system

Abstract

We provide the system. [Solution] An input means for inputting patient diagnostic information and image data, An analysis means that searches for similar cases from a past medical case database based on the data input via the aforementioned input means, A generation means that generates multiple treatment plans based on the results obtained by the analysis means, An evaluation means for calculating the suitability of the generated treatment options, A visualization means for visualizing and presenting the evaluation results obtained by the aforementioned evaluation means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For patients with cancer or serious illnesses, it is difficult to obtain a second opinion quickly and effectively. In particular, when the disease progresses rapidly, the time for collecting appropriate diagnostic information and considering specialized treatment options is limited. Therefore, there is a lack of support means for patients and their families to select the optimal treatment method. To solve this problem, it is necessary to develop a system that can provide quick and accurate diagnostic results and treatment plans.

Means for Solving the Problems

[0005] The present invention provides a system that includes input means for inputting patient diagnostic information and image data, analysis means for searching for similar cases from a past medical case database based on the input data, generation means for generating multiple treatment options based on the analysis results, evaluation means for calculating the suitability of the generated treatment options, and visualization means for visualizing the evaluation results. This allows patients and their families to quickly and easily understand treatment options and access appropriate medical institutions.

[0006] "Input means" refers to a device or interface for inputting patient diagnostic information and image data into the system.

[0007] "Analysis means" refers to a device or program for searching and analyzing similar cases from a database of past medical cases based on the input data.

[0008] "Generation means" refers to a device or program for generating multiple treatment plans from the results obtained by the analysis means.

[0009] "Evaluation means" refers to a device or program for calculating and evaluating the suitability of the generated treatment options.

[0010] "Visualization means" refers to a device or program that presents the evaluation results obtained by the evaluation means to the user in an easily understandable manner. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] As an embodiment of the present invention, a system is presented that allows patients with cancer or other serious illnesses to quickly obtain a second opinion. The system has the following configuration and operation.

[0033] First, the user inputs the patient's diagnostic information and medical imaging data into the terminal. This includes CT scan images, MRI images, and the patient's basic personal information. After the terminal performs an integrity check on this data, it transmits it to the server at the system's central location.

[0034] The server first processes the received data using an analysis tool. Here, it compares the data with a database of previously accumulated medical cases to search for similar cases. This analysis is performed using a pattern recognition algorithm.

[0035] Next, the server generates multiple treatment options using a generation mechanism based on the analysis results. The generation mechanism proposes treatment options based on similar cases obtained through the analysis and the latest medical guidelines. These may include treatments such as chemotherapy, radiation therapy, and immunotherapy.

[0036] Subsequently, the server uses an evaluation tool to calculate the suitability rate for the generated treatment plans. Here, the degree to which each treatment plan is suitable for the patient's situation is evaluated from the perspectives of success rate, risk, and other factors.

[0037] Finally, the server transmits the evaluation results to the user's terminal in an easily understandable format using visualization tools. The visualized data includes the risks and benefits of each treatment plan, information on medical institutions, and past success stories of each treatment.

[0038] As a concrete example, when a lung cancer patient uses the system, after the user inputs the patient's diagnostic data, the system suggests, for example, standard chemotherapy and a new treatment option such as immunotherapy. Detailed information, including the success rate, risks, and past cases of each treatment method, is visualized, along with the location of the medical facility. This allows the patient and their family to quickly and accurately choose a treatment plan.

[0039] In this way, the present invention streamlines the process of obtaining a second opinion and supports patients in making decisions to receive the most appropriate treatment.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scans and MRI images, as well as entering the patient's basic information and medical history.

[0043] Step 2:

[0044] The terminal checks the integrity of the entered data to ensure there are no errors. If errors are found, the user is prompted to re-enter the data. If there are no problems, the data is encrypted and sent to the server.

[0045] Step 3:

[0046] The server passes the received data to an analysis tool and searches for similar cases in a database of past medical cases. During this process, a pattern recognition algorithm is applied to identify past diagnoses that match the patient's symptoms.

[0047] Step 4:

[0048] The server generates treatment plans based on the analysis results. Multiple treatment options are proposed, each based on the latest medical guidelines and similar cases.

[0049] Step 5:

[0050] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. Here, it calculates how well the proposed treatment is suitable for the patient by evaluating the success rate, risks, and past success cases of each treatment method.

[0051] Step 6:

[0052] The server processes the evaluation results using visualization tools and creates data for display in a user-friendly format. Specifically, it formats comparative information on treatment options into graphs and tables, and also includes information on medical institutions.

[0053] Step 7:

[0054] The terminal receives visualization data sent from the server and presents it to the user. Based on the displayed information, the user evaluates the advantages and disadvantages of treatment options and makes the optimal choice.

[0055] In this way, the system provides a second opinion quickly and effectively through a series of processes, supporting patients and their families in deciding on the appropriate treatment plan.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] For patients to obtain a second opinion quickly and accurately, they need to rapidly compare a lot of information and select the appropriate treatment. However, current systems make it difficult to properly analyze diagnostic information and medical imaging data to propose effective treatment plans. Problems exist with the consistency of information and the accuracy of analysis, resulting in the challenge that it takes time for patients to receive the optimal treatment.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes information acquisition means, data verification means, information analysis means, treatment proposal means, treatment evaluation means, and result display means. This enables efficient processing of patient diagnostic information and image data, rapid presentation of highly suitable treatment methods, and allows patients to make informed and optimal treatment decisions.

[0061] "Information acquisition means" refers to the means of inputting patient diagnostic information and medical image data.

[0062] "Data verification means" are methods for confirming the integrity of input data and ensuring its accuracy.

[0063] "Information analysis means" refers to methods for searching for similar cases by comparing them with past medical case databases.

[0064] A "treatment proposal method" is a means for generating multiple treatment plans based on analysis results.

[0065] A "treatment evaluation tool" is a means of calculating the suitability rate of a generated treatment plan and evaluating how well it fits the patient's situation.

[0066] A "results display method" is a means of visually presenting evaluation results and providing information in a way that is easy for users to understand.

[0067] This invention is a system designed to help patients with cancer or other serious illnesses obtain a second opinion quickly. The system mainly consists of a server and terminals, which are operated by the user. The server performs various analyses and generation tasks, while the terminals handle data input and output.

[0068] The user first inputs the patient's diagnostic information and image data into the terminal. This input includes CT scan images, MRI images, and the patient's basic personal information. The terminal performs an integrity check and sends the data to the server. The server analyzes the data using a pattern recognition algorithm with a programming language such as Python. This algorithm compares the input data with a database of past medical cases and searches for similar cases.

[0069] The server generates multiple treatment options using a generative AI model based on information obtained through analysis. This generative method proposes treatment options based on similar cases and the latest medical guidelines. The accuracy of the generated treatment options is calculated by an evaluation tool on the server. The accuracy indicates how well each treatment option matches the patient's detailed information and is evaluated in terms of success rate and risk.

[0070] Finally, the evaluation results are visualized via the server and transmitted to the terminal. The visualized information is displayed in graphs and tables for easy user understanding and includes the risks and benefits of each treatment plan, information on related medical institutions, and success stories.

[0071] As a specific example, in the case of a lung cancer patient, the user inputs patient data from a terminal, and the server uses this data to suggest chemotherapy as a standard treatment and immunotherapy as a new treatment option. A prompt message such as "Based on the patient's diagnostic data, please suggest standard treatment and new treatment options for lung cancer, and evaluate the success rate and risk" can be used.

[0072] This system allows patients to make quick and accurate treatment choices based on a variety of information.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user uses a terminal to input patient diagnostic information and medical imaging data. Specific inputs include CT scan images, MRI images, and patient personal information (name, age, medical history, etc.). The terminal verifies the integrity of the entered data and prepares it for accurate transmission to the server. This process checks for the correctness of the data format and the absence of required input fields.

[0076] Step 2:

[0077] The server receives patient data transmitted from the terminal. First, the server re-verifies data integrity to confirm that the received data is in the correct format. Next, the server uses information analysis tools to compare the data with a database of past medical cases. As a specific data processing step, a pattern recognition algorithm is used to search for similar cases and obtain the analysis results. A list of similar cases is generated as the output of the analysis.

[0078] Step 3:

[0079] The server uses a generative AI model to generate treatment plans tailored to the patient, based on similar cases obtained through analysis. Similar cases and the latest medical guidelines are used as input, and multiple treatment options are created from the processed data. The generated treatment plans include details such as chemotherapy and immunotherapy. The output of the generation is a set of detailed information for each treatment plan.

[0080] Step 4:

[0081] The server calculates the fit rate for each generated treatment plan using treatment evaluation tools. This is done using statistical data on the risks and success rates of each treatment as input. The data is then processed, and the calculated fit rate indicates the effectiveness of each treatment plan. The output is a list containing the fit rate for each treatment.

[0082] Step 5:

[0083] The server ultimately visualizes the evaluation results using a results display device and sends them to the terminal. It receives a list of evaluation results as input and its specific actions include plotting the data in graph or tabular format. The visualized output is presented in a user-friendly format, displaying information such as the success rate of the treatment plan, risks, past success stories, and recommended medical facilities on the terminal.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] In modern medicine, it is crucial for patients to obtain second opinions quickly and accurately, but ensuring the safety and reliability of data during this process remains a challenge. Furthermore, it is necessary to ensure that the information obtained is easily understandable and allows for comparison and evaluation by the patient.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes data input means, data analysis means, information generation means, analysis and evaluation means, information visualization means, and data encryption and transmission means. This allows patients to securely transmit diagnostic information and receive the analyzed results in a visually easy-to-understand format.

[0089] A "data input means" is an interface for users to input patient diagnostic information and image data.

[0090] A "data analysis tool" is a module that includes an algorithm for searching for similar cases from a database of past medical cases based on the input information.

[0091] "Information generation means" refers to the process of generating multiple treatment strategies based on the results obtained through data analysis means.

[0092] The "analysis and evaluation means" is a function for calculating and evaluating the suitability of the generated treatment strategy.

[0093] An "information visualization means" is a system for visually presenting evaluation results obtained through analysis and evaluation means to the user.

[0094] "Data encryption transmission means" refers to a technology for encrypting analysis results and securely transmitting them to a designated recipient.

[0095] To implement this invention, the user must first input patient diagnostic information and medical image data into a terminal. The data input means is responsible for supplying this data to the system. The data is transmitted from the terminal to the server using a secure communication protocol (e.g., HTTPS). The transmitted information is encrypted by a data encryption transmission means and is protected from unauthorized access by third parties.

[0096] The server uses data analysis tools to compare the received data with a database of past medical cases. Machine learning techniques and pattern recognition algorithms are used to search for similar cases. This provides users with treatment strategies based on past success stories and new medical guidelines.

[0097] Based on the analysis results, the information generation system generates multiple treatment strategies. In this process, treatment options such as chemotherapy and immunotherapy are considered as choices. Next, the analysis and evaluation system calculates the suitability of each treatment strategy and evaluates the success rate, risks, etc.

[0098] To make the information easier for users to understand, visualization tools present these evaluation results on the device in the form of graphs and tables. Data visualization libraries (e.g., D3.js) are often used for this visualization. A concrete example is a patient with CT scan and MRI results comparing the success rates and risks of presented treatment options to aid in decision-making.

[0099] An example of a prompt message to run this system is: "Analyze and visualize treatment options for obtaining a second opinion on lung cancer treatment based on patient data."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The user inputs diagnostic information and medical image data into the terminal. This includes CT scan images, MRI images, and basic patient information. The input data undergoes a consistency check in preparation for being imported into the system, and is then processed via the data input method.

[0103] Step 2:

[0104] The terminal sends pre-processed data to the server using a secure communication protocol. The data encryption transmission method encrypts the data and transmits it securely in a way that prevents third-party access. This encryption process ensures the confidentiality of the data.

[0105] Step 3:

[0106] The server decrypts the received encrypted data and compares it with a database of past medical cases using data analysis tools. Machine learning algorithms are used to identify similar medical cases. As a result, similar cases are listed as output of the data analysis.

[0107] Step 4:

[0108] Based on the results of data analysis, the server uses information generation mechanisms to generate multiple treatment strategies. These strategies include different treatment options such as chemotherapy and immunotherapy. This generated information then becomes input data for the next evaluation step.

[0109] Step 5:

[0110] The server calculates the suitability of the generated treatment strategies using analytical and evaluation tools. The evaluation is based on success rates and risk assessments. This quantifies the suitability of each treatment option.

[0111] Step 6:

[0112] The server converts the analysis and evaluation results into graphs and tables using information visualization tools and presents them to the user. The data is visualized and sent to the user's terminal. This makes it easier for the user to understand the details of each treatment option.

[0113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0114] This invention provides a system for patients with cancer or other serious illnesses to obtain a quick and appropriate second opinion, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized information delivery. The specific operation of each component is described below.

[0115] First, the user inputs the patient's diagnostic information and image data into the terminal. This data includes CT scans, MRI images, and basic patient information (age, gender, medical history, etc.). The terminal then sends the entered data to the server.

[0116] The server processes diagnostic information using analysis tools based on the received data and searches for similar cases in a database of past medical cases. Based on the analysis results, the server creates multiple treatment options using a generation tool and uses an evaluation tool to calculate the suitability rate for each option.

[0117] Furthermore, the server incorporates an emotion engine that detects the user's emotions in real time from their voice tone and entered text. This emotion engine adjusts the visualization information based on the user's emotional state. For example, if the user is under high stress, it simplifies the information presentation and adds support information as needed.

[0118] For example, if a patient uses the system to seek a second opinion on lung cancer, the emotion engine detects that the user is feeling anxious. Based on the analysis, the server presents standard treatments as well as the latest immunotherapy options. However, if the user's anxiety is high, the information presented is simplified, and reassuring words based on treatment success rates and case studies are selected. Furthermore, information on nearby medical institutions is added to help the user proceed with treatment smoothly.

[0119] In this way, the present invention, which combines an emotion engine, aims to go beyond mere information provision and enable support tailored to the user's emotional state, thereby improving the quality of medical decision-making and enhancing patients' sense of security.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scan and MRI image files, and entering the patient's basic information and medical history.

[0123] Step 2:

[0124] The terminal sends the entered data to the server. The data is encrypted and an integrity check is performed before transmission.

[0125] Step 3:

[0126] The server processes the received diagnostic information using analytical tools and searches for similar cases in a database of past medical cases. This process uses a pattern recognition algorithm to identify the case that most closely matches the patient's symptoms.

[0127] Step 4:

[0128] The server uses a generation mechanism to create multiple treatment plans based on the analysis results. These treatment plans include standard therapies and the latest treatments, and are customized based on the patient's personal information.

[0129] Step 5:

[0130] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. The evaluation criteria include success rates, risks, and past treatment cases.

[0131] Step 6:

[0132] The device monitors the user's emotional state using an emotion engine. This engine analyzes the user's voice tone and language patterns to detect their emotions in real time.

[0133] Step 7:

[0134] The server adjusts the visualization data based on the detected emotions and presents treatment options in a concise and easy-to-understand format. For users experiencing high stress levels, detailed information is simplified, and reassuring support messages are added.

[0135] Step 8:

[0136] The device displays adjusted visualization data to the user. The user refers to this data to understand the merits and demerits of each treatment method and consider the next steps. The visualization data also includes location information and contact details for the selected appropriate medical institutions.

[0137] This system allows users to receive information tailored to their emotional state and effectively consider treatment options.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] A challenge in providing medical information is the lack of personalized information that takes into account the patient's emotional state. In particular, with serious illnesses, excessive information can actually increase patient anxiety, making emotionally sensitive information provision essential.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes equipment for inputting patient diagnostic information and image information, an analysis device for searching for similar information from past case records, and an emotion recognition device for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present appropriate and effective method proposals while taking emotions into consideration.

[0143] "Input equipment" refers to a combination of hardware and software used to import patient diagnostic information and image information into the system.

[0144] An "analytical device" is a device used to search for similar information from past case records and to scientifically evaluate diagnostic information.

[0145] A "formation device" refers to software and hardware used to generate multiple method options based on the results of an analytical device.

[0146] A "judgment device" is a device for quantifying and evaluating the suitability of the generated method options.

[0147] An "emotion recognition device" is software and hardware that analyzes a user's voice or text input to determine their emotional state.

[0148] A "presentation device" is a device for visually displaying the results obtained by the judgment device and the emotion recognition device.

[0149] The following system is configured as an embodiment for carrying out the present invention. This system is designed to enable patients with serious illnesses to quickly obtain a highly accurate second opinion. A specific embodiment will be described below.

[0150] First, the user inputs the patient's diagnostic and imaging information into the terminal. At this stage, basic information such as CT scans, MRI images, age, gender, and medical history is used. This information is entered in an appropriate format and transmitted to the server using the terminal's interface.

[0151] The server uses analytical equipment to analyze the input data. Specifically, it uses digital image processing software to analyze CT and MRI images and detect potential abnormalities. It also searches existing medical case databases for cases similar to the input diagnostic information.

[0152] Next, the server generates multiple treatment options using a forming device. In this process, a generative AI model is utilized to create scientific and rational treatment plans based on past success stories and the latest medical knowledge.

[0153] The generated method proposals are evaluated for suitability by a judgment device. This suitability evaluation is performed using statistical methods and machine learning algorithms, and quantitative indicators are provided.

[0154] In parallel, the server uses an emotion recognition device to detect the user's emotional state in real time from their voice and text input. This allows the server to understand if the user is experiencing high stress or anxiety, and adjust the content of the information presented accordingly.

[0155] Finally, the server visualizes the results generated by the judgment device and emotion recognition device on a presentation device and provides the information to the user's terminal. The information is displayed as graphs and concise text, and reassuring information is added as needed.

[0156] For example, if a user seeks a second opinion on lung cancer, the server will present both standard treatments and the latest immunotherapies. Simultaneously, if the user's anxiety is detected by the emotion recognition system, information providing reassurance, such as treatment success rates and case studies of other patients, will also be displayed. Furthermore, information on nearby medical facilities for initiating treatment will be added.

[0157] An example of a prompt message is: "Based on the diagnostic data entered by the user, provide the most suitable second opinion option. Also, use the emotion engine to present information that will reduce the user's stress."

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The user enters patient diagnostic and imaging information into the terminal. This input data includes CT scans, MRI images, age, gender, and medical history. This data is then formatted and encrypted before being prepared for transmission to the server.

[0161] Step 2:

[0162] The terminal sends the input data to the server. During this process, the data is encrypted using a secure protocol such as SSL / TLS and transmitted to the server over the network. The output is an encrypted data packet.

[0163] Step 3:

[0164] The server receives the data and begins analysis using an analysis device. First, it applies a digital image processing algorithm to extract feature points from CT and MRI images and detect anomalies. The output consists of interpreted digital data and identified anomaly information.

[0165] Step 4:

[0166] The server searches past medical databases based on the analyzed data to identify similar cases. It uses an information retrieval algorithm to generate a list of relevant cases. The output includes metadata and treatment outcome information for similar cases.

[0167] Step 5:

[0168] The server uses a generative AI model to form multiple treatment options based on the data obtained in the previous step. The generating device performs calculations to generate the optimal treatment plan. The output is a list of treatment options.

[0169] Step 6:

[0170] The server evaluates the suitability of treatment plans generated using a judgment device. It calculates the suitability rate for each treatment plan using statistical analysis and machine learning models. The output is suitability rate data for each treatment plan.

[0171] Step 7:

[0172] The server analyzes the user's emotional state using an emotion recognition device. It processes voice and text input from the user using an emotion analysis algorithm to measure stress and anxiety levels. The output is data indicating the user's emotional state.

[0173] Step 8:

[0174] The server adjusts the information presentation on the display device based on the emotion recognition results and generates visualization data. It organizes the information in graph and text formats and prepares it for transmission to the terminal. The output is the adjusted visualization data.

[0175] Step 9:

[0176] The terminal receives pre-configured data from the server and presents the information to the user visually. It displays treatment plans, suitability, and emotionally sensitive messages on the screen, and provides additional support information as needed.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In electronic payments, a challenge exists where information is provided without regard for the user's feelings, failing to alleviate anxiety and hesitation to purchase. Therefore, there is a need for considerate information presentation that takes into account the user's emotional state.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes an input means for inputting user information, an analysis means for searching for similar cases from a past information case data store based on the information input via the input means, and an emotion recognition means for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present information according to the user's emotional state.

[0182] "User" refers to an individual or legal entity that attempts to obtain information using the system.

[0183] "Input means" refers to the means by which users provide information necessary to the system, and includes digital devices and interfaces.

[0184] An "information case data store" refers to a database system that stores information case studies accumulated in the past.

[0185] "Analysis means" refers to a means for searching and extracting similar cases within a data store based on the input information.

[0186] A "generation means" is a means for generating multiple method proposals based on the results obtained from the analysis means.

[0187] "Evaluation means" refers to a device or software that has the function of calculating the precision of the generated options and providing the results to the user.

[0188] "Emotion recognition means" refers to means for detecting the user's emotional state and adjusting the information presented based on that state.

[0189] "Visualization means" refers to a method or apparatus for presenting information and results obtained by evaluation means and emotion recognition means in an easily viewable manner.

[0190] In this invention, the server analyzes the user's input information and searches for similar cases from a data store of past information and cases. The user inputs information via a smartphone or tablet interface, and the server receives it.

[0191] The server uses analysis tools to quickly search for similar cases in the data store based on the input information. Based on the analysis results, the generation tool formulates multiple methodologies. The precision of the formulated methodologies is calculated by the evaluation tool. Subsequently, the emotion recognition tool monitors the user's emotional state from their voice and facial expressions. Information presentation is adjusted as needed.

[0192] Information is presented according to the user's emotional state through visualization methods, in a format that is easy for the user to understand. For example, for users who are feeling anxious, detailed explanations and relevant reassuring information are selectively displayed with heightened emphasis. It is also possible to generate reassuring language using a generative AI model. For this reason, the prompt message used is, "If a user shows signs of anxiety or hesitation before making a purchase, please suggest ways to present them with information that will reassure them."

[0193] For example, if a user is considering purchasing a new gadget and appears anxious, the server will highlight product reviews and warranty information. In this way, a system is implemented that enables support that takes the user's emotional state into consideration.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user uses a terminal to enter the necessary information. This information includes details of the product they are considering purchasing and their basic information. The entered information is then sent from the terminal to the server.

[0197] Step 2:

[0198] The server processes the received information using parsing tools. It searches for similar information examples from a past information example data store and obtains results. The most appropriate information example obtained from the data store is selected based on the context of the input information.

[0199] Step 3:

[0200] The server generates multiple methodologies using a generation mechanism based on the analysis results. The generated methodologies include options related to the user's input information. Each methodology's precision is calculated by an evaluation mechanism. This calculation involves comparing the input information with the characteristics of past cases.

[0201] Step 4:

[0202] The server uses emotion recognition to monitor the user's emotional state from their voice and facial expressions. This allows it to understand the user's current emotions in real time and adjust the information presented as needed. Emotional data is acquired using voice recognition tools and facial expression analysis software.

[0203] Step 5:

[0204] The visualization system visually presents the user with information adjusted based on the results of emotion recognition. This information includes precision calculated by the evaluation system and detailed information designed to provide a sense of security. A graphical user interface is used for visualization.

[0205] Step 6:

[0206] Using a generative AI model, words and sentences designed to instill a sense of security are generated. The prompt, "If a user shows signs of anxiety or hesitation before making a purchase, suggest ways to present them with information that will reassure them," is utilized to generate appropriate messages. The AI's generated results are displayed to the user as final information.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] As an embodiment of the present invention, a system is presented that allows patients with cancer or other serious illnesses to quickly obtain a second opinion. The system has the following configuration and operation.

[0224] First, the user inputs the patient's diagnostic information and medical imaging data into the terminal. This includes CT scan images, MRI images, and the patient's basic personal information. After the terminal performs an integrity check on this data, it transmits it to the server at the system's central location.

[0225] The server first processes the received data using an analysis tool. Here, it compares the data with a database of previously accumulated medical cases to search for similar cases. This analysis is performed using a pattern recognition algorithm.

[0226] Next, the server generates multiple treatment options using a generation mechanism based on the analysis results. The generation mechanism proposes treatment options based on similar cases obtained through the analysis and the latest medical guidelines. These may include treatments such as chemotherapy, radiation therapy, and immunotherapy.

[0227] Subsequently, the server uses an evaluation tool to calculate the suitability rate for the generated treatment plans. Here, the degree to which each treatment plan is suitable for the patient's situation is evaluated from the perspectives of success rate, risk, and other factors.

[0228] Finally, the server transmits the evaluation results to the user's terminal in an easily understandable format using visualization tools. The visualized data includes the risks and benefits of each treatment plan, information on medical institutions, and past success stories of each treatment.

[0229] As a concrete example, when a lung cancer patient uses the system, after the user inputs the patient's diagnostic data, the system suggests, for example, standard chemotherapy and a new treatment option such as immunotherapy. Detailed information, including the success rate, risks, and past cases of each treatment method, is visualized, along with the location of the medical facility. This allows the patient and their family to quickly and accurately choose a treatment plan.

[0230] In this way, the present invention streamlines the process of obtaining a second opinion and supports patients in making decisions to receive the most appropriate treatment.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scans and MRI images, as well as entering the patient's basic information and medical history.

[0234] Step 2:

[0235] The terminal checks the integrity of the entered data to ensure there are no errors. If errors are found, the user is prompted to re-enter the data. If there are no problems, the data is encrypted and sent to the server.

[0236] Step 3:

[0237] The server passes the received data to an analysis tool and searches for similar cases in a database of past medical cases. During this process, a pattern recognition algorithm is applied to identify past diagnoses that match the patient's symptoms.

[0238] Step 4:

[0239] The server generates treatment plans based on the analysis results. Multiple treatment options are proposed, each based on the latest medical guidelines and similar cases.

[0240] Step 5:

[0241] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. Here, it calculates how well the proposed treatment is suitable for the patient by evaluating the success rate, risks, and past success cases of each treatment method.

[0242] Step 6:

[0243] The server processes the evaluation results using visualization tools and creates data for display in a user-friendly format. Specifically, it formats comparative information on treatment options into graphs and tables, and also includes information on medical institutions.

[0244] Step 7:

[0245] The terminal receives visualization data sent from the server and presents it to the user. Based on the displayed information, the user evaluates the advantages and disadvantages of treatment options and makes the optimal choice.

[0246] In this way, the system provides a second opinion quickly and effectively through a series of processes, supporting patients and their families in deciding on the appropriate treatment plan.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] For patients to obtain a second opinion quickly and accurately, they need to rapidly compare a lot of information and select the appropriate treatment. However, current systems make it difficult to properly analyze diagnostic information and medical imaging data to propose effective treatment plans. Problems exist with the consistency of information and the accuracy of analysis, resulting in the challenge that it takes time for patients to receive the optimal treatment.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes information acquisition means, data verification means, information analysis means, treatment proposal means, treatment evaluation means, and result display means. This enables efficient processing of patient diagnostic information and image data, rapid presentation of highly suitable treatment methods, and allows patients to make informed and optimal treatment decisions.

[0252] "Information acquisition means" refers to the means of inputting patient diagnostic information and medical image data.

[0253] "Data verification means" are methods for confirming the integrity of input data and ensuring its accuracy.

[0254] "Information analysis means" refers to methods for searching for similar cases by comparing them with past medical case databases.

[0255] A "treatment proposal method" is a means for generating multiple treatment plans based on analysis results.

[0256] A "treatment evaluation tool" is a means of calculating the suitability rate of a generated treatment plan and evaluating how well it fits the patient's situation.

[0257] A "results display method" is a means of visually presenting evaluation results and providing information in a way that is easy for users to understand.

[0258] This invention is a system designed to help patients with cancer or other serious illnesses obtain a second opinion quickly. The system mainly consists of a server and terminals, which are operated by the user. The server performs various analyses and generation tasks, while the terminals handle data input and output.

[0259] The user first inputs the patient's diagnostic information and image data into the terminal. This input includes CT scan images, MRI images, and the patient's basic personal information. The terminal performs an integrity check and sends the data to the server. The server analyzes the data using a pattern recognition algorithm with a programming language such as Python. This algorithm compares the input data with a database of past medical cases and searches for similar cases.

[0260] The server generates multiple treatment options using a generative AI model based on information obtained through analysis. This generative method proposes treatment options based on similar cases and the latest medical guidelines. The accuracy of the generated treatment options is calculated by an evaluation tool on the server. The accuracy indicates how well each treatment option matches the patient's detailed information and is evaluated in terms of success rate and risk.

[0261] Finally, the evaluation results are visualized via the server and transmitted to the terminal. The visualized information is displayed in graphs and tables for easy user understanding and includes the risks and benefits of each treatment plan, information on related medical institutions, and success stories.

[0262] As a specific example, in the case of a lung cancer patient, the user inputs patient data from a terminal, and the server uses this data to suggest chemotherapy as a standard treatment and immunotherapy as a new treatment option. A prompt message such as "Based on the patient's diagnostic data, please suggest standard treatment and new treatment options for lung cancer, and evaluate the success rate and risk" can be used.

[0263] This system allows patients to make quick and accurate treatment choices based on a variety of information.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The user uses a terminal to input patient diagnostic information and medical imaging data. Specific inputs include CT scan images, MRI images, and patient personal information (name, age, medical history, etc.). The terminal verifies the integrity of the entered data and prepares it for accurate transmission to the server. This process checks for the correctness of the data format and the absence of required input fields.

[0267] Step 2:

[0268] The server receives patient data transmitted from the terminal. First, the server re-verifies data integrity to confirm that the received data is in the correct format. Next, the server uses information analysis tools to compare the data with a database of past medical cases. As a specific data processing step, a pattern recognition algorithm is used to search for similar cases and obtain the analysis results. A list of similar cases is generated as the output of the analysis.

[0269] Step 3:

[0270] The server uses a generative AI model to generate treatment plans tailored to the patient, based on similar cases obtained through analysis. Similar cases and the latest medical guidelines are used as input, and multiple treatment options are created from the processed data. The generated treatment plans include details such as chemotherapy and immunotherapy. The output of the generation is a set of detailed information for each treatment plan.

[0271] Step 4:

[0272] The server calculates the fit rate for each generated treatment plan using treatment evaluation tools. This is done using statistical data on the risks and success rates of each treatment as input. The data is then processed, and the calculated fit rate indicates the effectiveness of each treatment plan. The output is a list containing the fit rate for each treatment.

[0273] Step 5:

[0274] The server ultimately visualizes the evaluation results using a results display device and sends them to the terminal. It receives a list of evaluation results as input and its specific actions include plotting the data in graph or tabular format. The visualized output is presented in a user-friendly format, displaying information such as the success rate of the treatment plan, risks, past success stories, and recommended medical facilities on the terminal.

[0275] (Application Example 1)

[0276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0277] In modern medicine, it is crucial for patients to obtain second opinions quickly and accurately, but ensuring the safety and reliability of data during this process remains a challenge. Furthermore, it is necessary to ensure that the information obtained is easily understandable and allows for comparison and evaluation by the patient.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes data input means, data analysis means, information generation means, analysis evaluation means, information visualization means, and data encryption and transmission means. As a result, the patient can securely transmit diagnostic information and receive the analyzed results in a visually understandable manner.

[0280] The "data input means" is an interface for the user to input the patient's diagnostic information and image data.

[0281] The "data analysis means" is a module including an algorithm for searching for similar cases from the past medical case database based on the input information.

[0282] The "information generation means" is a process for generating a plurality of treatment strategies based on the results obtained by the data analysis means.

[0283] The "analysis evaluation means" is a function for calculating and evaluating the compliance rate of the generated treatment strategies.

[0284] The "information visualization means" is a system for visually presenting the evaluation results obtained by the analysis evaluation means to the user.

[0285] The "data encryption and transmission means" is a technology for encrypting the analysis results and securely transmitting them to a specified recipient.

[0286] To implement this invention, first, the user needs to input the patient's diagnostic information and medical image data into the terminal. The data input means has the role of supplying these data to the system. The data is transmitted from the terminal to the server using a secure communication protocol (e.g., HTTPS). The transmitted information is encrypted by the data encryption transmission means and is protected from unauthorized access by third parties.

[0287] The server uses data analysis means to compare the received data with the past medical case database. Machine learning techniques and pattern recognition algorithms are used as algorithms for searching for similar cases. As a result, treatment strategies based on past successful cases and new medical guidelines are provided to the user.

[0288] Based on the analysis results, the information generation means generates multiple treatment strategies. In this process, treatment options such as chemotherapy and immunotherapy are considered as options. Next, the analysis evaluation means calculates the compliance rate of each treatment strategy and evaluates the success rate and risks, etc.

[0289] For the user to understand easily, the information visualization means presents these evaluation results to the terminal in the form of graphs or tables. Data visualization libraries (e.g., D3.js) are often used for this visualization. As a specific example, it is a case where a patient with CT scan and MRI results compares the success rate and risks of the presented treatment options and uses them for decision-making.

[0290] An example of the prompt sentence for running this system is "Analyze and visualize treatment options for obtaining a second opinion on lung cancer treatment based on patient data."

[0291] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0292] Step 1:

[0293] The user inputs diagnostic information and medical image data into the terminal. This includes CT scan images, MRI images, and basic patient information. The input data undergoes a consistency check in preparation for being imported into the system, and is then processed via the data input method.

[0294] Step 2:

[0295] The terminal sends pre-processed data to the server using a secure communication protocol. The data encryption transmission method encrypts the data and transmits it securely in a way that prevents third-party access. This encryption process ensures the confidentiality of the data.

[0296] Step 3:

[0297] The server decrypts the received encrypted data and compares it with a database of past medical cases using data analysis tools. Machine learning algorithms are used to identify similar medical cases. As a result, similar cases are listed as output of the data analysis.

[0298] Step 4:

[0299] Based on the results of data analysis, the server uses information generation mechanisms to generate multiple treatment strategies. These strategies include different treatment options such as chemotherapy and immunotherapy. This generated information then becomes input data for the next evaluation step.

[0300] Step 5:

[0301] The server calculates the suitability of the generated treatment strategies using analytical and evaluation tools. The evaluation is based on success rates and risk assessments. This quantifies the suitability of each treatment option.

[0302] Step 6:

[0303] The server converts the analysis and evaluation results into graphs or tables by means of information visualization means and presents them to the user. The data is visualized and transmitted to the user's terminal. Thereby, the user can more easily understand the details of each treatment option.

[0304] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0305] The present invention is a system for a patient with cancer or a serious illness to obtain a quick and appropriate second opinion, and particularly realizes more personalized information provision by combining an emotion engine that recognizes the user's emotion. The specific operations of each component will be described below.

[0306] First, the user inputs the patient's diagnosis information and image data into the terminal. This data includes CT scans, MRI images, and the patient's basic information (age, gender, past medical history, etc.). The terminal transmits the input data to the server.

[0307] Based on the received data, the server processes the diagnosis information using analysis means and searches for similar cases from the past medical case database. Based on the analysis results, the server creates a plurality of treatment plans using generation means and uses evaluation means to calculate the matching rate for each option.

[0308] Furthermore, an emotion engine is incorporated in the server, which detects the emotion in real time from the user's voice tone and the input text. This emotion engine adjusts the visualization information based on the user's emotional state. For example, when the user is in a high-stress state, the information presentation is made concise and support information is added as necessary.

[0309] For example, if a patient uses the system to seek a second opinion on lung cancer, the emotion engine detects that the user is feeling anxious. Based on the analysis, the server presents standard treatments as well as the latest immunotherapy options. However, if the user's anxiety is high, the information presented is simplified, and reassuring words based on treatment success rates and case studies are selected. Furthermore, information on nearby medical institutions is added to help the user proceed with treatment smoothly.

[0310] In this way, the present invention, which combines an emotion engine, aims to go beyond mere information provision and enable support tailored to the user's emotional state, thereby improving the quality of medical decision-making and enhancing patients' sense of security.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scan and MRI image files, and entering the patient's basic information and medical history.

[0314] Step 2:

[0315] The terminal sends the entered data to the server. The data is encrypted and an integrity check is performed before transmission.

[0316] Step 3:

[0317] The server processes the received diagnostic information using analytical tools and searches for similar cases in a database of past medical cases. This process uses a pattern recognition algorithm to identify the case that most closely matches the patient's symptoms.

[0318] Step 4:

[0319] The server uses a generation mechanism to create multiple treatment plans based on the analysis results. These treatment plans include standard therapies and the latest treatments, and are customized based on the patient's personal information.

[0320] Step 5:

[0321] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. The evaluation criteria include success rates, risks, and past treatment cases.

[0322] Step 6:

[0323] The device monitors the user's emotional state using an emotion engine. This engine analyzes the user's voice tone and language patterns to detect their emotions in real time.

[0324] Step 7:

[0325] The server adjusts the visualization data based on the detected emotions and presents treatment options in a concise and easy-to-understand format. For users experiencing high stress levels, detailed information is simplified, and reassuring support messages are added.

[0326] Step 8:

[0327] The device displays adjusted visualization data to the user. The user refers to this data to understand the merits and demerits of each treatment method and consider the next steps. The visualization data also includes location information and contact details for the selected appropriate medical institutions.

[0328] This system allows users to receive information tailored to their emotional state and effectively consider treatment options.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] A challenge in providing medical information is the lack of personalized information that takes into account the patient's emotional state. In particular, with serious illnesses, excessive information can actually increase patient anxiety, making emotionally sensitive information provision essential.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes equipment for inputting patient diagnostic information and image information, an analysis device for searching for similar information from past case records, and an emotion recognition device for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present appropriate and effective method proposals while taking emotions into consideration.

[0334] "Input equipment" refers to a combination of hardware and software used to import patient diagnostic information and image information into the system.

[0335] An "analytical device" is a device used to search for similar information from past case records and to scientifically evaluate diagnostic information.

[0336] A "formation device" refers to software and hardware used to generate multiple method options based on the results of an analytical device.

[0337] A "judgment device" is a device for quantifying and evaluating the suitability of the generated method options.

[0338] An "emotion recognition device" is software and hardware that analyzes a user's voice or text input to determine their emotional state.

[0339] A "presentation device" is a device for visually displaying the results obtained by the judgment device and the emotion recognition device.

[0340] The following system is configured as an embodiment for carrying out the present invention. This system is designed to enable patients with serious illnesses to quickly obtain a highly accurate second opinion. A specific embodiment will be described below.

[0341] First, the user inputs the patient's diagnostic and imaging information into the terminal. At this stage, basic information such as CT scans, MRI images, age, gender, and medical history is used. This information is entered in an appropriate format and transmitted to the server using the terminal's interface.

[0342] The server uses analytical equipment to analyze the input data. Specifically, it uses digital image processing software to analyze CT and MRI images and detect potential abnormalities. It also searches existing medical case databases for cases similar to the input diagnostic information.

[0343] Next, the server generates multiple treatment options using a forming device. In this process, a generative AI model is utilized to create scientific and rational treatment plans based on past success stories and the latest medical knowledge.

[0344] The generated method proposals are evaluated for suitability by a judgment device. This suitability evaluation is performed using statistical methods and machine learning algorithms, and quantitative indicators are provided.

[0345] In parallel, the server uses an emotion recognition device to detect the user's emotional state in real time from their voice and text input. This allows the server to understand if the user is experiencing high stress or anxiety, and adjust the content of the information presented accordingly.

[0346] Finally, the server visualizes the results generated by the judgment device and emotion recognition device on a presentation device and provides the information to the user's terminal. The information is displayed as graphs and concise text, and reassuring information is added as needed.

[0347] For example, if a user seeks a second opinion on lung cancer, the server will present both standard treatments and the latest immunotherapies. Simultaneously, if the user's anxiety is detected by the emotion recognition system, information providing reassurance, such as treatment success rates and case studies of other patients, will also be displayed. Furthermore, information on nearby medical facilities for initiating treatment will be added.

[0348] An example of a prompt message is: "Based on the diagnostic data entered by the user, provide the most suitable second opinion option. Also, use the emotion engine to present information that will reduce the user's stress."

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The user enters patient diagnostic and imaging information into the terminal. This input data includes CT scans, MRI images, age, gender, and medical history. This data is then formatted and encrypted before being prepared for transmission to the server.

[0352] Step 2:

[0353] The terminal sends the input data to the server. During this process, the data is encrypted using a secure protocol such as SSL / TLS and transmitted to the server over the network. The output is an encrypted data packet.

[0354] Step 3:

[0355] The server receives the data and begins analysis using an analysis device. First, it applies a digital image processing algorithm to extract feature points from CT and MRI images and detect anomalies. The output consists of interpreted digital data and identified anomaly information.

[0356] Step 4:

[0357] The server searches past medical databases based on the analyzed data to identify similar cases. It uses an information retrieval algorithm to generate a list of relevant cases. The output includes metadata and treatment outcome information for similar cases.

[0358] Step 5:

[0359] The server uses a generative AI model to form multiple treatment options based on the data obtained in the previous step. The generating device performs calculations to generate the optimal treatment plan. The output is a list of treatment options.

[0360] Step 6:

[0361] The server evaluates the suitability of treatment plans generated using a judgment device. It calculates the suitability rate for each treatment plan using statistical analysis and machine learning models. The output is suitability rate data for each treatment plan.

[0362] Step 7:

[0363] The server analyzes the user's emotional state using an emotion recognition device. It processes voice and text input from the user using an emotion analysis algorithm to measure stress and anxiety levels. The output is data indicating the user's emotional state.

[0364] Step 8:

[0365] The server adjusts the information presentation on the display device based on the emotion recognition results and generates visualization data. It organizes the information in graph and text formats and prepares it for transmission to the terminal. The output is the adjusted visualization data.

[0366] Step 9:

[0367] The terminal receives pre-configured data from the server and presents the information to the user visually. It displays treatment plans, suitability, and emotionally sensitive messages on the screen, and provides additional support information as needed.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] In electronic payments, a challenge exists where information is provided without regard for the user's feelings, failing to alleviate anxiety and hesitation to purchase. Therefore, there is a need for considerate information presentation that takes into account the user's emotional state.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0372] In this invention, the server includes an input means for inputting user information, an analysis means for searching for similar cases from a past information case data store based on the information input via the input means, and an emotion recognition means for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present information according to the user's emotional state.

[0373] "User" refers to an individual or legal entity that attempts to obtain information using the system.

[0374] "Input means" refers to the means by which users provide information necessary to the system, and includes digital devices and interfaces.

[0375] An "information case data store" refers to a database system that stores information case studies accumulated in the past.

[0376] "Analysis means" refers to a means for searching and extracting similar cases within a data store based on the input information.

[0377] A "generation means" is a means for generating multiple method proposals based on the results obtained from the analysis means.

[0378] "Evaluation means" refers to a device or software that has the function of calculating the precision of the generated options and providing the results to the user.

[0379] "Emotion recognition means" refers to means for detecting the user's emotional state and adjusting the information presented based on that state.

[0380] "Visualization means" refers to a method or apparatus for presenting information and results obtained by evaluation means and emotion recognition means in an easily viewable manner.

[0381] In this invention, the server analyzes the user's input information and searches for similar cases from a data store of past information and cases. The user inputs information via a smartphone or tablet interface, and the server receives it.

[0382] The server uses analysis tools to quickly search for similar cases in the data store based on the input information. Based on the analysis results, the generation tool formulates multiple methodologies. The precision of the formulated methodologies is calculated by the evaluation tool. Subsequently, the emotion recognition tool monitors the user's emotional state from their voice and facial expressions. Information presentation is adjusted as needed.

[0383] Information is presented according to the user's emotional state through visualization methods, in a format that is easy for the user to understand. For example, for users who are feeling anxious, detailed explanations and relevant reassuring information are selectively displayed with heightened emphasis. It is also possible to generate reassuring language using a generative AI model. For this reason, the prompt message used is, "If a user shows signs of anxiety or hesitation before making a purchase, please suggest ways to present them with information that will reassure them."

[0384] For example, if a user is considering purchasing a new gadget and appears anxious, the server will highlight product reviews and warranty information. In this way, a system is implemented that enables support that takes the user's emotional state into consideration.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user uses a terminal to enter the necessary information. This information includes details of the product they are considering purchasing and their basic information. The entered information is then sent from the terminal to the server.

[0388] Step 2:

[0389] The server processes the received information using parsing tools. It searches for similar information examples from a past information example data store and obtains results. The most appropriate information example obtained from the data store is selected based on the context of the input information.

[0390] Step 3:

[0391] The server generates multiple methodologies using a generation mechanism based on the analysis results. The generated methodologies include options related to the user's input information. Each methodology's precision is calculated by an evaluation mechanism. This calculation involves comparing the input information with the characteristics of past cases.

[0392] Step 4:

[0393] The server uses emotion recognition to monitor the user's emotional state from their voice and facial expressions. This allows it to understand the user's current emotions in real time and adjust the information presented as needed. Emotional data is acquired using voice recognition tools and facial expression analysis software.

[0394] Step 5:

[0395] The visualization system visually presents the user with information adjusted based on the results of emotion recognition. This information includes precision calculated by the evaluation system and detailed information designed to provide a sense of security. A graphical user interface is used for visualization.

[0396] Step 6:

[0397] Using a generative AI model, words and sentences designed to instill a sense of security are generated. The prompt, "If a user shows signs of anxiety or hesitation before making a purchase, suggest ways to present them with information that will reassure them," is utilized to generate appropriate messages. The AI's generated results are displayed to the user as final information.

[0398] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0399] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0400] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0404] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0405] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0406] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0407] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0408] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0409] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0410] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0411] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0412] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0413] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0414] As an embodiment of the present invention, a system is presented that allows patients with cancer or other serious illnesses to quickly obtain a second opinion. The system has the following configuration and operation.

[0415] First, the user inputs the patient's diagnostic information and medical imaging data into the terminal. This includes CT scan images, MRI images, and the patient's basic personal information. After the terminal performs an integrity check on this data, it transmits it to the server at the system's central location.

[0416] The server first processes the received data using an analysis tool. Here, it compares the data with a database of previously accumulated medical cases to search for similar cases. This analysis is performed using a pattern recognition algorithm.

[0417] Next, the server generates multiple treatment options using a generation mechanism based on the analysis results. The generation mechanism proposes treatment options based on similar cases obtained through the analysis and the latest medical guidelines. These may include treatments such as chemotherapy, radiation therapy, and immunotherapy.

[0418] Subsequently, the server uses an evaluation tool to calculate the suitability rate for the generated treatment plans. Here, the degree to which each treatment plan is suitable for the patient's situation is evaluated from the perspectives of success rate, risk, and other factors.

[0419] Finally, the server transmits the evaluation results to the user's terminal in an easily understandable format using visualization tools. The visualized data includes the risks and benefits of each treatment plan, information on medical institutions, and past success stories of each treatment.

[0420] As a concrete example, when a lung cancer patient uses the system, after the user inputs the patient's diagnostic data, the system suggests, for example, standard chemotherapy and a new treatment option such as immunotherapy. Detailed information, including the success rate, risks, and past cases of each treatment method, is visualized, along with the location of the medical facility. This allows the patient and their family to quickly and accurately choose a treatment plan.

[0421] In this way, the present invention streamlines the process of obtaining a second opinion and supports patients in making decisions to receive the most appropriate treatment.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scans and MRI images, as well as entering the patient's basic information and medical history.

[0425] Step 2:

[0426] The terminal checks the integrity of the entered data to ensure there are no errors. If errors are found, the user is prompted to re-enter the data. If there are no problems, the data is encrypted and sent to the server.

[0427] Step 3:

[0428] The server passes the received data to an analysis tool and searches for similar cases in a database of past medical cases. During this process, a pattern recognition algorithm is applied to identify past diagnoses that match the patient's symptoms.

[0429] Step 4:

[0430] The server generates treatment plans based on the analysis results. Multiple treatment options are proposed, each based on the latest medical guidelines and similar cases.

[0431] Step 5:

[0432] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. Here, it calculates how well the proposed treatment is suitable for the patient by evaluating the success rate, risks, and past success cases of each treatment method.

[0433] Step 6:

[0434] The server processes the evaluation results using visualization tools and creates data for display in a user-friendly format. Specifically, it formats comparative information on treatment options into graphs and tables, and also includes information on medical institutions.

[0435] Step 7:

[0436] The terminal receives visualization data sent from the server and presents it to the user. Based on the displayed information, the user evaluates the advantages and disadvantages of treatment options and makes the optimal choice.

[0437] In this way, the system provides a second opinion quickly and effectively through a series of processes, supporting patients and their families in deciding on the appropriate treatment plan.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] For patients to obtain a second opinion quickly and accurately, they need to rapidly compare a lot of information and select the appropriate treatment. However, current systems make it difficult to properly analyze diagnostic information and medical imaging data to propose effective treatment plans. Problems exist with the consistency of information and the accuracy of analysis, resulting in the challenge that it takes time for patients to receive the optimal treatment.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes information acquisition means, data verification means, information analysis means, treatment proposal means, treatment evaluation means, and result display means. This enables efficient processing of patient diagnostic information and image data, rapid presentation of highly suitable treatment methods, and allows patients to make informed and optimal treatment decisions.

[0443] "Information acquisition means" refers to the means of inputting patient diagnostic information and medical image data.

[0444] "Data verification means" are methods for confirming the integrity of input data and ensuring its accuracy.

[0445] "Information analysis means" refers to methods for searching for similar cases by comparing them with past medical case databases.

[0446] A "treatment proposal method" is a means for generating multiple treatment plans based on analysis results.

[0447] A "treatment evaluation tool" is a means of calculating the suitability rate of a generated treatment plan and evaluating how well it fits the patient's situation.

[0448] A "results display method" is a means of visually presenting evaluation results and providing information in a way that is easy for users to understand.

[0449] This invention is a system designed to help patients with cancer or other serious illnesses obtain a second opinion quickly. The system mainly consists of a server and terminals, which are operated by the user. The server performs various analyses and generation tasks, while the terminals handle data input and output.

[0450] The user first inputs the patient's diagnostic information and image data into the terminal. This input includes CT scan images, MRI images, and the patient's basic personal information. The terminal performs an integrity check and sends the data to the server. The server analyzes the data using a pattern recognition algorithm with a programming language such as Python. This algorithm compares the input data with a database of past medical cases and searches for similar cases.

[0451] The server generates multiple treatment options using a generative AI model based on information obtained through analysis. This generative method proposes treatment options based on similar cases and the latest medical guidelines. The accuracy of the generated treatment options is calculated by an evaluation tool on the server. The accuracy indicates how well each treatment option matches the patient's detailed information and is evaluated in terms of success rate and risk.

[0452] Finally, the evaluation results are visualized via the server and transmitted to the terminal. The visualized information is displayed in graphs and tables for easy user understanding and includes the risks and benefits of each treatment plan, information on related medical institutions, and success stories.

[0453] As a specific example, in the case of a lung cancer patient, the user inputs patient data from a terminal, and the server uses this data to suggest chemotherapy as a standard treatment and immunotherapy as a new treatment option. A prompt message such as "Based on the patient's diagnostic data, please suggest standard treatment and new treatment options for lung cancer, and evaluate the success rate and risk" can be used.

[0454] This system allows patients to make quick and accurate treatment choices based on a variety of information.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user uses a terminal to input patient diagnostic information and medical imaging data. Specific inputs include CT scan images, MRI images, and patient personal information (name, age, medical history, etc.). The terminal verifies the integrity of the entered data and prepares it for accurate transmission to the server. This process checks for the correctness of the data format and the absence of required input fields.

[0458] Step 2:

[0459] The server receives patient data transmitted from the terminal. First, the server re-verifies data integrity to confirm that the received data is in the correct format. Next, the server uses information analysis tools to compare the data with a database of past medical cases. As a specific data processing step, a pattern recognition algorithm is used to search for similar cases and obtain the analysis results. A list of similar cases is generated as the output of the analysis.

[0460] Step 3:

[0461] The server uses a generative AI model to generate treatment plans tailored to the patient, based on similar cases obtained through analysis. Similar cases and the latest medical guidelines are used as input, and multiple treatment options are created from the processed data. The generated treatment plans include details such as chemotherapy and immunotherapy. The output of the generation is a set of detailed information for each treatment plan.

[0462] Step 4:

[0463] The server calculates the fit rate for each generated treatment plan using treatment evaluation tools. This is done using statistical data on the risks and success rates of each treatment as input. The data is then processed, and the calculated fit rate indicates the effectiveness of each treatment plan. The output is a list containing the fit rate for each treatment.

[0464] Step 5:

[0465] The server ultimately visualizes the evaluation results using a results display device and sends them to the terminal. It receives a list of evaluation results as input and its specific actions include plotting the data in graph or tabular format. The visualized output is presented in a user-friendly format, displaying information such as the success rate of the treatment plan, risks, past success stories, and recommended medical facilities on the terminal.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In modern medicine, it is crucial for patients to obtain second opinions quickly and accurately, but ensuring the safety and reliability of data during this process remains a challenge. Furthermore, it is necessary to ensure that the information obtained is easily understandable and allows for comparison and evaluation by the patient.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes data input means, data analysis means, information generation means, analysis and evaluation means, information visualization means, and data encryption and transmission means. This allows patients to securely transmit diagnostic information and receive the analyzed results in a visually easy-to-understand format.

[0471] A "data input means" is an interface for users to input patient diagnostic information and image data.

[0472] A "data analysis tool" is a module that includes an algorithm for searching for similar cases from a database of past medical cases based on the input information.

[0473] "Information generation means" refers to the process of generating multiple treatment strategies based on the results obtained through data analysis means.

[0474] The "analysis and evaluation means" is a function for calculating and evaluating the suitability of the generated treatment strategy.

[0475] An "information visualization means" is a system for visually presenting evaluation results obtained through analysis and evaluation means to the user.

[0476] "Data encryption transmission means" refers to a technology for encrypting analysis results and securely transmitting them to a designated recipient.

[0477] To implement this invention, the user must first input patient diagnostic information and medical image data into a terminal. The data input means is responsible for supplying this data to the system. The data is transmitted from the terminal to the server using a secure communication protocol (e.g., HTTPS). The transmitted information is encrypted by a data encryption transmission means and is protected from unauthorized access by third parties.

[0478] The server uses data analysis tools to compare the received data with a database of past medical cases. Machine learning techniques and pattern recognition algorithms are used to search for similar cases. This provides users with treatment strategies based on past success stories and new medical guidelines.

[0479] Based on the analysis results, the information generation system generates multiple treatment strategies. In this process, treatment options such as chemotherapy and immunotherapy are considered as choices. Next, the analysis and evaluation system calculates the suitability of each treatment strategy and evaluates the success rate, risks, etc.

[0480] To make the information easier for users to understand, visualization tools present these evaluation results on the device in the form of graphs and tables. Data visualization libraries (e.g., D3.js) are often used for this visualization. A concrete example is a patient with CT scan and MRI results comparing the success rates and risks of presented treatment options to aid in decision-making.

[0481] An example of a prompt message to run this system is: "Analyze and visualize treatment options for obtaining a second opinion on lung cancer treatment based on patient data."

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user inputs diagnostic information and medical image data into the terminal. This includes CT scan images, MRI images, and basic patient information. The input data undergoes a consistency check in preparation for being imported into the system, and is then processed via the data input method.

[0485] Step 2:

[0486] The terminal sends pre-processed data to the server using a secure communication protocol. The data encryption transmission method encrypts the data and transmits it securely in a way that prevents third-party access. This encryption process ensures the confidentiality of the data.

[0487] Step 3:

[0488] The server decrypts the received encrypted data and compares it with a database of past medical cases using data analysis tools. Machine learning algorithms are used to identify similar medical cases. As a result, similar cases are listed as output of the data analysis.

[0489] Step 4:

[0490] Based on the results of data analysis, the server uses information generation mechanisms to generate multiple treatment strategies. These strategies include different treatment options such as chemotherapy and immunotherapy. This generated information then becomes input data for the next evaluation step.

[0491] Step 5:

[0492] The server calculates the suitability of the generated treatment strategies using analytical and evaluation tools. The evaluation is based on success rates and risk assessments. This quantifies the suitability of each treatment option.

[0493] Step 6:

[0494] The server converts the analysis and evaluation results into graphs and tables using information visualization tools and presents them to the user. The data is visualized and sent to the user's terminal. This makes it easier for the user to understand the details of each treatment option.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] This invention provides a system for patients with cancer or other serious illnesses to obtain a quick and appropriate second opinion, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized information delivery. The specific operation of each component is described below.

[0497] First, the user inputs the patient's diagnostic information and image data into the terminal. This data includes CT scans, MRI images, and basic patient information (age, gender, medical history, etc.). The terminal then sends the entered data to the server.

[0498] The server processes diagnostic information using analysis tools based on the received data and searches for similar cases in a database of past medical cases. Based on the analysis results, the server creates multiple treatment options using a generation tool and uses an evaluation tool to calculate the suitability rate for each option.

[0499] Furthermore, the server incorporates an emotion engine that detects the user's emotions in real time from their voice tone and entered text. This emotion engine adjusts the visualization information based on the user's emotional state. For example, if the user is under high stress, it simplifies the information presentation and adds support information as needed.

[0500] For example, if a patient uses the system to seek a second opinion on lung cancer, the emotion engine detects that the user is feeling anxious. Based on the analysis, the server presents standard treatments as well as the latest immunotherapy options. However, if the user's anxiety is high, the information presented is simplified, and reassuring words based on treatment success rates and case studies are selected. Furthermore, information on nearby medical institutions is added to help the user proceed with treatment smoothly.

[0501] In this way, the present invention, which combines an emotion engine, aims to go beyond mere information provision and enable support tailored to the user's emotional state, thereby improving the quality of medical decision-making and enhancing patients' sense of security.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scan and MRI image files, and entering the patient's basic information and medical history.

[0505] Step 2:

[0506] The terminal sends the entered data to the server. The data is encrypted and an integrity check is performed before transmission.

[0507] Step 3:

[0508] The server processes the received diagnostic information using analytical tools and searches for similar cases in a database of past medical cases. This process uses a pattern recognition algorithm to identify the case that most closely matches the patient's symptoms.

[0509] Step 4:

[0510] The server uses a generation mechanism to create multiple treatment plans based on the analysis results. These treatment plans include standard therapies and the latest treatments, and are customized based on the patient's personal information.

[0511] Step 5:

[0512] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. The evaluation criteria include success rates, risks, and past treatment cases.

[0513] Step 6:

[0514] The device monitors the user's emotional state using an emotion engine. This engine analyzes the user's voice tone and language patterns to detect their emotions in real time.

[0515] Step 7:

[0516] The server adjusts the visualization data based on the detected emotions and presents treatment options in a concise and easy-to-understand format. For users experiencing high stress levels, detailed information is simplified, and reassuring support messages are added.

[0517] Step 8:

[0518] The device displays adjusted visualization data to the user. The user refers to this data to understand the merits and demerits of each treatment method and consider the next steps. The visualization data also includes location information and contact details for the selected appropriate medical institutions.

[0519] This system allows users to receive information tailored to their emotional state and effectively consider treatment options.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] A challenge in providing medical information is the lack of personalized information that takes into account the patient's emotional state. In particular, with serious illnesses, excessive information can actually increase patient anxiety, making emotionally sensitive information provision essential.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes equipment for inputting patient diagnostic information and image information, an analysis device for searching for similar information from past case records, and an emotion recognition device for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present appropriate and effective method proposals while taking emotions into consideration.

[0525] "Input equipment" refers to a combination of hardware and software used to import patient diagnostic information and image information into the system.

[0526] An "analytical device" is a device used to search for similar information from past case records and to scientifically evaluate diagnostic information.

[0527] A "formation device" refers to software and hardware used to generate multiple method options based on the results of an analytical device.

[0528] A "judgment device" is a device for quantifying and evaluating the suitability of the generated method options.

[0529] An "emotion recognition device" is software and hardware that analyzes a user's voice or text input to determine their emotional state.

[0530] A "presentation device" is a device for visually displaying the results obtained by the judgment device and the emotion recognition device.

[0531] The following system is configured as an embodiment for carrying out the present invention. This system is designed to enable patients with serious illnesses to quickly obtain a highly accurate second opinion. A specific embodiment will be described below.

[0532] First, the user inputs the patient's diagnostic and imaging information into the terminal. At this stage, basic information such as CT scans, MRI images, age, gender, and medical history is used. This information is entered in an appropriate format and transmitted to the server using the terminal's interface.

[0533] The server uses analytical equipment to analyze the input data. Specifically, it uses digital image processing software to analyze CT and MRI images and detect potential abnormalities. It also searches existing medical case databases for cases similar to the input diagnostic information.

[0534] Next, the server generates multiple treatment options using a forming device. In this process, a generative AI model is utilized to create scientific and rational treatment plans based on past success stories and the latest medical knowledge.

[0535] The generated method proposals are evaluated for suitability by a judgment device. This suitability evaluation is performed using statistical methods and machine learning algorithms, and quantitative indicators are provided.

[0536] In parallel, the server uses an emotion recognition device to detect the user's emotional state in real time from their voice and text input. This allows the server to understand if the user is experiencing high stress or anxiety, and adjust the content of the information presented accordingly.

[0537] Finally, the server visualizes the results generated by the judgment device and emotion recognition device on a presentation device and provides the information to the user's terminal. The information is displayed as graphs and concise text, and reassuring information is added as needed.

[0538] For example, if a user seeks a second opinion on lung cancer, the server will present both standard treatments and the latest immunotherapies. Simultaneously, if the user's anxiety is detected by the emotion recognition system, information providing reassurance, such as treatment success rates and case studies of other patients, will also be displayed. Furthermore, information on nearby medical facilities for initiating treatment will be added.

[0539] An example of a prompt message is: "Based on the diagnostic data entered by the user, provide the most suitable second opinion option. Also, use the emotion engine to present information that will reduce the user's stress."

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The user enters patient diagnostic and imaging information into the terminal. This input data includes CT scans, MRI images, age, gender, and medical history. This data is then formatted and encrypted before being prepared for transmission to the server.

[0543] Step 2:

[0544] The terminal sends the input data to the server. During this process, the data is encrypted using a secure protocol such as SSL / TLS and transmitted to the server over the network. The output is an encrypted data packet.

[0545] Step 3:

[0546] The server receives the data and begins analysis using an analysis device. First, it applies a digital image processing algorithm to extract feature points from CT and MRI images and detect anomalies. The output consists of interpreted digital data and identified anomaly information.

[0547] Step 4:

[0548] The server searches past medical databases based on the analyzed data to identify similar cases. It uses an information retrieval algorithm to generate a list of relevant cases. The output includes metadata and treatment outcome information for similar cases.

[0549] Step 5:

[0550] The server uses a generative AI model to form multiple treatment options based on the data obtained in the previous step. The generating device performs calculations to generate the optimal treatment plan. The output is a list of treatment options.

[0551] Step 6:

[0552] The server evaluates the suitability of treatment plans generated using a judgment device. It calculates the suitability rate for each treatment plan using statistical analysis and machine learning models. The output is suitability rate data for each treatment plan.

[0553] Step 7:

[0554] The server analyzes the user's emotional state using an emotion recognition device. It processes voice and text input from the user using an emotion analysis algorithm to measure stress and anxiety levels. The output is data indicating the user's emotional state.

[0555] Step 8:

[0556] The server adjusts the information presentation on the display device based on the emotion recognition results and generates visualization data. It organizes the information in graph and text formats and prepares it for transmission to the terminal. The output is the adjusted visualization data.

[0557] Step 9:

[0558] The terminal receives pre-configured data from the server and presents the information to the user visually. It displays treatment plans, suitability, and emotionally sensitive messages on the screen, and provides additional support information as needed.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] In electronic payments, a challenge exists where information is provided without regard for the user's feelings, failing to alleviate anxiety and hesitation to purchase. Therefore, there is a need for considerate information presentation that takes into account the user's emotional state.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes an input means for inputting user information, an analysis means for searching for similar cases from a past information case data store based on the information input via the input means, and an emotion recognition means for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present information according to the user's emotional state.

[0564] "User" refers to an individual or legal entity that attempts to obtain information using the system.

[0565] "Input means" refers to the means by which users provide information necessary to the system, and includes digital devices and interfaces.

[0566] An "information case data store" refers to a database system that stores information case studies accumulated in the past.

[0567] "Analysis means" refers to a means for searching and extracting similar cases within a data store based on the input information.

[0568] A "generation means" is a means for generating multiple method proposals based on the results obtained from the analysis means.

[0569] "Evaluation means" refers to a device or software that has the function of calculating the precision of the generated options and providing the results to the user.

[0570] "Emotion recognition means" refers to means for detecting the user's emotional state and adjusting the information presented based on that state.

[0571] "Visualization means" refers to a method or apparatus for presenting information and results obtained by evaluation means and emotion recognition means in an easily viewable manner.

[0572] In this invention, the server analyzes the user's input information and searches for similar cases from a data store of past information and cases. The user inputs information via a smartphone or tablet interface, and the server receives it.

[0573] The server uses analysis tools to quickly search for similar cases in the data store based on the input information. Based on the analysis results, the generation tool formulates multiple methodologies. The precision of the formulated methodologies is calculated by the evaluation tool. Subsequently, the emotion recognition tool monitors the user's emotional state from their voice and facial expressions. Information presentation is adjusted as needed.

[0574] Information is presented according to the user's emotional state through visualization methods, in a format that is easy for the user to understand. For example, for users who are feeling anxious, detailed explanations and relevant reassuring information are selectively displayed with heightened emphasis. It is also possible to generate reassuring language using a generative AI model. For this reason, the prompt message used is, "If a user shows signs of anxiety or hesitation before making a purchase, please suggest ways to present them with information that will reassure them."

[0575] For example, if a user is considering purchasing a new gadget and appears anxious, the server will highlight product reviews and warranty information. In this way, a system is implemented that enables support that takes the user's emotional state into consideration.

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The user uses a terminal to enter the necessary information. This information includes details of the product they are considering purchasing and their basic information. The entered information is then sent from the terminal to the server.

[0579] Step 2:

[0580] The server processes the received information using parsing tools. It searches for similar information examples from a past information example data store and obtains results. The most appropriate information example obtained from the data store is selected based on the context of the input information.

[0581] Step 3:

[0582] The server generates multiple methodologies using a generation mechanism based on the analysis results. The generated methodologies include options related to the user's input information. Each methodology's precision is calculated by an evaluation mechanism. This calculation involves comparing the input information with the characteristics of past cases.

[0583] Step 4:

[0584] The server uses emotion recognition to monitor the user's emotional state from their voice and facial expressions. This allows it to understand the user's current emotions in real time and adjust the information presented as needed. Emotional data is acquired using voice recognition tools and facial expression analysis software.

[0585] Step 5:

[0586] The visualization system visually presents the user with information adjusted based on the results of emotion recognition. This information includes precision calculated by the evaluation system and detailed information designed to provide a sense of security. A graphical user interface is used for visualization.

[0587] Step 6:

[0588] Using a generative AI model, words and sentences designed to instill a sense of security are generated. The prompt, "If a user shows signs of anxiety or hesitation before making a purchase, suggest ways to present them with information that will reassure them," is utilized to generate appropriate messages. The AI's generated results are displayed to the user as final information.

[0589] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0590] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0591] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0595] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0596] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0597] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0598] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0599] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0600] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0601] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0602] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0603] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0604] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0605] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] As an embodiment of the present invention, a system is presented that allows patients with cancer or other serious illnesses to quickly obtain a second opinion. The system has the following configuration and operation.

[0607] First, the user inputs the patient's diagnostic information and medical imaging data into the terminal. This includes CT scan images, MRI images, and the patient's basic personal information. After the terminal performs an integrity check on this data, it transmits it to the server at the system's central location.

[0608] The server first processes the received data using an analysis tool. Here, it compares the data with a database of previously accumulated medical cases to search for similar cases. This analysis is performed using a pattern recognition algorithm.

[0609] Next, the server generates multiple treatment options using a generation mechanism based on the analysis results. The generation mechanism proposes treatment options based on similar cases obtained through the analysis and the latest medical guidelines. These may include treatments such as chemotherapy, radiation therapy, and immunotherapy.

[0610] Subsequently, the server uses an evaluation tool to calculate the suitability rate for the generated treatment plans. Here, the degree to which each treatment plan is suitable for the patient's situation is evaluated from the perspectives of success rate, risk, and other factors.

[0611] Finally, the server transmits the evaluation results to the user's terminal in an easily understandable format using visualization tools. The visualized data includes the risks and benefits of each treatment plan, information on medical institutions, and past success stories of each treatment.

[0612] As a concrete example, when a lung cancer patient uses the system, after the user inputs the patient's diagnostic data, the system suggests, for example, standard chemotherapy and a new treatment option such as immunotherapy. Detailed information, including the success rate, risks, and past cases of each treatment method, is visualized, along with the location of the medical facility. This allows the patient and their family to quickly and accurately choose a treatment plan.

[0613] In this way, the present invention streamlines the process of obtaining a second opinion and supports patients in making decisions to receive the most appropriate treatment.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scans and MRI images, as well as entering the patient's basic information and medical history.

[0617] Step 2:

[0618] The terminal checks the integrity of the entered data to ensure there are no errors. If errors are found, the user is prompted to re-enter the data. If there are no problems, the data is encrypted and sent to the server.

[0619] Step 3:

[0620] The server passes the received data to an analysis tool and searches for similar cases in a database of past medical cases. During this process, a pattern recognition algorithm is applied to identify past diagnoses that match the patient's symptoms.

[0621] Step 4:

[0622] The server generates treatment plans based on the analysis results. Multiple treatment options are proposed, each based on the latest medical guidelines and similar cases.

[0623] Step 5:

[0624] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. Here, it calculates how well the proposed treatment is suitable for the patient by evaluating the success rate, risks, and past success cases of each treatment method.

[0625] Step 6:

[0626] The server processes the evaluation results using visualization tools and creates data for display in a user-friendly format. Specifically, it formats comparative information on treatment options into graphs and tables, and also includes information on medical institutions.

[0627] Step 7:

[0628] The terminal receives visualization data sent from the server and presents it to the user. Based on the displayed information, the user evaluates the advantages and disadvantages of treatment options and makes the optimal choice.

[0629] In this way, the system provides a second opinion quickly and effectively through a series of processes, supporting patients and their families in deciding on the appropriate treatment plan.

[0630] (Example 1)

[0631] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] For patients to obtain a second opinion quickly and accurately, they need to rapidly compare a lot of information and select the appropriate treatment. However, current systems make it difficult to properly analyze diagnostic information and medical imaging data to propose effective treatment plans. Problems exist with the consistency of information and the accuracy of analysis, resulting in the challenge that it takes time for patients to receive the optimal treatment.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0634] In this invention, the server includes information acquisition means, data verification means, information analysis means, treatment proposal means, treatment evaluation means, and result display means. This enables efficient processing of patient diagnostic information and image data, rapid presentation of highly suitable treatment methods, and allows patients to make informed and optimal treatment decisions.

[0635] "Information acquisition means" refers to the means of inputting patient diagnostic information and medical image data.

[0636] "Data verification means" are methods for confirming the integrity of input data and ensuring its accuracy.

[0637] "Information analysis means" refers to methods for searching for similar cases by comparing them with past medical case databases.

[0638] A "treatment proposal method" is a means for generating multiple treatment plans based on analysis results.

[0639] A "treatment evaluation tool" is a means of calculating the suitability rate of a generated treatment plan and evaluating how well it fits the patient's situation.

[0640] A "results display method" is a means of visually presenting evaluation results and providing information in a way that is easy for users to understand.

[0641] This invention is a system designed to help patients with cancer or other serious illnesses obtain a second opinion quickly. The system mainly consists of a server and terminals, which are operated by the user. The server performs various analyses and generation tasks, while the terminals handle data input and output.

[0642] The user first inputs the patient's diagnostic information and image data into the terminal. This input includes CT scan images, MRI images, and the patient's basic personal information. The terminal performs an integrity check and sends the data to the server. The server analyzes the data using a pattern recognition algorithm with a programming language such as Python. This algorithm compares the input data with a database of past medical cases and searches for similar cases.

[0643] The server generates multiple treatment options using a generative AI model based on information obtained through analysis. This generative method proposes treatment options based on similar cases and the latest medical guidelines. The accuracy of the generated treatment options is calculated by an evaluation tool on the server. The accuracy indicates how well each treatment option matches the patient's detailed information and is evaluated in terms of success rate and risk.

[0644] Finally, the evaluation results are visualized via the server and transmitted to the terminal. The visualized information is displayed in graphs and tables for easy user understanding and includes the risks and benefits of each treatment plan, information on related medical institutions, and success stories.

[0645] As a specific example, in the case of a lung cancer patient, the user inputs patient data from a terminal, and the server uses this data to suggest chemotherapy as a standard treatment and immunotherapy as a new treatment option. A prompt message such as "Based on the patient's diagnostic data, please suggest standard treatment and new treatment options for lung cancer, and evaluate the success rate and risk" can be used.

[0646] This system allows patients to make quick and accurate treatment choices based on a variety of information.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The user uses a terminal to input patient diagnostic information and medical imaging data. Specific inputs include CT scan images, MRI images, and patient personal information (name, age, medical history, etc.). The terminal verifies the integrity of the entered data and prepares it for accurate transmission to the server. This process checks for the correctness of the data format and the absence of required input fields.

[0650] Step 2:

[0651] The server receives patient data transmitted from the terminal. First, the server re-verifies data integrity to confirm that the received data is in the correct format. Next, the server uses information analysis tools to compare the data with a database of past medical cases. As a specific data processing step, a pattern recognition algorithm is used to search for similar cases and obtain the analysis results. A list of similar cases is generated as the output of the analysis.

[0652] Step 3:

[0653] The server uses a generative AI model to generate treatment plans tailored to the patient, based on similar cases obtained through analysis. Similar cases and the latest medical guidelines are used as input, and multiple treatment options are created from the processed data. The generated treatment plans include details such as chemotherapy and immunotherapy. The output of the generation is a set of detailed information for each treatment plan.

[0654] Step 4:

[0655] The server calculates the fit rate for each generated treatment plan using treatment evaluation tools. This is done using statistical data on the risks and success rates of each treatment as input. The data is then processed, and the calculated fit rate indicates the effectiveness of each treatment plan. The output is a list containing the fit rate for each treatment.

[0656] Step 5:

[0657] The server ultimately visualizes the evaluation results using a results display device and sends them to the terminal. It receives a list of evaluation results as input and its specific actions include plotting the data in graph or tabular format. The visualized output is presented in a user-friendly format, displaying information such as the success rate of the treatment plan, risks, past success stories, and recommended medical facilities on the terminal.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern medicine, it is crucial for patients to obtain second opinions quickly and accurately, but ensuring the safety and reliability of data during this process remains a challenge. Furthermore, it is necessary to ensure that the information obtained is easily understandable and allows for comparison and evaluation by the patient.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server includes data input means, data analysis means, information generation means, analysis and evaluation means, information visualization means, and data encryption and transmission means. This allows patients to securely transmit diagnostic information and receive the analyzed results in a visually easy-to-understand format.

[0663] A "data input means" is an interface for users to input patient diagnostic information and image data.

[0664] A "data analysis tool" is a module that includes an algorithm for searching for similar cases from a database of past medical cases based on the input information.

[0665] "Information generation means" refers to the process of generating multiple treatment strategies based on the results obtained through data analysis means.

[0666] The "analysis and evaluation means" is a function for calculating and evaluating the suitability of the generated treatment strategy.

[0667] An "information visualization means" is a system for visually presenting evaluation results obtained through analysis and evaluation means to the user.

[0668] "Data encryption transmission means" refers to a technology for encrypting analysis results and securely transmitting them to a designated recipient.

[0669] To implement this invention, the user must first input patient diagnostic information and medical image data into a terminal. The data input means is responsible for supplying this data to the system. The data is transmitted from the terminal to the server using a secure communication protocol (e.g., HTTPS). The transmitted information is encrypted by a data encryption transmission means and is protected from unauthorized access by third parties.

[0670] The server uses data analysis tools to compare the received data with a database of past medical cases. Machine learning techniques and pattern recognition algorithms are used to search for similar cases. This provides users with treatment strategies based on past success stories and new medical guidelines.

[0671] Based on the analysis results, the information generation system generates multiple treatment strategies. In this process, treatment options such as chemotherapy and immunotherapy are considered as choices. Next, the analysis and evaluation system calculates the suitability of each treatment strategy and evaluates the success rate, risks, etc.

[0672] To make the information easier for users to understand, visualization tools present these evaluation results on the device in the form of graphs and tables. Data visualization libraries (e.g., D3.js) are often used for this visualization. A concrete example is a patient with CT scan and MRI results comparing the success rates and risks of presented treatment options to aid in decision-making.

[0673] An example of a prompt message to run this system is: "Analyze and visualize treatment options for obtaining a second opinion on lung cancer treatment based on patient data."

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] The user inputs diagnostic information and medical image data into the terminal. This includes CT scan images, MRI images, and basic patient information. The input data undergoes a consistency check in preparation for being imported into the system, and is then processed via the data input method.

[0677] Step 2:

[0678] The terminal sends pre-processed data to the server using a secure communication protocol. The data encryption transmission method encrypts the data and transmits it securely in a way that prevents third-party access. This encryption process ensures the confidentiality of the data.

[0679] Step 3:

[0680] The server decrypts the received encrypted data and compares it with a database of past medical cases using data analysis tools. Machine learning algorithms are used to identify similar medical cases. As a result, similar cases are listed as output of the data analysis.

[0681] Step 4:

[0682] Based on the results of data analysis, the server uses information generation mechanisms to generate multiple treatment strategies. These strategies include different treatment options such as chemotherapy and immunotherapy. This generated information then becomes input data for the next evaluation step.

[0683] Step 5:

[0684] The server calculates the suitability of the generated treatment strategies using analytical and evaluation tools. The evaluation is based on success rates and risk assessments. This quantifies the suitability of each treatment option.

[0685] Step 6:

[0686] The server converts the analysis and evaluation results into graphs and tables using information visualization tools and presents them to the user. The data is visualized and sent to the user's terminal. This makes it easier for the user to understand the details of each treatment option.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention provides a system for patients with cancer or other serious illnesses to obtain a quick and appropriate second opinion, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized information delivery. The specific operation of each component is described below.

[0689] First, the user inputs the patient's diagnostic information and image data into the terminal. This data includes CT scans, MRI images, and basic patient information (age, gender, medical history, etc.). The terminal then sends the entered data to the server.

[0690] The server processes diagnostic information using analysis tools based on the received data and searches for similar cases in a database of past medical cases. Based on the analysis results, the server creates multiple treatment options using a generation tool and uses an evaluation tool to calculate the suitability rate for each option.

[0691] Furthermore, the server incorporates an emotion engine that detects the user's emotions in real time from their voice tone and entered text. This emotion engine adjusts the visualization information based on the user's emotional state. For example, if the user is under high stress, it simplifies the information presentation and adds support information as needed.

[0692] For example, if a patient uses the system to seek a second opinion on lung cancer, the emotion engine detects that the user is feeling anxious. Based on the analysis, the server presents standard treatments as well as the latest immunotherapy options. However, if the user's anxiety is high, the information presented is simplified, and reassuring words based on treatment success rates and case studies are selected. Furthermore, information on nearby medical institutions is added to help the user proceed with treatment smoothly.

[0693] In this way, the present invention, which combines an emotion engine, aims to go beyond mere information provision and enable support tailored to the user's emotional state, thereby improving the quality of medical decision-making and enhancing patients' sense of security.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The user inputs patient diagnostic information and image data into the terminal. This includes uploading CT scan and MRI image files, and entering the patient's basic information and medical history.

[0697] Step 2:

[0698] The terminal sends the entered data to the server. The data is encrypted and an integrity check is performed before transmission.

[0699] Step 3:

[0700] The server processes the received diagnostic information using analytical tools and searches for similar cases in a database of past medical cases. This process uses a pattern recognition algorithm to identify the case that most closely matches the patient's symptoms.

[0701] Step 4:

[0702] The server uses a generation mechanism to create multiple treatment plans based on the analysis results. These treatment plans include standard therapies and the latest treatments, and are customized based on the patient's personal information.

[0703] Step 5:

[0704] The server uses an evaluation tool to calculate the suitability of the generated treatment plans. The evaluation criteria include success rates, risks, and past treatment cases.

[0705] Step 6:

[0706] The device monitors the user's emotional state using an emotion engine. This engine analyzes the user's voice tone and language patterns to detect their emotions in real time.

[0707] Step 7:

[0708] The server adjusts the visualization data based on the detected emotions and presents treatment options in a concise and easy-to-understand format. For users experiencing high stress levels, detailed information is simplified, and reassuring support messages are added.

[0709] Step 8:

[0710] The device displays adjusted visualization data to the user. The user refers to this data to understand the merits and demerits of each treatment method and consider the next steps. The visualization data also includes location information and contact details for the selected appropriate medical institutions.

[0711] This system allows users to receive information tailored to their emotional state and effectively consider treatment options.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] A challenge in providing medical information is the lack of personalized information that takes into account the patient's emotional state. In particular, with serious illnesses, excessive information can actually increase patient anxiety, making emotionally sensitive information provision essential.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes equipment for inputting patient diagnostic information and image information, an analysis device for searching for similar information from past case records, and an emotion recognition device for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present appropriate and effective method proposals while taking emotions into consideration.

[0717] "Input equipment" refers to a combination of hardware and software used to import patient diagnostic information and image information into the system.

[0718] An "analytical device" is a device used to search for similar information from past case records and to scientifically evaluate diagnostic information.

[0719] A "formation device" refers to software and hardware used to generate multiple method options based on the results of an analytical device.

[0720] A "judgment device" is a device for quantifying and evaluating the suitability of the generated method options.

[0721] An "emotion recognition device" is software and hardware that analyzes a user's voice or text input to determine their emotional state.

[0722] A "presentation device" is a device for visually displaying the results obtained by the judgment device and the emotion recognition device.

[0723] The following system is configured as an embodiment for carrying out the present invention. This system is designed to enable patients with serious illnesses to quickly obtain a highly accurate second opinion. A specific embodiment will be described below.

[0724] First, the user inputs the patient's diagnostic and imaging information into the terminal. At this stage, basic information such as CT scans, MRI images, age, gender, and medical history is used. This information is entered in an appropriate format and transmitted to the server using the terminal's interface.

[0725] The server uses analytical equipment to analyze the input data. Specifically, it uses digital image processing software to analyze CT and MRI images and detect potential abnormalities. It also searches existing medical case databases for cases similar to the input diagnostic information.

[0726] Next, the server generates multiple treatment options using a forming device. In this process, a generative AI model is utilized to create scientific and rational treatment plans based on past success stories and the latest medical knowledge.

[0727] The generated method proposals are evaluated for suitability by a judgment device. This suitability evaluation is performed using statistical methods and machine learning algorithms, and quantitative indicators are provided.

[0728] In parallel, the server uses an emotion recognition device to detect the user's emotional state in real time from their voice and text input. This allows the server to understand if the user is experiencing high stress or anxiety, and adjust the content of the information presented accordingly.

[0729] Finally, the server visualizes the results generated by the judgment device and emotion recognition device on a presentation device and provides the information to the user's terminal. The information is displayed as graphs and concise text, and reassuring information is added as needed.

[0730] For example, if a user seeks a second opinion on lung cancer, the server will present both standard treatments and the latest immunotherapies. Simultaneously, if the user's anxiety is detected by the emotion recognition system, information providing reassurance, such as treatment success rates and case studies of other patients, will also be displayed. Furthermore, information on nearby medical facilities for initiating treatment will be added.

[0731] An example of a prompt message is: "Based on the diagnostic data entered by the user, provide the most suitable second opinion option. Also, use the emotion engine to present information that will reduce the user's stress."

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The user enters patient diagnostic and imaging information into the terminal. This input data includes CT scans, MRI images, age, gender, and medical history. This data is then formatted and encrypted before being prepared for transmission to the server.

[0735] Step 2:

[0736] The terminal sends the input data to the server. During this process, the data is encrypted using a secure protocol such as SSL / TLS and transmitted to the server over the network. The output is an encrypted data packet.

[0737] Step 3:

[0738] The server receives the data and begins analysis using an analysis device. First, it applies a digital image processing algorithm to extract feature points from CT and MRI images and detect anomalies. The output consists of interpreted digital data and identified anomaly information.

[0739] Step 4:

[0740] The server searches past medical databases based on the analyzed data to identify similar cases. It uses an information retrieval algorithm to generate a list of relevant cases. The output includes metadata and treatment outcome information for similar cases.

[0741] Step 5:

[0742] The server uses a generative AI model to form multiple treatment options based on the data obtained in the previous step. The generating device performs calculations to generate the optimal treatment plan. The output is a list of treatment options.

[0743] Step 6:

[0744] The server evaluates the suitability of treatment plans generated using a judgment device. It calculates the suitability rate for each treatment plan using statistical analysis and machine learning models. The output is suitability rate data for each treatment plan.

[0745] Step 7:

[0746] The server analyzes the user's emotional state using an emotion recognition device. It processes voice and text input from the user using an emotion analysis algorithm to measure stress and anxiety levels. The output is data indicating the user's emotional state.

[0747] Step 8:

[0748] The server adjusts the information presentation on the display device based on the emotion recognition results and generates visualization data. It organizes the information in graph and text formats and prepares it for transmission to the terminal. The output is the adjusted visualization data.

[0749] Step 9:

[0750] The terminal receives pre-configured data from the server and presents the information to the user visually. It displays treatment plans, suitability, and emotionally sensitive messages on the screen, and provides additional support information as needed.

[0751] (Application Example 2)

[0752] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0753] In electronic payments, a challenge exists where information is provided without regard for the user's feelings, failing to alleviate anxiety and hesitation to purchase. Therefore, there is a need for considerate information presentation that takes into account the user's emotional state.

[0754] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0755] In this invention, the server includes an input means for inputting user information, an analysis means for searching for similar cases from a past information case data store based on the information input via the input means, and an emotion recognition means for detecting the user's emotional state and adjusting the information presentation. This makes it possible to present information according to the user's emotional state.

[0756] "User" refers to an individual or legal entity that attempts to obtain information using the system.

[0757] "Input means" refers to the means by which users provide information necessary to the system, and includes digital devices and interfaces.

[0758] An "information case data store" refers to a database system that stores information case studies accumulated in the past.

[0759] "Analysis means" refers to a means for searching and extracting similar cases within a data store based on the input information.

[0760] A "generation means" is a means for generating multiple method proposals based on the results obtained from the analysis means.

[0761] "Evaluation means" refers to a device or software that has the function of calculating the precision of the generated options and providing the results to the user.

[0762] "Emotion recognition means" refers to means for detecting the user's emotional state and adjusting the information presented based on that state.

[0763] "Visualization means" refers to a method or apparatus for presenting information and results obtained by evaluation means and emotion recognition means in an easily viewable manner.

[0764] In this invention, the server analyzes the user's input information and searches for similar cases from a data store of past information and cases. The user inputs information via a smartphone or tablet interface, and the server receives it.

[0765] The server uses analysis tools to quickly search for similar cases in the data store based on the input information. Based on the analysis results, the generation tool formulates multiple methodologies. The precision of the formulated methodologies is calculated by the evaluation tool. Subsequently, the emotion recognition tool monitors the user's emotional state from their voice and facial expressions. Information presentation is adjusted as needed.

[0766] Information is presented according to the user's emotional state through visualization methods, in a format that is easy for the user to understand. For example, for users who are feeling anxious, detailed explanations and relevant reassuring information are selectively displayed with heightened emphasis. It is also possible to generate reassuring language using a generative AI model. For this reason, the prompt message used is, "If a user shows signs of anxiety or hesitation before making a purchase, please suggest ways to present them with information that will reassure them."

[0767] For example, if a user is considering purchasing a new gadget and appears anxious, the server will highlight product reviews and warranty information. In this way, a system is implemented that enables support that takes the user's emotional state into consideration.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] The user uses a terminal to enter the necessary information. This information includes details of the product they are considering purchasing and their basic information. The entered information is then sent from the terminal to the server.

[0771] Step 2:

[0772] The server processes the received information using parsing tools. It searches for similar information examples from a past information example data store and obtains results. The most appropriate information example obtained from the data store is selected based on the context of the input information.

[0773] Step 3:

[0774] The server generates multiple methodologies using a generation mechanism based on the analysis results. The generated methodologies include options related to the user's input information. Each methodology's precision is calculated by an evaluation mechanism. This calculation involves comparing the input information with the characteristics of past cases.

[0775] Step 4:

[0776] The server uses emotion recognition to monitor the user's emotional state from their voice and facial expressions. This allows it to understand the user's current emotions in real time and adjust the information presented as needed. Emotional data is acquired using voice recognition tools and facial expression analysis software.

[0777] Step 5:

[0778] The visualization system visually presents the user with information adjusted based on the results of emotion recognition. This information includes precision calculated by the evaluation system and detailed information designed to provide a sense of security. A graphical user interface is used for visualization.

[0779] Step 6:

[0780] Using a generative AI model, words and sentences designed to instill a sense of security are generated. The prompt, "If a user shows signs of anxiety or hesitation before making a purchase, suggest ways to present them with information that will reassure them," is utilized to generate appropriate messages. The AI's generated results are displayed to the user as final information.

[0781] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0782] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0783] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0784] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0785] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0786] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0787] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0788] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0789] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0790] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0791] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0792] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0793] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0794] 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.

[0795] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0796] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using this memory.

[0797] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0798] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0799] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0800] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0801] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0802] The following is further disclosed regarding the embodiments described above.

[0803] (Claim 1)

[0804] An input means for inputting patient diagnostic information and image data,

[0805] An analysis means that searches for similar cases from a past medical case database based on the data input via the aforementioned input means,

[0806] A generation means that generates multiple treatment plans based on the results obtained by the analysis means,

[0807] An evaluation means for calculating the suitability of the generated treatment options,

[0808] A visualization means for visualizing and presenting the evaluation results obtained by the aforementioned evaluation means,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the evaluation means includes a visualization means for selecting a medical institution related to the generated treatment plan and presenting its location information and contact method.

[0812] (Claim 3)

[0813] The system according to claim 1, wherein the visualization means provides comparative information of the generated treatment plans in graph or tabular format.

[0814] "Example 1"

[0815] (Claim 1)

[0816] Information acquisition means for inputting patient diagnostic information and image data,

[0817] A data verification means that confirms the integrity of the data input via the information acquisition means,

[0818] An information analysis means that searches for similar cases from a past medical case database based on the data that has passed through the aforementioned data verification means,

[0819] A treatment proposal means that generates multiple treatment plans based on the results obtained by the information analysis means,

[0820] A treatment evaluation method for calculating the suitability rate of the generated treatment plan,

[0821] A results display means for visually presenting the evaluation results obtained by the aforementioned treatment evaluation means,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the treatment evaluation means includes a result display means that selects a medical facility related to the generated treatment plan and presents its location information and contact information.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the result display means provides comparative information of the generated treatment plans in the form of a figure or a table.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A data input means for inputting patient diagnostic information and image data,

[0830] A data analysis means that searches for similar cases from a past medical case database based on the information entered via the aforementioned data input means,

[0831] Information generation means that generates multiple treatment strategies based on the results obtained by the data analysis means,

[0832] An analytical evaluation means for calculating the accuracy of the generated treatment strategy,

[0833] Information visualization means for visualizing and presenting the evaluation results obtained by the aforementioned analysis and evaluation means,

[0834] A data encryption transmission means that encrypts and securely transmits the analysis results using the information visualization means,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the analysis and evaluation means includes an information visualization means for selecting medical institutions related to the generated treatment strategy and presenting their location information and contact methods.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the information visualization means provides comparative information of the generated treatment strategies in graph or tabular format.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] Equipment for inputting patient diagnostic information and imaging information,

[0843] An analysis device that searches for similar information from past case records based on data input via the aforementioned equipment,

[0844] A forming apparatus that generates multiple method proposals based on information obtained from the aforementioned analytical apparatus,

[0845] A determination device for calculating the suitability of the generated method options,

[0846] An emotion recognition device that detects the user's emotional state and adjusts the information presentation accordingly,

[0847] A presentation device that visually displays the results obtained from the judgment device and the emotion recognition device,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein the presentation device selects an organization related to the generated method proposal and displays its location information and contact information.

[0851] (Claim 3)

[0852] The system according to claim 1, wherein the emotion recognition device detects emotions from the user's voice or text and adjusts and provides the information.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] An input method for entering user information,

[0856] An analysis means that searches for similar cases from a past information case data store based on the information input via the aforementioned input means,

[0857] A generation means that generates multiple method proposals based on the results obtained by the analysis means,

[0858] An evaluation means for calculating the precision of the generated options,

[0859] An emotion recognition means that detects the user's emotional state and adjusts the information presentation,

[0860] A visualization means for visualizing and presenting the results obtained by the evaluation means and the emotion recognition means,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, wherein the evaluation means includes visualization means for selecting an organization related to the generated method proposal and presenting its location information and contact method.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the visualization means provides comparative information of the generated method proposals in graph or tabular format. [Explanation of symbols]

[0866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means for inputting patient diagnostic information and image data, An analysis means that searches for similar cases from a past medical case database based on the data input via the aforementioned input means, A generation means that generates multiple treatment plans based on the results obtained by the analysis means, An evaluation means for calculating the suitability of the generated treatment options, A visualization means for visualizing and presenting the evaluation results obtained by the aforementioned evaluation means, A system that includes this.

2. The system according to claim 1, wherein the evaluation means includes a visualization means for selecting a medical institution related to the generated treatment plan and presenting its location information and contact method.

3. The system according to claim 1, wherein the visualization means provides comparative information of the generated treatment plans in graph or tabular format.

Images