize medical terminology. The data analysis unit, for example, uses the generation AI4 to analyze the contents of the medical record data and standardize the medical terminology used at each hospital. For example, different terms that describe the same medical condition are unified to maintain data consistency. When standardizing medical terminology, the data analysis unit uses the generation AI4 to analyze the medical record data of each hospital and automatically generate terminology conversion rules. For example, abbreviations used at a specific hospital are converted into standard terms. The data analysis unit also facilitates data exchange between different hospitals by using the generation AI4 to analyze the medical record data and standardize medical terminology. For example, when converting medical record data from different hospitals into a unified format, inconsistencies in terminology are resolved. This makes it possible to resolve inconsistencies in terminology between different hospitals.

[0049] The data analysis unit can evaluate the reliability of medical record data and automatically filter out unreliable data. For example, the data analysis unit introduces an algorithm in which the generation AI4 analyzes medical record data and evaluates the reliability of the data. For example, it calculates a reliability score based on the consistency and accuracy of the data. The data analysis unit also adds a function to automatically filter out unreliable data, and the generation AI4 excludes unreliable data during data conversion. For example, it detects and excludes typographical errors and incomplete data. The data analysis unit also improves the quality of the converted data by having the generation AI4 evaluate the reliability of the medical record data and filter out unreliable data. For example, excluding unreliable data increases the accuracy of the unified format data. This can improve the quality of the data.

[0050] The data analysis unit can analyze the patient's emotions and the doctor's comments contained in the medical record data and classify the data based on emotions. For example, the data analysis unit uses an algorithm in which the generation AI4 analyzes the medical record data and estimates the patient's emotions and the doctor's comments. For example, it calculates an emotion score using text analysis. The data analysis unit also uses an emotion estimation function to classify the data based on the emotional information contained in the medical record data. For example, it classifies data with positive emotions and data with negative emotions. The data analysis unit also uses the emotion estimation function in which the generation AI4 analyzes the emotional information in the medical record data and classifies the data based on emotions. For example, it proposes a data display format according to the patient's emotional state. This can improve the efficiency of data utilization.

[0051] The conversion unit can convert not only medical record data but also medical image data into a unified format and manage them in an integrated manner. The conversion unit, for example, uses generation AI4 to convert medical record data and medical image data into a unified format and build a system for integrated management. For example, it converts X-ray images and MRI images into a standard format. The conversion unit also analyzes medical image data and develops an algorithm that generation AI4 uses to convert it into a unified format. For example, it converts image data of different formats into a unified format. The conversion unit also maintains data consistency by having generation AI4 convert medical record data and medical image data into a unified format and manage them in an integrated manner, for example, by integrating medical record data and image data into a single database. This allows data consistency to be maintained.

[0052] The conversion unit can automatically translate medical record data written in different languages, thereby achieving international standardization of medical data. The conversion unit, for example, uses the generation AI4 to build a system that automatically translates medical record data written in different languages. For example, it translates medical record data into English, French, Chinese, etc. The conversion unit also introduces an automatic translation function, and the generation AI4 converts medical record data in different languages ​​into a unified format. For example, it converts the translated data into a standard format. The conversion unit also automatically translates medical record data in different languages ​​using the generation AI4, thereby achieving international standardization of medical data. For example, it converts the translated data into a unified format and stores it in an international database. This can improve the efficiency of data utilization.

[0053] The conversion unit can propose a display format for the data that takes into account the patient's emotional state when converting medical record data. For example, the generation AI4 analyzes the medical record data and introduces an algorithm that estimates the patient's emotional state. For example, an emotion score is calculated using text analysis. The conversion unit also uses an emotion estimation function to propose a display format that takes into account the patient's emotional state when converting medical record data. For example, data with positive emotions is highlighted. The conversion unit also uses the emotion estimation function to analyze the emotional information of the medical record data and proposes a display format for the data according to the emotional state. For example, a customized display is performed according to the patient's emotional state. This can improve the efficiency of data utilization.

[0054] The display unit can learn the doctor's past browsing history and medical practice style and automatically suggest the optimal display format. For example, the display unit constructs a system in which the generation AI4 analyzes the doctor's past browsing history and automatically suggests the optimal display format. For example, it prioritizes displaying frequently viewed information. The display unit also learns the doctor's medical practice style and the generation AI4 develops an algorithm that suggests the optimal display format. For example, it provides a customized display according to the medical department or specialty. The display unit also learns the doctor's past browsing history and medical practice style and automatically suggests the optimal display format. For example, it provides a display format that suits the doctor's preferences. This improves convenience for doctors.

[0055] The display unit can customize the display according to the doctor's medical department or specialty and emphasize specialized information. The display unit, for example, constructs a system in which the generation AI4 analyzes the doctor's medical department or specialty and customizes the display. For example, specialized information is emphasized. Furthermore, in order to customize the display according to the doctor's specialty, the generation AI4 analyzes the data and adjusts the display format. For example, technical terms and related information are emphasized. Furthermore, the display unit, for example, customizes the display according to the doctor's medical department or specialty and emphasizes specialized information. For example, data related to the specialty is displayed preferentially. This improves convenience for doctors.

[0056] The display unit can provide a display format optimized for the device used by the doctor. For example, the generation AI4 analyzes the device used by the doctor and builds a system that provides an optimized display format. For example, it proposes a layout suitable for tablets and smartphones. In addition, the generation AI4 analyzes data and adjusts the display format to provide a display format optimized for the device. For example, it displays according to the screen size and resolution. In addition, the generation AI4 provides a display format optimized for the device used by the doctor. For example, it proposes an interface suitable for touch operation. This can improve convenience for doctors.

[0057] The display unit can provide an interactive display format that allows doctors to share information with other medical staff in real time. For example, the display unit constructs a system in which the generation AI4 provides an interactive display format that allows doctors and other medical staff to share information in real time. For example, a collaborative editing function is added. In addition, in order to provide an interactive display format, the display unit allows the generation AI4 to analyze data and adjust the display format. For example, real-time information update and comment functions are added. In addition, the display unit provides an interactive display format in which the generation AI4 allows doctors and other medical staff to share information in real time. For example, a chat function or a video call function is integrated. This can improve convenience for doctors.

[0058] The display unit can analyze the emotional state of the doctor and propose a display format for smooth communication with the patient. For example, the display unit constructs a system in which the generation AI4 analyzes the emotional state of the doctor and proposes a display format for smooth communication with the patient. For example, the display content is adjusted based on the emotion score. The display unit also uses an emotion estimation function to propose a display format for smooth communication with the patient. For example, a display format that elicits positive emotions is provided. The display unit also analyzes the emotional state of the doctor and proposes a display format for smooth communication with the patient. For example, a customized display is provided according to the emotional state. This improves convenience for the doctor.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The data analysis unit can analyze the contents of the medical record data and extract information about the patient's lifestyle and environment. For example, using generation AI4, it can extract information such as the patient's eating habits, exercise habits, and living environment from the medical record data. The data analysis unit can also analyze the patient's lifestyle and environmental information to evaluate health risks. For example, it can evaluate unhealthy eating habits or a continued lack of exercise as factors that increase health risks. The data analysis unit can also provide individualized health advice based on the patient's lifestyle and environmental information. For example, it can suggest dietary improvements or exercise plans. This can support the patient's health management.

[0061] The data analysis unit can analyze the contents of the medical record data, estimate the patient's emotional state, and propose a treatment plan based on the emotions. For example, using generative AI4, the data analysis unit can estimate the patient's emotional state from the medical record data and evaluate their stress and anxiety levels. The data analysis unit also uses the emotion estimation function to propose a treatment plan based on the patient's emotional state. For example, if stress is high, it can suggest relaxation techniques, and if anxiety is high, it can recommend counseling. The data analysis unit can also continuously monitor the patient's emotional state and evaluate the effectiveness of the treatment plan. For example, it can track changes in the patient's emotional state and adjust the treatment plan. This can support the patient's mental health.

[0062] The data analysis unit can analyze the contents of the medical record data, learn the doctor's treatment patterns, and improve the efficiency of medical treatment. For example, it can use generative AI4 to extract the doctor's treatment patterns from the medical record data and optimize the flow of treatment. The data analysis unit can also make suggestions to improve the efficiency of medical treatment based on the doctor's treatment patterns. For example, it can propose shortening treatment times and improving the medical process. The data analysis unit can also continuously monitor the doctor's treatment patterns and provide feedback to improve the efficiency of medical treatment. For example, it can track changes in treatment patterns and provide advice on efficiency. This can improve the efficiency of the doctor's treatment.

[0063] The data analysis unit can analyze the contents of the medical record data, estimate the patient's emotional state, and support the selection of medications based on emotions. For example, an algorithm is introduced that uses generative AI4 to estimate the patient's emotional state from the medical record data and support the selection of medications based on emotions. The data analysis unit also uses an emotion estimation function to support the selection of medications based on the patient's emotional state. For example, it can recommend an anti-anxiety medication if the patient is highly stressed, and an antidepressant medication if the patient is severely depressed. The data analysis unit can also continuously monitor the patient's emotional state and evaluate the effectiveness of medications. For example, it can track changes in the patient's emotional state and adjust medications. This can improve the effectiveness of treatment for patients.

[0064] When converting medical record data, the conversion unit can propose a display format for the data that takes into account the patient's emotional state. For example, the generation AI4 analyzes the medical record data and introduces an algorithm that estimates the patient's emotional state. For example, an emotion score is calculated using text analysis. The conversion unit also uses an emotion estimation function to propose a display format that takes into account the patient's emotional state when converting the medical record data. For example, data with positive emotions is highlighted. The conversion unit also uses the emotion estimation function to analyze the emotional information in the medical record data and proposes a display format for the data according to the emotional state. For example, a customized display is performed according to the patient's emotional state. This can improve the efficiency of data utilization.

[0065] When converting medical record data, the conversion unit can propose a data display format that takes into account the doctor's medical practice style. For example, the generation AI4 analyzes the medical record data and introduces an algorithm that learns the doctor's medical practice style. For example, it analyzes the medical practice style based on past medical history. The conversion unit also proposes a data display format that takes into account the doctor's medical practice style. For example, it provides a display format that suits a specific medical department or specialty. The conversion unit also learns the doctor's medical practice style through the generation AI4 and proposes a data display format that suits the medical practice style. For example, it provides a customized display that suits the medical practice style. This can improve convenience for doctors.

[0066] When converting medical record data, the conversion unit can propose a display format for the data that takes into account the patient's emotional state. For example, the generation AI4 analyzes the medical record data and introduces an algorithm that estimates the patient's emotional state. For example, an emotion score is calculated using text analysis. The conversion unit also uses an emotion estimation function to propose a display format that takes into account the patient's emotional state when converting the medical record data. For example, data with positive emotions is highlighted. The conversion unit also uses the emotion estimation function to analyze the emotional information in the medical record data and proposes a display format for the data according to the emotional state. For example, a customized display is performed according to the patient's emotional state. This can improve the efficiency of data utilization.

[0067] The display unit can learn the doctor's past browsing history and medical practice style, and automatically suggest the optimal display format. For example, a system can be constructed in which the generation AI4 analyzes the doctor's past browsing history and automatically suggests the optimal display format. For example, frequently viewed information can be displayed preferentially. The display unit can also learn the doctor's medical practice style, and the generation AI4 can develop an algorithm to suggest the optimal display format. For example, a customized display can be provided according to the medical department or specialty. The display unit can also learn the doctor's past browsing history and medical practice style, and automatically suggest the optimal display format. For example, a display format can be provided that suits the doctor's preferences. This can improve convenience for doctors.

[0068] The display unit can customize the display according to the doctor's medical department or specialty and emphasize specialized information. For example, the generation AI4 analyzes the doctor's medical department or specialty and builds a system that customizes the display. For example, specialized information is highlighted. Furthermore, in order to customize the display according to the doctor's specialty, the generation AI4 analyzes the data and adjusts the display format. For example, technical terms and related information are highlighted. Furthermore, the display unit customizes the display according to the doctor's medical department or specialty, and emphasizes specialized information. For example, data related to the specialty is displayed preferentially. This improves convenience for doctors.

[0069] The display unit can analyze the emotional state of the doctor and propose a display format for smooth communication with the patient. For example, a system is constructed in which the generation AI4 analyzes the emotional state of the doctor and proposes a display format for smooth communication with the patient. For example, the display content is adjusted based on the emotion score. The display unit also uses an emotion estimation function to propose a display format for smooth communication with the patient. For example, a display format that elicits positive emotions is provided. The display unit also analyzes the emotional state of the doctor and proposes a display format for smooth communication with the patient. For example, a customized display is provided according to the emotional state. This improves convenience for the doctor.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The data analysis unit uses the generation AI 4 to analyze medical record data in different formats for each hospital. For example, it analyzes medical record data in PDF, Excel, and text formats, and extracts the necessary information using a method appropriate for each format. PDF format medical record data is converted to text data, Excel format medical record data is analyzed for cell content, and text format medical record data is analyzed using natural language processing technology. Step 2: The conversion unit converts the medical record data analyzed by the data analysis unit into a unified format. For example, medical record data in PDF, Excel, or text format can be converted into XML format, and the content is tagged while preserving the document structure in a manner appropriate for each format. PDF format medical record data is converted into XML format while preserving the document structure, Excel format medical record data has its cell contents converted into XML tags, and text format medical record data is converted into XML format using natural language processing technology. Step 3: The display unit displays the unified format data converted by the conversion unit according to the doctor's preference. For example, a text-based display, a graphical display, or a customizable display format is provided. If the doctor prefers a text-based display, the display is in text format, and if the doctor prefers a graphical display, the display is in graphical format. By providing a customizable display format, the doctor can view the medical record data in a format that suits his or her preference.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 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 data analysis unit uses generation AI4 to convert medical record data, which varies from hospital to hospital, into a unified format. a conversion unit that converts the medical record data analyzed by the data analysis unit into a unified format; a display unit that displays the unified format data converted by the conversion unit according to the doctor's preference. A system characterized by:

2. The data analysis unit Evaluating the reliability of the medical record data and automatically filtering out unreliable data 2. The system of claim 1.

3. The conversion unit Not only the medical record data but also medical image data will be converted into a unified format and managed in an integrated manner.

2. The system of claim 1.

4. The display unit The system learns the doctor's browsing history and medical practice style and automatically suggests the optimal display format.

2. The system of claim 1.

5. The data analysis unit Analyze the emotions of patients and doctors' comments contained in the medical record data and classify the data based on emotions.

2. The system of claim 1.

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