System

Abstract

An object of a system according to an embodiment is to analyze medical expense details and propose savings and improvements in medical expenses.SOLUTION: A system includes a medical expense detail analysis part, a visualization part, a transition analysis part, and a proposal part. The medical expense specification analysis part analyzes medical expense specifications. The visualizing unit visualizes the data of the medical expense details analyzed by the medical expense detail analyzing unit in a graph or a diagram. The transition analysis part analyzes the past medical expense data and displays the monthly transition of medical expenses. A proposal part analyzes the increase / decrease factors of medical expenses, and proposes improvement points or savable items.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] Conventional technology does not adequately analyze or visualize medical expense statements, making it difficult to propose ways to save on medical expenses or improve them.

[0005] The system according to the embodiment aims to analyze medical expense statements and propose ways to save on medical expenses and areas for improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a medical expense statement analysis unit, a visualization unit, a trend analysis unit, and a proposal unit. The medical expense statement analysis unit analyzes medical expense statements. The visualization unit visualizes the medical expense statement data analyzed by the medical expense statement analysis unit in the form of graphs or diagrams. The trend analysis unit analyzes past medical expense data and displays monthly trends in medical expenses. The proposal unit analyzes factors behind increases or decreases in medical expenses and proposes areas for improvement and items where savings can be made. [Effects of the Invention]

[0007] The system according to the embodiment can analyze medical expense details and suggest ways to save on medical expenses and areas for improvement. [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 medical expense visualization system according to an embodiment of the present invention automatically analyzes medical expense statements and uses AI to thoroughly visualize monthly medical expenses. This allows patients to wisely save on their medical expenses.

[0029] The medical expense visualization system according to the embodiment includes a medical expense statement analysis unit, a visualization unit, a trend analysis unit, and a proposal unit. The medical expense statement analysis unit analyzes the medical expense statement. For example, the medical expense statement analysis unit scans the medical expense statement, and the generation AI automatically analyzes each item. The medical expense statement analysis unit also automatically classifies the medical expenses, drug expenses, and testing expenses listed on the medical expense statement, and explains how each expense is composed. The generation AI receives the scanned data of the medical expense statement as input and performs analysis based on that data. The visualization unit visualizes the medical expense statement data analyzed by the medical expense statement analysis unit using graphs and diagrams. For example, the visualization unit displays the overall medical expense composition using pie charts and bar graphs based on the data analyzed by the generation AI. This allows patients to intuitively understand the breakdown of their medical expenses. The trend analysis unit analyzes past medical expense data and displays monthly trends in medical expenses. For example, the trend analysis unit allows the generation AI to display a line graph showing the increase or decrease in medical expenses over the past year, allowing users to visually check which months saw increases in medical expenses. The generation AI receives past medical expense statement data as input and performs analysis based on that data. The proposal unit analyzes the factors behind increases or decreases in medical expenses and proposes areas for improvement or savings. For example, the proposal unit allows the generation AI to analyze the cause of an increase in medical expenses in a specific month and propose specific methods for eliminating that cause. The generation AI receives medical expense statement data and the analysis results as input and makes proposals based on that data. This allows the medical expense visualization system according to the embodiment to help patients save on their medical expenses wisely. For example, understanding the breakdown of medical expenses can help reduce unnecessary expenses. Furthermore, analyzing the factors behind increases or decreases in medical expenses can identify specific areas for improvement that will lead to future savings in medical expenses.

[0030] When analyzing each item of the medical expense statement, the medical expense statement analysis unit can refer to the evaluation data of medical institutions and prioritize displaying information about highly reliable medical institutions. For example, the medical expense statement analysis unit analyzes each item of the medical expense statement, and the generation AI refers to the evaluation data of medical institutions. For example, it can prioritize displaying medical expenses and drug expenses from highly reliable medical institutions, providing patients with peace of mind. The generation AI evaluates the reliability of medical institutions based on the evaluation data and displays information based on the results. This makes it possible to prioritize displaying information about highly reliable medical institutions, providing patients with peace of mind.

[0031] The medical expense statement analysis unit can link the analysis results of the medical expense statement with the patient's health condition and treatment history to provide personalized health advice. For example, the medical expense statement analysis unit links the analysis results of the medical expense statement with the patient's health condition and treatment history, and the generation AI provides personalized health advice. For example, it can propose future treatment plans and preventative measures based on past treatment history. The generation AI performs analysis based on the patient's health condition and treatment history, and provides advice based on the results. This enables more appropriate medical management by providing personalized health advice based on the patient's health condition and treatment history.

[0032] The medical expense statement analysis unit not only analyzes scanned data of medical expense statements, but also prescriptions and test results at the same time, enabling comprehensive visualization of medical expenses. For example, the medical expense statement analysis unit not only analyzes scanned data of medical expense statements, but also prescriptions and test results at the same time, and the generation AI enables comprehensive visualization of medical expenses. For example, it displays comprehensive medical expenses including the cost of prescription drugs and test costs. The generation AI integrates and analyzes multiple data sources, and visualizes medical expenses based on the results. This makes it possible to visualize comprehensive medical expenses, including prescriptions and test results.

[0033] The medical expense detail analysis unit integrates the analysis results of the medical expense details with the medical expense data of all family members, making it possible to visualize medical expenses for the entire household. For example, the medical expense detail analysis unit integrates the analysis results of the medical expense details with the medical expense data of all family members, and the generation AI visualizes the medical expenses for the entire household. For example, the medical expenses and drug expenses of all family members can be displayed together to grasp the medical expenses of the entire household. The generation AI integrates and analyzes the medical expense data of all family members, and visualizes the medical expenses based on the results. This makes it possible to integrate and visualize the medical expenses of all family members, making it possible to manage medical expenses for the entire household.

[0034] When visualizing the composition of medical expenses, the generating AI can refer to statistical data by region and age and provide comparative information. For example, when visualizing the composition of medical expenses, the generating AI can refer to statistical data by region and provide comparative information. For example, it can show how much a patient's medical expenses are compared to the average medical expenses in the same region. The generating AI performs analysis based on statistical data by region and age and provides comparative information based on the results. In this way, by referencing statistical data by region and age and providing comparative information, it becomes easier for patients to understand their medical expenses.

[0035] When visualizing the composition of medical expenses, the generating AI can refer to the patient's lifestyle data and provide health management advice. For example, when visualizing the composition of medical expenses, the generating AI can refer to the patient's lifestyle data and provide health management advice. For example, it can suggest healthy lifestyle habits based on diet and exercise habits. The generating AI performs analysis based on the lifestyle data and provides advice based on the results. In this way, by referring to the patient's lifestyle data and providing health management advice, it is possible to support the patient's health management.

[0036] When visualizing the composition of medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, when visualizing the composition of medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, it can compare multiple insurance plans and select the most appropriate plan for the patient. The generative AI can simulate insurance plans and propose the most appropriate plan based on the results. This allows the patient to select the most appropriate insurance plan by simulating different medical insurance plans and proposing the most appropriate plan.

[0037] When visualizing the composition of medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, when visualizing the composition of medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, by comparing it with households in the same area or with the same family structure, it can identify items where savings can be made. The generating AI performs analysis based on the medical expense data of other households and provides tips for saving money based on the results. In this way, by comparing it with the medical expense data of other households and providing tips for saving money, patients can save on medical expenses.

[0038] When performing a time-series trend analysis, the generation AI can analyze seasonal fluctuations in medical expenses and provide seasonal health management advice. For example, when performing a time-series trend analysis, the generation AI can analyze seasonal fluctuations in medical expenses and provide seasonal health management advice. For example, in response to medical expenses that increase in winter, the generation AI can provide advice on cold prevention. The generation AI performs analysis based on seasonal data and provides advice based on the results. In this way, the generation AI can support patients' health management by analyzing seasonal fluctuations in medical expenses and providing seasonal health management advice.

[0039] The generating AI can refer to the patient's lifestyle data when analyzing trends over time and suggest improvements to the lifestyle. For example, when analyzing trends over time, the generating AI can refer to the patient's lifestyle data and suggest improvements to the lifestyle. For example, it can suggest healthy lifestyle habits based on eating and exercise habits. The generating AI performs analysis based on the lifestyle data and suggests improvements based on the results. In this way, by referring to the patient's lifestyle data and suggesting improvements to the lifestyle, it is possible to support the patient's health management.

[0040] When performing a time-course analysis, the generating AI can refer to data from different medical institutions and compare trends in medical expenses for each medical institution. For example, when performing a time-course analysis, the generating AI can refer to data from different medical institutions and compare trends in medical expenses for each medical institution. For example, it can compare trends in medical fees and drug fees at multiple medical institutions and provide the optimal option. The generating AI performs analysis based on data from medical institutions and provides comparative information based on the results. This allows patients to select the most appropriate medical institution by referring to data from different medical institutions and comparing trends in medical expenses for each medical institution.

[0041] When performing time-course analysis, the generative AI can compare data with that of other patients to find common trends. For example, when performing time-course analysis, the generative AI can compare data with that of other patients to find common trends. For example, it can compare the trends in medical expenses of patients with the same condition and identify common treatment patterns. The generative AI performs analysis based on the data of other patients and finds common trends based on the results. This allows patients to select the optimal treatment by comparing data with that of other patients and finding common trends.

[0042] When analyzing factors that increase or decrease medical costs, the generating AI can refer to evaluation data from medical institutions and provide information on highly reliable medical institutions. For example, when analyzing factors that increase or decrease medical costs, the generating AI can refer to evaluation data from medical institutions and provide information on highly reliable medical institutions. For example, it can provide detailed explanations of the treatment contents and costs of highly rated medical institutions. The generating AI evaluates the reliability of medical institutions based on the evaluation data and provides information based on the results. This allows the provision of information on highly reliable medical institutions to give patients peace of mind.

[0043] When analyzing factors that increase or decrease medical expenses, generative AI can refer to a patient's health condition and treatment history to provide personalized health advice. For example, when analyzing factors that increase or decrease medical expenses, generative AI can refer to a patient's health condition and treatment history to provide personalized health advice. For example, it can propose future treatment plans and preventative measures based on past treatment history. Generative AI performs analysis based on health condition and treatment history and provides advice based on the results. This enables more appropriate medical management by providing personalized health advice based on the patient's health condition and treatment history.

[0044] When analyzing factors that increase or decrease medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, when analyzing factors that increase or decrease medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, it can compare multiple insurance plans and select the most appropriate plan for the patient. The generative AI can simulate insurance plans and propose the most appropriate plan based on the results. This allows the patient to select the most appropriate insurance plan by simulating different medical insurance plans and proposing the most appropriate plan.

[0045] When analyzing factors behind increases or decreases in medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, when analyzing factors behind increases or decreases in medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, by comparing it with households in the same area or with the same family structure, it can identify items where savings can be made. The generating AI performs analysis based on the medical expense data of other households and provides tips for saving money based on the results. This allows patients to save on medical expenses by comparing it with the medical expense data of other households and providing tips for saving money.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The medical expense visualization system can also add a voice input function to the medical expense statement analysis section. For example, if a patient describes their medical expense statement verbally, the generation AI will analyze the voice data and automatically enter the details of the medical expense statement. This allows patients to import their medical expense statement into the system without any hassle. The voice input function is also useful for patients with visual impairments, making medical expense visualization available to more people.

[0048] The medical cost visualization system can also refer to regional medical cost data in the medical cost breakdown analysis section and provide comparative information on medical costs by region. For example, it can show how much a patient's medical costs are compared to those of other patients in the same region. The generation AI performs analysis based on regional data and provides comparative information based on the results. This makes it easier for patients to understand their own medical costs by providing comparative information on medical costs by region.

[0049] The medical expense visualization system can also integrate the medical expense data of all family members into the medical expense detail analysis section, making the medical expenses of the entire household visible. For example, the medical expenses and drug costs of all family members can be displayed together to grasp the medical expenses of the entire household. The generation AI integrates and analyzes the medical expense data of all family members, and visualizes the medical expenses based on the results. This makes it possible to integrate and visualize the medical expenses of all family members, making it possible to manage medical expenses for the entire household.

[0050] The medical expense visualization system can also add a simulation function for different medical insurance plans to the medical expense statement analysis section, allowing it to propose the optimal plan. For example, it can compare multiple insurance plans and select the most suitable plan for the patient. The generation AI simulates insurance plans and proposes the optimal plan based on the results. This allows patients to select the most suitable insurance plan by simulating different medical insurance plans and proposing the optimal plan.

[0051] The medical expense visualization system can also provide savings tips by having the medical expense detail analysis section compare the medical expense data of other households. For example, it can identify items where savings can be made by comparing with households in the same area or with the same family structure. The generation AI performs analysis based on the medical expense data of other households and provides savings tips based on the results. This allows patients to save on medical expenses by comparing with other households' medical expense data and providing savings tips.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The medical expense statement analysis unit analyzes the medical expense statement. For example, the medical expense statement analysis unit scans the medical expense statement, and the generation AI automatically analyzes each item. The medical expense statement analysis unit also automatically classifies the medical expenses, drug expenses, test expenses, etc. listed on the medical expense statement, and explains how each expense is composed. The generation AI receives the scanned data of the medical expense statement as input and performs analysis based on that data. Step 2: The visualization unit visualizes the medical expense breakdown data analyzed by the medical expense breakdown analysis unit in the form of graphs and charts. For example, the visualization unit displays the overall medical expense breakdown using pie charts and bar graphs based on the data analyzed by the generation AI. This allows patients to intuitively understand the breakdown of their medical expenses. Step 3: The trend analysis unit analyzes past medical expense data and displays monthly trends in medical expenses. For example, the generation AI displays a line graph showing the increase or decrease in medical expenses over the past year, allowing users to visually confirm which months saw increases in medical expenses. The generation AI receives past medical expense details data as input and performs analysis based on that data. Step 4: The proposal unit analyzes the factors behind increases and decreases in medical expenses and proposes areas for improvement and savings. For example, the proposal unit uses the generation AI to analyze the causes of increases in medical expenses in a particular month and propose specific methods to eliminate those causes. The generation AI receives detailed medical expense data and the results of that analysis as input, and makes proposals based on that data.

[0054] (Example 2) The medical expense visualization system according to an embodiment of the present invention automatically analyzes medical expense statements and uses AI to thoroughly visualize monthly medical expenses. This allows patients to wisely save on their medical expenses.

[0055] The medical expense visualization system according to the embodiment includes a medical expense statement analysis unit, a visualization unit, a trend analysis unit, and a proposal unit. The medical expense statement analysis unit analyzes the medical expense statement. For example, the medical expense statement analysis unit scans the medical expense statement, and the generation AI automatically analyzes each item. The medical expense statement analysis unit also automatically classifies the medical expenses, drug expenses, and testing expenses listed on the medical expense statement, and explains how each expense is composed. The generation AI receives the scanned data of the medical expense statement as input and performs analysis based on that data. The visualization unit visualizes the medical expense statement data analyzed by the medical expense statement analysis unit using graphs and diagrams. For example, the visualization unit displays the overall medical expense composition using pie charts and bar graphs based on the data analyzed by the generation AI. This allows patients to intuitively understand the breakdown of their medical expenses. The trend analysis unit analyzes past medical expense data and displays monthly trends in medical expenses. For example, the trend analysis unit allows the generation AI to display a line graph showing the increase or decrease in medical expenses over the past year, allowing users to visually check which months saw increases in medical expenses. The generation AI receives past medical expense statement data as input and performs analysis based on that data. The proposal unit analyzes the factors behind increases or decreases in medical expenses and proposes areas for improvement or savings. For example, the proposal unit allows the generation AI to analyze the cause of an increase in medical expenses in a specific month and propose specific methods for eliminating that cause. The generation AI receives medical expense statement data and the analysis results as input and makes proposals based on that data. This allows the medical expense visualization system according to the embodiment to help patients save on their medical expenses wisely. For example, understanding the breakdown of medical expenses can help reduce unnecessary expenses. Furthermore, analyzing the factors behind increases or decreases in medical expenses can identify specific areas for improvement that will lead to future savings in medical expenses.

[0056] The medical expense statement analysis unit uses an emotion estimation function to analyze the patient's emotions from the scan data and can provide commentary based on those emotions. For example, the medical expense statement analysis unit analyzes the scan data of a medical expense statement, and the generation AI estimates the patient's emotions. For example, it analyzes the anxiety and stress a patient feels when medical expenses are high, and provides commentary that gives a sense of security based on those emotions. The generation AI uses an emotion estimation algorithm to analyze the patient's emotions from the scan data and generates commentary based on the results. This makes it possible to provide commentary based on the patient's emotions, giving a sense of security.

[0057] When analyzing each item of the medical expense statement, the medical expense statement analysis unit can refer to the evaluation data of medical institutions and prioritize displaying information about highly reliable medical institutions. For example, the medical expense statement analysis unit analyzes each item of the medical expense statement, and the generation AI refers to the evaluation data of medical institutions. For example, it can prioritize displaying medical expenses and drug expenses from highly reliable medical institutions, providing patients with peace of mind. The generation AI evaluates the reliability of medical institutions based on the evaluation data and displays information based on the results. This makes it possible to prioritize displaying information about highly reliable medical institutions, providing patients with peace of mind.

[0058] The medical expense statement analysis unit can link the analysis results of the medical expense statement with the patient's health condition and treatment history to provide personalized health advice. For example, the medical expense statement analysis unit links the analysis results of the medical expense statement with the patient's health condition and treatment history, and the generation AI provides personalized health advice. For example, it can propose future treatment plans and preventative measures based on past treatment history. The generation AI performs analysis based on the patient's health condition and treatment history, and provides advice based on the results. This enables more appropriate medical management by providing personalized health advice based on the patient's health condition and treatment history.

[0059] The medical expense statement analysis unit not only analyzes scanned data of medical expense statements, but also prescriptions and test results at the same time, enabling comprehensive visualization of medical expenses. For example, the medical expense statement analysis unit not only analyzes scanned data of medical expense statements, but also prescriptions and test results at the same time, and the generation AI enables comprehensive visualization of medical expenses. For example, it displays comprehensive medical expenses including the cost of prescription drugs and test costs. The generation AI integrates and analyzes multiple data sources, and visualizes medical expenses based on the results. This makes it possible to visualize comprehensive medical expenses, including prescriptions and test results.

[0060] The medical expense detail analysis unit integrates the analysis results of the medical expense details with the medical expense data of all family members, making it possible to visualize medical expenses for the entire household. For example, the medical expense detail analysis unit integrates the analysis results of the medical expense details with the medical expense data of all family members, and the generation AI visualizes the medical expenses for the entire household. For example, the medical expenses and drug expenses of all family members can be displayed together to grasp the medical expenses of the entire household. The generation AI integrates and analyzes the medical expense data of all family members, and visualizes the medical expenses based on the results. This makes it possible to integrate and visualize the medical expenses of all family members, making it possible to manage medical expenses for the entire household.

[0061] The medical expense statement analysis unit can use the emotion estimation function to collect the patient's emotional response to the analysis results of the medical expense statement and provide commentary that elicits positive emotions. For example, the medical expense statement analysis unit can use the emotion estimation function to collect the patient's emotional response to the analysis results of the medical expense statement, and the generation AI can provide commentary that elicits positive emotions. For example, it can provide positive information about the effectiveness of treatment and the likelihood of recovery. The generation AI uses an emotion estimation algorithm to analyze the patient's emotional response and generates commentary based on the results. In this way, by collecting the patient's emotional response and providing commentary that elicits positive emotions, it is possible to increase the patient's sense of security.

[0062] When visualizing the overall composition of medical expenses, the generative AI can use its emotion estimation function to analyze the patient's emotions and generate graphs and diagrams based on their emotions. For example, when visualizing the overall composition of medical expenses, the generative AI can use its emotion estimation function to analyze the patient's emotions and generate graphs based on their emotions. For example, it can use colors and designs that evoke positive emotions. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and generates graphs and diagrams based on the results. This can help patients understand by generating graphs and diagrams based on their emotions.

[0063] When visualizing the composition of medical expenses, the generating AI can refer to statistical data by region and age and provide comparative information. For example, when visualizing the composition of medical expenses, the generating AI can refer to statistical data by region and provide comparative information. For example, it can show how much a patient's medical expenses are compared to the average medical expenses in the same region. The generating AI performs analysis based on statistical data by region and age and provides comparative information based on the results. In this way, by referencing statistical data by region and age and providing comparative information, it becomes easier for patients to understand their medical expenses.

[0064] When visualizing the composition of medical expenses, the generating AI can refer to the patient's lifestyle data and provide health management advice. For example, when visualizing the composition of medical expenses, the generating AI can refer to the patient's lifestyle data and provide health management advice. For example, it can suggest healthy lifestyle habits based on diet and exercise habits. The generating AI performs analysis based on the lifestyle data and provides advice based on the results. In this way, by referring to the patient's lifestyle data and providing health management advice, it is possible to support the patient's health management.

[0065] When visualizing the composition of medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, when visualizing the composition of medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, it can compare multiple insurance plans and select the most appropriate plan for the patient. The generative AI can simulate insurance plans and propose the most appropriate plan based on the results. This allows the patient to select the most appropriate insurance plan by simulating different medical insurance plans and proposing the most appropriate plan.

[0066] When visualizing the composition of medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, when visualizing the composition of medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, by comparing it with households in the same area or with the same family structure, it can identify items where savings can be made. The generating AI performs analysis based on the medical expense data of other households and provides tips for saving money based on the results. In this way, by comparing it with the medical expense data of other households and providing tips for saving money, patients can save on medical expenses.

[0067] When performing a time-course analysis, the generative AI can use an emotion estimation function to analyze the patient's emotions and generate a transition graph based on the emotions. For example, when performing a time-course analysis, the generative AI can use an emotion estimation function to analyze the patient's emotions and generate a transition graph based on the emotions. For example, it can use colors and designs that elicit positive emotions. The generative AI analyzes the patient's emotions using an emotion estimation algorithm and generates a transition graph based on the results. This can help patients understand their emotions by generating a transition graph based on their emotions.

[0068] When performing a time-series trend analysis, the generation AI can analyze seasonal fluctuations in medical expenses and provide seasonal health management advice. For example, when performing a time-series trend analysis, the generation AI can analyze seasonal fluctuations in medical expenses and provide seasonal health management advice. For example, in response to medical expenses that increase in winter, the generation AI can provide advice on cold prevention. The generation AI performs analysis based on seasonal data and provides advice based on the results. In this way, the generation AI can support patients' health management by analyzing seasonal fluctuations in medical expenses and providing seasonal health management advice.

[0069] The generating AI can refer to the patient's lifestyle data when analyzing trends over time and suggest improvements to the lifestyle. For example, when analyzing trends over time, the generating AI can refer to the patient's lifestyle data and suggest improvements to the lifestyle. For example, it can suggest healthy lifestyle habits based on eating and exercise habits. The generating AI performs analysis based on the lifestyle data and suggests improvements based on the results. In this way, by referring to the patient's lifestyle data and suggesting improvements to the lifestyle, it is possible to support the patient's health management.

[0070] When performing a time-course analysis, the generating AI can refer to data from different medical institutions and compare trends in medical expenses for each medical institution. For example, when performing a time-course analysis, the generating AI can refer to data from different medical institutions and compare trends in medical expenses for each medical institution. For example, it can compare trends in medical fees and drug fees at multiple medical institutions and provide the optimal option. The generating AI performs analysis based on data from medical institutions and provides comparative information based on the results. This allows patients to select the most appropriate medical institution by referring to data from different medical institutions and comparing trends in medical expenses for each medical institution.

[0071] When performing time-course analysis, the generative AI can compare data with that of other patients to find common trends. For example, when performing time-course analysis, the generative AI can compare data with that of other patients to find common trends. For example, it can compare the trends in medical expenses of patients with the same condition and identify common treatment patterns. The generative AI performs analysis based on the data of other patients and finds common trends based on the results. This allows patients to select the optimal treatment by comparing data with that of other patients and finding common trends.

[0072] The emotion estimation function can collect the patient's emotional responses over time and generate a transition graph that elicits positive emotions. For example, the emotion estimation function collects the patient's emotional responses over time and the generation AI generates a transition graph that elicits positive emotions. For example, it uses colors and designs that convey a sense of security. The generation AI analyzes the patient's emotional responses using an emotion estimation algorithm and generates a transition graph based on the results. In this way, by collecting the patient's emotional responses and generating a transition graph that elicits positive emotions, it is possible to increase the patient's understanding and sense of security.

[0073] When analyzing factors that increase or decrease medical expenses, the generative AI can use its emotion estimation function to analyze the patient's emotions and suggest improvements based on those emotions. For example, when analyzing factors that increase or decrease medical expenses, the generative AI can use its emotion estimation function to analyze the patient's emotions and suggest improvements based on those emotions. For example, it can provide specific advice to reduce the patient's anxiety. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and suggests improvements based on the results. This makes it possible to increase the patient's sense of security by suggesting improvements based on the patient's emotions.

[0074] When analyzing factors that increase or decrease medical costs, the generating AI can refer to evaluation data from medical institutions and provide information on highly reliable medical institutions. For example, when analyzing factors that increase or decrease medical costs, the generating AI can refer to evaluation data from medical institutions and provide information on highly reliable medical institutions. For example, it can provide detailed explanations of the treatment contents and costs of highly rated medical institutions. The generating AI evaluates the reliability of medical institutions based on the evaluation data and provides information based on the results. This allows the provision of information on highly reliable medical institutions to give patients peace of mind.

[0075] When analyzing factors that increase or decrease medical expenses, generative AI can refer to a patient's health condition and treatment history to provide personalized health advice. For example, when analyzing factors that increase or decrease medical expenses, generative AI can refer to a patient's health condition and treatment history to provide personalized health advice. For example, it can propose future treatment plans and preventative measures based on past treatment history. Generative AI performs analysis based on health condition and treatment history and provides advice based on the results. This enables more appropriate medical management by providing personalized health advice based on the patient's health condition and treatment history.

[0076] When analyzing factors that increase or decrease medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, when analyzing factors that increase or decrease medical expenses, the generative AI can simulate different medical insurance plans and propose the most appropriate plan. For example, it can compare multiple insurance plans and select the most appropriate plan for the patient. The generative AI can simulate insurance plans and propose the most appropriate plan based on the results. This allows the patient to select the most appropriate insurance plan by simulating different medical insurance plans and proposing the most appropriate plan.

[0077] When analyzing factors behind increases or decreases in medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, when analyzing factors behind increases or decreases in medical expenses, the generating AI can compare it with the medical expense data of other households and provide tips for saving money. For example, by comparing it with households in the same area or with the same family structure, it can identify items where savings can be made. The generating AI performs analysis based on the medical expense data of other households and provides tips for saving money based on the results. This allows patients to save on medical expenses by comparing it with the medical expense data of other households and providing tips for saving money.

[0078] The emotion estimation function collects the patient's emotional responses to factors that increase or decrease medical expenses, and can suggest improvements that will elicit positive emotions. For example, the emotion estimation function collects the patient's emotional responses to factors that increase or decrease medical expenses, and the generation AI suggests improvements that will elicit positive emotions. For example, it provides specific advice that will give a sense of security. The generation AI uses an emotion estimation algorithm to analyze the patient's emotional responses, and suggests improvements based on the results. In this way, by collecting the patient's emotional responses and suggesting improvements that will elicit positive emotions, it is possible to increase the patient's sense of security.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The medical expense visualization system can also add a voice input function to the medical expense statement analysis section. For example, if a patient describes their medical expense statement verbally, the generation AI will analyze the voice data and automatically enter the details of the medical expense statement. This allows patients to import their medical expense statement into the system without any hassle. The voice input function is also useful for patients with visual impairments, making medical expense visualization available to more people.

[0081] The medical cost visualization system can also use the emotion estimation function in the medical cost detail analysis section to provide advice on saving on medical costs based on the patient's emotions. For example, if a patient is anxious about rising medical costs, it can suggest specific ways to save money to alleviate that anxiety. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and provides advice based on the results. This increases the patient's sense of security by providing advice that is sensitive to their emotions.

[0082] The medical cost visualization system can also refer to regional medical cost data in the medical cost breakdown analysis section and provide comparative information on medical costs by region. For example, it can show how much a patient's medical costs are compared to those of other patients in the same region. The generation AI performs analysis based on regional data and provides comparative information based on the results. This makes it easier for patients to understand their own medical costs by providing comparative information on medical costs by region.

[0083] The medical cost visualization system can also use the emotion estimation function in the medical cost detail analysis section to provide advice on selecting a medical institution based on the patient's emotions. For example, if a patient is feeling uneasy about a particular medical institution, it will suggest a reliable medical institution to alleviate that anxiety. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and provides advice based on the results. This increases the patient's sense of security by providing advice on selecting a medical institution based on the patient's emotions.

[0084] The medical expense visualization system can also integrate the medical expense data of all family members into the medical expense detail analysis section, making the medical expenses of the entire household visible. For example, the medical expenses and drug costs of all family members can be displayed together to grasp the medical expenses of the entire household. The generation AI integrates and analyzes the medical expense data of all family members, and visualizes the medical expenses based on the results. This makes it possible to integrate and visualize the medical expenses of all family members, making it possible to manage medical expenses for the entire household.

[0085] The medical cost visualization system can also use an emotion estimation function in the medical cost detail analysis section to generate a graph of medical cost trends based on the patient's emotions. For example, it can use colors and designs that evoke positive emotions. The generation AI uses an emotion estimation algorithm to analyze the patient's emotions and generates a graph of trends based on the results. This helps patients understand the situation by generating a graph of trends based on their emotions.

[0086] The medical expense visualization system can also add a simulation function for different medical insurance plans to the medical expense statement analysis section, allowing it to propose the optimal plan. For example, it can compare multiple insurance plans and select the most suitable plan for the patient. The generation AI simulates insurance plans and proposes the optimal plan based on the results. This allows patients to select the most suitable insurance plan by simulating different medical insurance plans and proposing the optimal plan.

[0087] The medical cost visualization system can also use the emotion estimation function in the medical cost detail analysis section to make suggestions for saving on medical costs based on the patient's emotions. For example, if a patient is anxious about rising medical costs, it can suggest specific ways to save money to alleviate that anxiety. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and makes suggestions based on the results. This makes it possible to make saving suggestions that are in tune with the patient's emotions, thereby increasing their sense of security.

[0088] The medical expense visualization system can also provide savings tips by having the medical expense detail analysis section compare the medical expense data of other households. For example, it can identify items where savings can be made by comparing with households in the same area or with the same family structure. The generation AI performs analysis based on the medical expense data of other households and provides savings tips based on the results. This allows patients to save on medical expenses by comparing with other households' medical expense data and providing savings tips.

[0089] The medical cost visualization system can also use an emotion estimation function in the medical cost detail analysis section to analyze factors that increase or decrease medical costs based on the patient's emotions. For example, if a patient is anxious about the cause of an increase in medical costs in a particular month, specific advice can be provided to alleviate that anxiety. The generative AI uses an emotion estimation algorithm to analyze the patient's emotions and provides advice based on the results. This allows for an increased sense of security for patients by analyzing factors that increase or decrease medical costs based on the patient's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The medical expense statement analysis unit analyzes the medical expense statement. For example, the medical expense statement analysis unit scans the medical expense statement, and the generation AI automatically analyzes each item. The medical expense statement analysis unit also automatically classifies the medical expenses, drug expenses, test expenses, etc. listed on the medical expense statement, and explains how each expense is composed. The generation AI receives the scanned data of the medical expense statement as input and performs analysis based on that data. Step 2: The visualization unit visualizes the medical expense breakdown data analyzed by the medical expense breakdown analysis unit in the form of graphs and charts. For example, the visualization unit displays the overall medical expense breakdown using pie charts and bar graphs based on the data analyzed by the generation AI. This allows patients to intuitively understand the breakdown of their medical expenses. Step 3: The trend analysis unit analyzes past medical expense data and displays monthly trends in medical expenses. For example, the generation AI displays a line graph showing the increase or decrease in medical expenses over the past year, allowing users to visually confirm which months saw increases in medical expenses. The generation AI receives past medical expense details data as input and performs analysis based on that data. Step 4: The proposal unit analyzes the factors behind increases and decreases in medical expenses and proposes areas for improvement and savings. For example, the proposal unit uses the generation AI to analyze the causes of increases in medical expenses in a particular month and propose specific methods to eliminate those causes. The generation AI receives detailed medical expense data and the results of that analysis as input, and makes proposals based on that data.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 medical expense statement analysis unit that analyzes medical expense statements; a visualization unit that visualizes the medical expense details data analyzed by the medical expense details analysis unit in the form of graphs or diagrams; A trend analysis section that analyzes past medical expense data and displays monthly trends in medical expenses; A proposal section that analyzes factors that increase or decrease medical expenses and proposes areas for improvement or savings. A system characterized by:

2. The medical expense detail analysis unit Analyzing patient emotions in relation to scan data and providing commentary based on said emotions 2. The system of claim 1.

3. The medical expense detail analysis unit In addition to the scanned data of the medical expense details, prescriptions and test results are also analyzed at the same time to achieve comprehensive visualization of medical expenses.

2. The system of claim 1.

4. The generating AI is Analyzing patient sentiment when visualizing the overall medical expense structure and generating graphs and charts based on the sentiment 2. The system of claim 1.

5. The generating AI is Analyzing patient emotions during time-course analysis and generating a transition graph based on the emotions 2. The system of claim 1.

6. The generating AI is Analyzing the patient's feelings when analyzing the factors that cause increases or decreases in the medical expenses, and proposing improvements based on the feelings.

2. The system of claim 1.

7. The medical expense detail analysis unit Collecting the patient's emotional response to the analysis results of the medical bill and providing commentary that elicits positive emotions 2. The system of claim 1.

8. The emotion estimation function Collect patients' emotional responses to their progress over time and generate a progress graph that elicits positive emotions 2. The system of claim 1.

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