system

Abstract

Provide a system. A method for storing past treatment experiences in a database; a means for searching the database for treatment experiences; A means for analyzing a question from a user using a natural language processing engine; means for operating the search means based on the analysis results; means for displaying search results from said database to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] Patients suffering from serious illnesses such as cancer want to hear about the treatment experiences of past patients to alleviate their anxiety about treatment, but current methods require human interaction, often requiring them to thank and respond to the person they are consulting. This creates an additional burden for patients who are already short on time and mentally. The goal of this project is to solve this problem and provide an environment where patients can easily learn about treatment experiences. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including means for storing past treatment experiences in a database, means for searching for treatment experiences from the database, means for analyzing questions from users using a natural language processing engine, means for operating the search means based on the results of the analysis, and means for displaying search results from the database to the user, thereby enabling patients to quickly and appropriately obtain the information they need while reducing their mental burden.

[0006] "Treatment experiences" are descriptions of specific events, impressions, and results experienced by patients who have received treatment in the past.

[0007] A "database" is an electronic system for systematically organizing and storing information, and enables data to be searched and extracted based on specific conditions.

[0008] A "natural language processing engine" is a collection of algorithms and programs for understanding and analyzing human language, and processes text data and analyzes its meaning.

[0009] A "user question" is a text-based inquiry that is entered by a person using the system and that requests an answer.

[0010] "Analysis results" are data such as extracted meanings and lists of keywords that are generated by the natural language processing engine after processing the input question.

[0011] A "search tool" is a function or process for finding specific information in a database.

[0012] The "display means" refers to a function or device for visually displaying the searched information to the user. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. Specific embodiments of the system are described below.

[0035] Overall system configuration

[0036] This system consists of a user terminal, a server, and a database. Users input questions using their terminal, and the server analyzes and processes the questions to provide appropriate treatment experience stories.

[0037] Detailed process flow

[0038] 1. User enters a question

[0039] What happens:

[0040] The user enters questions or concerns about the treatment in text format. For example, they might enter, "What are the side effects of radiation therapy?" The question text entered by the user is sent to the server via the device's UI.

[0041] 2. Send the question to the server

[0042] What happens:

[0043] When a user submits a question, the device sends it to the server in JSON format, for example as an HTTP POST request in the following format:

[0044] json

[0045] {

[0046] "question": "What are the side effects of radiation therapy?"

[0047] }

[0048] 3. Analyze the question using natural language processing

[0049] What happens:

[0050] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. Specifically, the keywords "radiation therapy" and "side effects" are extracted.

[0051] 4. Generate appropriate answers based on the analysis results

[0052] What happens:

[0053] The server searches a database based on the analysis results obtained from the natural language processing engine. The database contains past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects."

[0054] 5. Send the answer to the user's device

[0055] What happens:

[0056] Based on the searched data of treatment experiences, the server generates an appropriate answer, which is then sent to the user's device in JSON format.

[0057] 6. Show the answer to the user

[0058] What happens:

[0059] The terminal receives the response from the server and displays it in a user-friendly format, such as "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[0060] Specific examples

[0061] Example 1:

[0062] A user types a question: "What are the side effects of chemotherapy?"

[0063] Process flow:

[0064] The device sends the question to the server.

[0065] The server analyzes "chemotherapy" and "side effects" using a natural language processing engine.

[0066] The database is searched for relevant testimonials and answers such as "Many patients experience nausea and fatigue as side effects of chemotherapy" are generated.

[0067] The answer will be displayed on the device.

[0068] Example 2:

[0069] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[0070] Process flow:

[0071] The device sends the question to the server.

[0072] The server analyzes "after radiation therapy" and "lifestyle advice" using a natural language processing engine.

[0073] The system searches the database for relevant experiences and generates answers such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended."

[0074] The answer will be displayed on the device.

[0075] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[0079] Step 2:

[0080] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[0081] json

[0082] {

[0083] "question": "What are the side effects of radiation therapy?"

[0084] }

[0085] Step 3:

[0086] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0087] Step 4:

[0088] The server searches a database based on the analysis results obtained from the NLP engine. The database stores past treatment experiences and searches for experiences that match the analyzed keywords. For example, the following SQL query is executed:

[0089] sql

[0090] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[0091] Step 5:

[0092] The server retrieves the treatment experiences searched from the database and generates an appropriate answer based on this information. For example, it might generate an answer such as, "Many patients report dry skin and fatigue. In rare cases, they experience nausea and loss of appetite."

[0093] Step 6:

[0094] The server generates a response and sends it back to the user's device in JSON format, for example:

[0095] json

[0096] {

[0097] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0098] }

[0099] Step 7:

[0100] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[0101] Example 1

[0102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] In recent years, the number of patients suffering from serious illnesses such as cancer has been increasing, and they often have various questions and anxieties during their treatment. These patients can obtain valuable information about their treatment by requesting past treatment experiences, but conducting such research manually requires time and effort. Current systems have difficulty properly analyzing questions and providing treatment experiences quickly and accurately. Furthermore, they lack a means to effectively extract appropriate information from large amounts of data, making it difficult to quickly provide users with the information they are looking for. Therefore, there is a need for a system that allows users to easily and quickly obtain treatment experiences.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0105] In this invention, the server includes means for inputting a question from a user as text data, means for converting the input text data into JSON format and transmitting it, means for passing the received question to a natural language processing engine for analysis, means for searching a database based on extracted keywords, means for generating an appropriate answer based on the search results, means for transmitting the generated answer to a user terminal, and means for displaying the transmitted answer to the user, thereby enabling users to easily and quickly obtain treatment experiences.

[0106] "Means for inputting user questions as text data" refers to an interface function that allows the user to input doubts or concerns about treatment as questions in text format.

[0107] "Means for converting input text data into JSON format and sending it" refers to a function that converts the question entered by the user into a data format called JavaScript (registered trademark) Object Notation (JSON) and sends it to the server.

[0108] "Means of passing received questions to a natural language processing engine for analysis" refers to the analysis function in which the server provides the received questions to a natural language processing engine, extracting keywords and understanding the context.

[0109] "Means for searching the database based on extracted keywords" refers to the function of searching for related information and experiences within the database based on keywords obtained from the natural language processing engine.

[0110] "Means for generating appropriate answers based on search results" refers to a function for utilizing database search results to generate appropriate answers to user questions.

[0111] "Means for sending the generated answer to the user terminal" refers to the function of converting the generated answer back into JSON format and sending it to the user terminal.

[0112] "Means for displaying the transmitted answers to the user" refers to a function for the user terminal to analyze the answers received and display them in a format that is easy for the user to understand.

[0113] A "generative AI model" is a model that uses artificial intelligence technology to perform natural language processing, and is used to analyze the intent of a question and extract appropriate keywords.

[0114] A "prompt sentence" refers to an input sentence used to convey questions or instructions to a generative AI model.

[0115] This invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. The system accepts questions from users, analyzes them, and provides appropriate treatment testimonials, thereby alleviating patients' doubts and anxieties. A specific embodiment of this system is described below.

[0116] This system consists of a user's terminal, a server, and a database. The user uses the terminal to input questions about treatment in text format. For example, they might input a question like, "What are the side effects of radiation therapy?"

[0117] When a user enters a question, the device converts the question into JSON format and sends it as an HTTP POST request to the server. The server then passes the received question to a natural language processing engine for analysis. This natural language processing engine uses technologies such as Google® Cloud Natural Language API and Hugging Face's Transformers. Based on keywords extracted through the analysis (e.g., "radiation therapy" and "side effects"), the server queries a database to search for related treatment experiences.

[0118] The database stores past treatment experiences. Relational databases such as MySQL (registered trademark) or PostgreSQL are often used for this. The server generates appropriate answers based on the search results and sends them in JSON format to the terminal. The terminal then analyzes the received answers and displays them in a user-friendly format.

[0119] For example, if a user enters the question "What are the side effects of chemotherapy?", the process is as follows: The device sends the question to the server, which analyzes the question using a natural language processing engine. The keywords "chemotherapy" and "side effects" are extracted, and related treatment experiences are searched for in the database. For example, an answer such as "Many patients experience nausea and fatigue as side effects of chemotherapy" is generated and displayed on the device.

[0120] An example of a prompt sentence would be "Please tell me about the side effects of chemotherapy," which would be input to the generative AI model. This prompt sentence would then be passed to a natural language processing engine for analysis.

[0121] This system allows patients to easily and quickly obtain reliable treatment experiences, reducing anxiety about treatment. It is also easy to use and can be accessed even by users who do not have a lot of medical knowledge.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The user enters a question.

[0125] What happens:

[0126] Using the device interface, users enter questions about treatment in text format, such as "What are the side effects of radiation therapy?"

[0127] input:

[0128] Text questions.

[0129] output:

[0130] Text data captured on the device.

[0131] Specific behavior:

[0132] A user enters a question into a form built with HTML and JavaScript and clicks the "Submit" button.

[0133] Step 2:

[0134] The device converts the question into JSON format and sends it to the server.

[0135] What happens:

[0136] The terminal converts the entered text data into JSON format and sends it to the server using AJAX.

[0137] input:

[0138] Text questions.

[0139] output:

[0140] The question converted to JSON format, and the HTTP POST request.

[0141] Specific behavior:

[0142] JavaScript on the terminal side converts the text data into the following JSON format.

[0143] json

[0144] {

[0145] "question": "What are the side effects of radiation therapy?"

[0146] }

[0147] Step 3:

[0148] The server passes the question to a natural language processing engine for analysis.

[0149] What happens:

[0150] The server parses the received JSON-formatted question data and passes it to a natural language processing engine (e.g., Google Cloud Natural Language API or Hugging Face Transformers).

[0151] input:

[0152] Question data in JSON format.

[0153] output:

[0154] Keywords and their context as analysis results.

[0155] Specific behavior:

[0156] The server-side Python code passes the query to the Google Cloud Natural Language API, which extracts the keywords "radiation therapy" and "side effects" as analysis results.

[0157] Step 4:

[0158] The server searches the database based on the analysis results.

[0159] What happens:

[0160] The server searches a database (e.g., MySQL or PostgreSQL) based on keywords obtained from the natural language processing engine to retrieve relevant treatment experiences.

[0161] input:

[0162] Keywords and their context.

[0163] output:

[0164] Treatment experiences as search results.

[0165] Specific behavior:

[0166] A server-side SQL query searches the database to retrieve treatment experiences related to "radiation therapy" and "side effects."

[0167] Step 5:

[0168] The server generates an appropriate answer based on the search results.

[0169] What happens:

[0170] The server generates appropriate answers to the user's questions based on the treatment experiences obtained from the search results.

[0171] input:

[0172] Search results for treatment experiences.

[0173] output:

[0174] The generated answer.

[0175] Specific behavior:

[0176] Server-side Python code combines the search results to generate answers such as, "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0177] Step 6:

[0178] The server generates a response and sends it to the device in JSON format.

[0179] What happens:

[0180] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0181] input:

[0182] The generated answer.

[0183] output:

[0184] Response data in JSON format.

[0185] Specific behavior:

[0186] The server-side Python code converts the generated response into a JSON format like this:

[0187] json

[0188] {

[0189] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0190] }

[0191] Step 7:

[0192] The terminal analyzes the answer and displays it to the user.

[0193] What happens:

[0194] The terminal analyzes the JSON formatted response data received from the server and displays it in a format that is easy for the user to understand.

[0195] input:

[0196] Response data in JSON format.

[0197] output:

[0198] The final answer that is shown to the user.

[0199] Specific behavior:

[0200] JavaScript on the device analyzes the JSON data and displays the following on the web page: "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[0201] (Application example 1)

[0202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0203] Patients suffering from serious illnesses such as cancer often have concerns and questions about their treatment, but with limited reliable information sources, it is difficult to get appropriate answers.In addition, information about treatment-related products and services is not provided in a centralized manner, making it difficult for patients to find the supplies they need.This creates an issue of increasing mental burden during treatment.

[0204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0205] In this invention, the server includes means for storing past treatment experiences in a database, means for searching for treatment experiences from the database, means for analyzing questions from users using a natural language processing engine, means for operating the search means based on the analysis results, means for displaying search results from the database to the user, and means for presenting product information relevant to the user based on the search results, thereby enabling patients to easily obtain reliable information about treatments and efficiently find related products they need.

[0206] "Past treatment experiences" refers to information about the specific progress, side effects, and life after treatment experienced by patients who have undergone treatment for cancer or other conditions.

[0207] The "database" is an information management system that efficiently stores a vast amount of treatment experiences and related information and allows for quick retrieval.

[0208] A "natural language processing engine" is a technology and its implementation system that analyzes questions entered by users and understands the intent and keywords of the questions.

[0209] "Questions from users" refers to text data entered by patients who have questions or concerns about treatment for cancer or other conditions.

[0210] "Analysis results" refers to information about the intent of the question and keywords obtained when the natural language processing engine analyzes the user's question.

[0211] "Related product information" refers to information about products and services that a patient may need during or after treatment, based on the user's questions and analysis results.

[0212] "Search tools" refer to the technology and its implementation system for searching for appropriate treatment experiences and product information from the database.

[0213] "Display means" refers to the technology and its implementation system for presenting search results obtained from the server on the user's terminal in an easy-to-understand manner.

[0214] This invention is a system that allows patients suffering from serious illnesses such as cancer to obtain treatment experiences and related product information. The system is composed of a user terminal, a server, and a database.

[0215] Overall system configuration

[0216] Users access the system using a smartphone. The server is built with a front-end React Native application and a back-end Flask application, and works in conjunction with a natural language processing engine and database. The database stores past treatment experiences and related product information.

[0217] The primary software used includes natural language processing engines (e.g., SpaCy and BERT-based models), Flask (a Python-based web framework), and React Native (a framework for mobile application development).

[0218] Process Overview

[0219] 1. Enter and submit your question:

[0220] Users enter questions about their treatment in text format on their smartphones, such as "What are the side effects of chemotherapy?", and when they press the send button, the question is sent in JSON format to the backend server.

[0221] 2. Question Analysis:

[0222] The server passes the received question to a natural language processing engine, which extracts keywords and analyzes the context to understand the intent of the question. For example, keywords such as "chemotherapy" and "side effects" are extracted.

[0223] 3. Search for testimonials and product information:

[0224] Based on the analysis results, the server's search means searches a database that stores past treatment experiences and product information related to treatment, and searches for appropriate experiences and product information.

[0225] 4. Generate and display results:

[0226] The server generates an appropriate answer based on the searched treatment experiences and related product information. For example, it generates a specific answer such as, "Many patients experience nausea and fatigue. There are supplements that can reduce side effects." This is then sent to the smartphone device in JSON format. The device receives this and displays it in a format that is easy for the user to understand.

[0227] Specific examples

[0228] 1. Example 1:

[0229] A user types a question: "What are the side effects of chemotherapy?"

[0230] The question is sent to a server, where a natural language processing engine analyzes "chemotherapy" and "side effects."

[0231] The database is searched for related testimonials and product information, and the answer generated is, "Many patients experience nausea and fatigue. There are also supplements available to reduce side effects."

[0232] This response is displayed on the user's terminal.

[0233] 2. Example 2:

[0234] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[0235] The questions are sent to a server, where a natural language processing engine analyzes them into "post-radiation therapy" and "lifestyle advice."

[0236] The database is searched for related testimonials and product information, and the answer generated is, "It is important to get enough rest after radiation therapy. Relaxation products are also recommended."

[0237] This response is displayed on the user's terminal.

[0238] Prompt Sentence Examples

[0239] What are the side effects of chemotherapy?

[0240] I want advice on lifestyle after radiation therapy.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] Users use their smartphone devices to input questions about treatment in text format, such as "What are the side effects of chemotherapy?" The question text is sent from the device by clicking the "Send" button.

[0244] Step 2:

[0245] The device detects the click event of the submit button and converts the entered question into JSON format. For example, this JSON data looks like this:

[0246] json

[0247] {

[0248] "question": "What are the side effects of chemotherapy?"

[0249] }

[0250] The converted JSON data is sent to the backend server via an HTTP POST request.

[0251] Step 3:

[0252] The server analyzes the received HTTP POST request and extracts the question text from the JSON data. The extracted question text is passed to a natural language processing engine. The natural language processing engine analyzes the input question text and extracts keywords and context. For example, the keywords "chemotherapy" and "side effects" are extracted.

[0253] Step 4:

[0254] The server generates a search query for the database based on the analysis results (keywords and context) obtained from the natural language processing engine. Using the generated query, the server searches the database to retrieve relevant treatment experiences and product information.

[0255] Step 5:

[0256] The searched treatment experience stories and related product information are converted into an appropriate format by the server and generated as a JSON response to be provided to the user. This JSON data includes, for example, the following information:

[0257] json

[0258] {

[0259] "answer": "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects."

[0260] }

[0261] The generated JSON data is sent to the user terminal.

[0262] Step 6:

[0263] The device parses the JSON data received from the server and displays the provided answers in an easy-to-read format for the user. For example, a specific answer such as "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects" is displayed on the user interface.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion engine, it is possible to recognize the user's emotions and provide more appropriate information. Specific embodiments of this invention are described below.

[0266] Overall system configuration

[0267] The system consists of a user's device, a server, a database, and an emotion engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[0268] Detailed process flow

[0269] 1. User enters a question

[0270] What happens:

[0271] Users enter their questions and concerns about treatment in text format. For example, a user might enter, "What are the side effects of radiation therapy?" This can be done manually or by voice input. The user presses the "Send" button to send the question.

[0272] 2. Send the question to the server

[0273] What happens:

[0274] When a user submits a question, the device sends the question to the server in JSON format, for example as an HTTP POST request in the following format:

[0275] json

[0276] {

[0277] "question": "What are the side effects of radiation therapy?"

[0278] }

[0279] 3. Analyze the question using natural language processing

[0280] What happens:

[0281] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0282] 4. Recognize user emotions

[0283] What happens:

[0284] The server analyzes the user's emotions using an emotion engine, which determines the user's emotional state based on the context, punctuation, and vocabulary of the input text. For example, it can recognize if the user is feeling anxious or seeking encouragement.

[0285] 5. Generate appropriate answers based on the analysis results

[0286] What happens:

[0287] The server searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and searches for appropriate experiences. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored according to the results of the emotion engine.

[0288] 6. Send the answer to the user's device

[0289] What happens:

[0290] The server generates a response and sends it back to the user's device. The response is in JSON format and includes information that takes into account the user's emotional state. For example, it might say, "Many patients have reported dry skin and fatigue. However, please rest assured that medical staff are always available to assist you."

[0291] 7. Show the answer to the user

[0292] What happens:

[0293] The device parses the response received from the server and displays it in a user-friendly format. The user can then check the response displayed on the device screen and obtain information about treatment.

[0294] Specific examples

[0295] Example 1:

[0296] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which uses a natural language processing engine to analyze the keywords "chemotherapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue as side effects of chemotherapy. However, don't worry, your medical team is always there to support you," is generated and displayed on the user's device.

[0297] Example 2:

[0298] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and an emotion engine recognizes the user's feelings of encouragement. For example, a response such as "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment" is generated and displayed on the user's device.

[0299] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden. Furthermore, by combining it with an emotion engine, support is provided that takes into consideration the user's emotions.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[0303] Step 2:

[0304] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[0305] json

[0306] {

[0307] "question": "What are the side effects of radiation therapy?"

[0308] }

[0309] Step 3:

[0310] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0311] Step 4:

[0312] The server uses an emotion engine to recognize the user's emotion from the text provided by the user. The emotion engine determines the emotion from the context, punctuation, and wording in the text, and analyzes whether the user is feeling anxious.

[0313] Step 5:

[0314] The server searches the database based on the analysis results from the NLP engine and emotion engine. The database stores past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects." An example search query would be:

[0315] sql

[0316] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[0317] Step 6:

[0318] The server retrieves the treatment experiences searched from the database and generates appropriate answers based on this information. Based on the analysis results of the emotion engine, the answer is adjusted to take into account the user's emotional state. For example, the answer might be something like, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to help."

[0319] Step 7:

[0320] The server generates a response and sends it back to the user's device. The response is sent in JSON format. For example, it might look something like this:

[0321] json

[0322] {

[0323] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[0324] }

[0325] Step 8:

[0326] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] When patients are facing a serious illness such as cancer, they can obtain information about treatment by searching the internet or consulting with a medical institution. However, these methods make it difficult to obtain information that is tailored to the patient's specific questions and emotional state. Furthermore, psychological support is often insufficient. Therefore, there is a need for a system that can reduce the anxiety and stress felt by patients and provide more appropriate information tailored to each individual's situation.

[0330] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0331] In this invention, the server includes a means for storing past treatment experiences in a database, a means for analyzing questions from users using a natural language processing engine, and a means for recognizing the emotional state of users using an emotion analysis engine, which makes it possible to provide appropriate answers and encouraging messages according to the emotional state of the user when the patient seeks information on a specific question.

[0332] "Past treatment experiences" are recorded information about the experiences, progress, and results of patients who have received treatment.

[0333] A "database" is a system for efficiently searching, storing, managing, and updating information.

[0334] A "natural language processing engine" is a program that analyzes the language that humans use on a daily basis and understands its meaning.

[0335] "Questions from users" are text messages entered by users of the system expressing specific questions or concerns about treatment.

[0336] An "emotion analysis engine" is a program that determines a user's emotional state (e.g., anxiety, joy, sadness, etc.) from text data and voice data and identifies those emotions.

[0337] A "search means" is a function or program for finding desired information from a database.

[0338] "Adjustment" refers to the functions or processes that modify or optimize the search results based on the user's emotional state.

[0339] "Means for displaying messages" refers to functions and interfaces for presenting search results and adjusted information so that users can visually confirm them.

[0340] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion analysis engine, it is possible to recognize the emotions of users and provide more appropriate information. Specific embodiments of this invention are described below.

[0341] Overall system configuration

[0342] The system consists of a user's device, a server, a database, and an emotion analysis engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion analysis engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[0343] Hardware and software used

[0344] User devices: PCs, smartphones, tablets, etc.

[0345] Server: High-performance computer (such as a cloud server)

[0346] Database: MySQL, MongoDB, etc.

[0347] Natural language processing engines: spaCy, Google NLP API, etc.

[0348] Sentiment analysis engine: IBM Watson (registered trademark) Tone Analyzer, etc.

[0349] Data processing and calculation

[0350] 1. The user enters a question

[0351] The user uses the device to enter a question about treatment in text format, for example, "What are the side effects of radiation therapy?" Voice input is also possible.

[0352] 2. Send the question to the server

[0353] The device converts the question entered by the user into JSON format and sends it to the server. For example, the following HTTP POST request is sent:

[0354] json

[0355] {

[0356] "question": "What are the side effects of radiation therapy?"

[0357] }

[0358] 3. Analyze the question using natural language processing

[0359] The server passes the received question to a natural language processing engine for analysis. During the analysis process, keywords are extracted to understand the intent of the question, and the keywords "radiation therapy" and "side effects" are identified.

[0360] 4. Recognize user emotions

[0361] The server uses a sentiment analysis engine to analyze the user's emotional state, determining their feelings from the context of the text, punctuation, and vocabulary, and recognizing emotions such as anxiety or worry.

[0362] 5. Generate appropriate answers based on the analysis results

[0363] The server searches a database based on keywords obtained from a natural language processing engine and emotional states obtained from an emotion analysis engine, extracts relevant information from a database storing past treatment experiences, and generates an answer that takes emotions into consideration.

[0364] 6. Send the answer to the user's device

[0365] The server generates a response and sends it to the user's device in JSON format, such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to help."

[0366] 7. Show the answer to the user

[0367] The terminal converts the answers received from the server into a display format and provides it to the user. The user can check the answers displayed on the screen and obtain information about treatment.

[0368] Specific examples

[0369] Example 1:

[0370] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which then uses a natural language processing engine to analyze "chemotherapy" and "side effects." The sentiment analysis engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue, but don't worry, your medical team is always here to support you" is generated and displayed on the user's device.

[0371] Example 2:

[0372] A user inputs a question such as, "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and the sentiment analysis engine recognizes that the user is seeking encouragement. For example, a response such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment," is generated and displayed on the user's device.

[0373] This allows users to obtain the necessary treatment information while reducing their mental burden. By combining it with an emotion analysis engine, it is possible to provide support that takes into consideration the user's emotions.

[0374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0375] Step 1:

[0376] The user inputs a question into the terminal. The user inputs questions or concerns about treatment in text format and presses the send button. Specific actions include the user typing "What are the side effects of radiation therapy?" into the keyboard. The input is text data.

[0377] Step 2:

[0378] The device sends the question to the server. The device converts the question entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, the following JSON data is sent to the server:

[0379] json

[0380] {

[0381] "question": "What are the side effects of radiation therapy?"

[0382] }

[0383] The input is the user question and the output is JSON data.

[0384] Step 3:

[0385] The server passes the question to a natural language processing engine for analysis. The server analyzes the received JSON data and extracts keywords to understand the intent of the question. Specifically, the server uses a natural language processing engine (e.g., spaCy) to extract the keywords "radiation therapy" and "side effects." The input is JSON data, and the output is a list of keywords.

[0386] Step 4:

[0387] The server passes the text to a sentiment analysis engine to recognize the user's emotional state. The server uses keywords to pass the text entered by the user to a sentiment analysis engine (for example, IBM Watson Tone Analyzer) to analyze the emotional state. Specifically, emotions such as anxiety or worry are identified from the context and punctuation of the text. The input is the user's question text, and the output is information about the emotional state.

[0388] Step 5:

[0389] The server searches the database based on the analysis results and generates an appropriate answer. The server searches the database using keywords obtained from the natural language processing engine and the emotional state obtained from the sentiment analysis engine. Specifically, it extracts personal stories related to "radiation therapy" and "side effects" from the database (for example, MySQL) and generates an answer that takes emotions into consideration. The input is a list of keywords and emotional state information, and the output is a tailored answer.

[0390] Step 6:

[0391] The server sends the generated response to the user's device. The server converts the generated response into JSON format and sends it to the user's device as an HTTP response. Specifically, the following JSON data is sent to the device:

[0392] json

[0393] {

[0394] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[0395] }

[0396] The input is the tailored answer and the output is JSON data.

[0397] Step 7:

[0398] The device displays the answer received from the server. The device parses the received JSON data and displays it in a format that is easy for the user to understand. Specifically, it converts it into HTML or app UI components and displays it on the user's screen. The input is the JSON data received from the server, and the output is information that can be displayed visually. The user can check this displayed answer and obtain the information they need.

[0399] (Application example 2)

[0400] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0401] Patients suffering from serious illnesses such as cancer are prone to anxiety and stress when collecting treatment-related stories and information. Furthermore, when the information provided is general, it may not be tailored to the individual patient's feelings and circumstances, making it difficult to provide appropriate support. Furthermore, because traditional methods of collecting treatment information do not include psychological support, even if patients obtain the necessary information, it is difficult to alleviate the psychological burden. To address these issues, a system is needed that provides information on treatment stories while also providing an interface that takes patients' feelings into consideration.

[0402] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0403] In this invention, the server includes means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing user emotions using an emotion engine, means for generating answers using a generative AI model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display, thereby enabling the provision of specific, emotion-sensitive information about treatment.

[0404] The "database" is an information management system that systematically stores past treatment experiences and allows them to be quickly searched and retrieved as needed.

[0405] A "natural language processing engine" is a technology that analyzes text data entered by a user to extract keywords and understand the context.

[0406] An "emotion engine" is a technology that analyzes the user's emotional state from input data and generates an appropriate response.

[0407] A "generative artificial intelligence model" is an artificial intelligence technology that automatically generates text and information based on input data.

[0408] A "head-mounted display (HMD)" is a display device worn on the head, and is hardware that displays a virtual space to the user.

[0409] A "virtual space" is a virtual three-dimensional space generated by a computer that users can experience interactively.

[0410] A system for carrying out this invention includes the following elements: means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing the user's emotions using an emotion engine, means for generating answers using a generative artificial intelligence model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display.

[0411] First, the user puts on a head-mounted display (HMD) and enters a virtual cafe-like environment. When the user enters questions or concerns about treatment in text format, the questions are sent from the HMD to the server. The submitted questions are passed to the server as an HTTP POST request.

[0412] The server first analyzes the received question using a natural language processing engine. The natural language processing engine extracts keywords from the question and understands its intent. Next, the server uses an emotion engine to analyze the user's emotional state from the input text data. This analysis determines whether the user is anxious or seeking encouragement.

[0413] The server then searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and can search for relevant experiences and advice. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored to the results of the emotion engine.

[0414] Finally, the server generates a response that is sent to the user's head-mounted display and displayed in the virtual space. This allows the user to obtain information about treatment in a relaxing environment that feels like being in a real cafe. Furthermore, by combining it with an emotion engine, support that takes the user's emotions into consideration is also possible.

[0415] As a concrete example, consider the case where a user inputs the question, "What are the side effects of radiation therapy?" The server analyzes this question and extracts the keywords "radiation therapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. The answer generated is, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to support you," and is displayed on the user's head-mounted display.

[0416] Example prompt sentence:

[0417] "What are the side effects of radiation therapy?"

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] The user puts on a head-mounted display and enters a virtual space (virtual cafe). They input specific questions or concerns in text format. The input data includes questions typed by the user, such as "What are the side effects of radiation therapy?" After completing the input, the user presses the "Submit" button. The output is the question data in text format.

[0421] Step 2:

[0422] The device sends the question entered by the user to the server as an HTTP POST request. The entered question is converted to JSON format and sent to the server. The input is the user's question data, and the output is an HTTP request to the server. For example, a JSON-formatted request would look like this: { "question": "What are the side effects of radiation therapy?"}

[0423] Step 3:

[0424] The server passes the received question data to a natural language processing engine (NLP engine) to analyze the question. First, it extracts keywords from the question, and then performs context understanding. For example, it extracts the keywords "radiation therapy" and "side effects" from the question. The input is the question data in the HTTP POST request, and the output is the analyzed keywords.

[0425] Step 4:

[0426] The server uses an emotion engine to analyze the user's emotional state from their input. The input is the question data itself, but the emotion engine analyzes the context, wording, and punctuation to determine the user's emotional state. For example, the emotion "anxiety" may be recognized. The input is the question text, and the output is the emotional state (e.g., "anxious").

[0427] Step 5:

[0428] The server searches the database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. It searches the database of past treatment experiences and extracts the most relevant experiences. The input is keywords and emotional states, and the output is the relevant treatment experience.

[0429] Step 6:

[0430] The server generates an appropriate answer using a generative artificial intelligence model (generative AI model) based on the analyzed keywords and emotional state. For example, it generates an answer such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to provide support." The input is the relevant treatment experience and emotional state, and the output is the tailored answer text.

[0431] Step 7:

[0432] The server converts the generated answer into JSON format and sends it to the user's head-mounted display. The input is the generated answer text, and the output is the HTTP response sent to the device.

[0433] Step 8:

[0434] The device parses the received answer and displays it in the virtual space via the user's head-mounted display. For example, the user can view the answer while relaxing in the virtual space. The input is the answer data from the server, and the output is the answer displayed on the HMD display screen.

[0435] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0436] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0437] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0438] [Second embodiment]

[0439] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0440] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0441] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0442] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0443] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0444] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0445] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0446] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0447] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0448] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0449] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0450] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0451] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. Specific embodiments of the system are described below.

[0452] Overall system configuration

[0453] This system consists of a user terminal, a server, and a database. Users input questions using their terminal, and the server analyzes and processes the questions to provide appropriate treatment experience stories.

[0454] Detailed process flow

[0455] 1. User enters a question

[0456] What happens:

[0457] The user enters questions or concerns about the treatment in text format. For example, they might enter, "What are the side effects of radiation therapy?" The question text entered by the user is sent to the server via the device's UI.

[0458] 2. Send the question to the server

[0459] What happens:

[0460] When a user submits a question, the device sends it to the server in JSON format, for example as an HTTP POST request in the following format:

[0461] json

[0462] {

[0463] "question": "What are the side effects of radiation therapy?"

[0464] }

[0465] 3. Analyze the question using natural language processing

[0466] What happens:

[0467] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. Specifically, the keywords "radiation therapy" and "side effects" are extracted.

[0468] 4. Generate appropriate answers based on the analysis results

[0469] What happens:

[0470] The server searches a database based on the analysis results obtained from the natural language processing engine. The database contains past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects."

[0471] 5. Send the answer to the user's device

[0472] What happens:

[0473] Based on the searched data of treatment experiences, the server generates an appropriate answer, which is then sent to the user's device in JSON format.

[0474] 6. Show the answer to the user

[0475] What happens:

[0476] The terminal receives the response from the server and displays it in a user-friendly format, such as "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[0477] Specific examples

[0478] Example 1:

[0479] A user types a question: "What are the side effects of chemotherapy?"

[0480] Process flow:

[0481] The device sends the question to the server.

[0482] The server analyzes "chemotherapy" and "side effects" using a natural language processing engine.

[0483] The database is searched for relevant testimonials and answers such as "Many patients experience nausea and fatigue as side effects of chemotherapy" are generated.

[0484] The answer will be displayed on the device.

[0485] Example 2:

[0486] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[0487] Process flow:

[0488] The device sends the question to the server.

[0489] The server analyzes "after radiation therapy" and "lifestyle advice" using a natural language processing engine.

[0490] The system searches the database for relevant experiences and generates answers such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended."

[0491] The answer will be displayed on the device.

[0492] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden.

[0493] The processing flow will be explained below.

[0494] Step 1:

[0495] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[0496] Step 2:

[0497] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[0498] json

[0499] {

[0500] "question": "What are the side effects of radiation therapy?"

[0501] }

[0502] Step 3:

[0503] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0504] Step 4:

[0505] The server searches a database based on the analysis results obtained from the NLP engine. The database stores past treatment experiences and searches for experiences that match the analyzed keywords. For example, the following SQL query is executed:

[0506] sql

[0507] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[0508] Step 5:

[0509] The server retrieves the treatment experiences searched from the database and generates an appropriate answer based on this information. For example, it might generate an answer such as, "Many patients report dry skin and fatigue. In rare cases, they experience nausea and loss of appetite."

[0510] Step 6:

[0511] The server generates a response and sends it back to the user's device in JSON format, for example:

[0512] json

[0513] {

[0514] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0515] }

[0516] Step 7:

[0517] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[0518] Example 1

[0519] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0520] In recent years, the number of patients suffering from serious illnesses such as cancer has been increasing, and they often have various questions and anxieties during their treatment. These patients can obtain valuable information about their treatment by requesting past treatment experiences, but conducting such research manually requires time and effort. Current systems have difficulty properly analyzing questions and providing treatment experiences quickly and accurately. Furthermore, they lack a means to effectively extract appropriate information from large amounts of data, making it difficult to quickly provide users with the information they are looking for. Therefore, there is a need for a system that allows users to easily and quickly obtain treatment experiences.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0522] In this invention, the server includes means for inputting a question from a user as text data, means for converting the input text data into JSON format and transmitting it, means for passing the received question to a natural language processing engine for analysis, means for searching a database based on extracted keywords, means for generating an appropriate answer based on the search results, means for transmitting the generated answer to a user terminal, and means for displaying the transmitted answer to the user, thereby enabling users to easily and quickly obtain treatment experiences.

[0523] "Means for inputting user questions as text data" refers to an interface function that allows the user to input doubts or concerns about treatment as questions in text format.

[0524] "Means for converting input text data into JSON format and sending it" refers to the function of converting the question entered by the user into a data format called JavaScript Object Notation (JSON) and sending it to the server.

[0525] "Means of passing received questions to a natural language processing engine for analysis" refers to the analysis function in which the server provides the received questions to a natural language processing engine, extracting keywords and understanding the context.

[0526] "Means for searching the database based on extracted keywords" refers to the function of searching for related information and experiences within the database based on keywords obtained from the natural language processing engine.

[0527] "Means for generating appropriate answers based on search results" refers to a function for utilizing database search results to generate appropriate answers to user questions.

[0528] "Means for sending the generated answer to the user terminal" refers to the function of converting the generated answer back into JSON format and sending it to the user terminal.

[0529] "Means for displaying the transmitted answers to the user" refers to a function for the user terminal to analyze the answers received and display them in a format that is easy for the user to understand.

[0530] A "generative AI model" is a model that uses artificial intelligence technology to perform natural language processing, and is used to analyze the intent of a question and extract appropriate keywords.

[0531] A "prompt sentence" refers to an input sentence used to convey questions or instructions to a generative AI model.

[0532] This invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. The system accepts questions from users, analyzes them, and provides appropriate treatment testimonials, thereby alleviating patients' doubts and anxieties. A specific embodiment of this system is described below.

[0533] This system consists of a user's terminal, a server, and a database. The user uses the terminal to input questions about treatment in text format. For example, they might input a question like, "What are the side effects of radiation therapy?"

[0534] When a user enters a question, the device converts it into JSON format and sends it as an HTTP POST request to the server. The server then passes the received question to a natural language processing engine for analysis. This natural language processing engine uses Google Cloud Natural Language API and Hugging Face's Transformers. Based on keywords extracted through the analysis (e.g., "radiation therapy" and "side effects"), the server queries a database to search for related treatment experiences.

[0535] The database stores past treatment experiences. It is often a relational database such as MySQL or PostgreSQL. The server generates appropriate answers based on the search results and sends them to the device in JSON format. The device then analyzes the received answers and displays them in a user-friendly format.

[0536] For example, if a user enters the question "What are the side effects of chemotherapy?", the process is as follows: The device sends the question to the server, which analyzes the question using a natural language processing engine. The keywords "chemotherapy" and "side effects" are extracted, and related treatment experiences are searched for in the database. For example, an answer such as "Many patients experience nausea and fatigue as side effects of chemotherapy" is generated and displayed on the device.

[0537] An example of a prompt sentence would be "Please tell me about the side effects of chemotherapy," which would be input to the generative AI model. This prompt sentence would then be passed to a natural language processing engine for analysis.

[0538] This system allows patients to easily and quickly obtain reliable treatment experiences, reducing anxiety about treatment. It is also easy to use and can be accessed even by users who do not have a lot of medical knowledge.

[0539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0540] Step 1:

[0541] The user enters a question.

[0542] What happens:

[0543] Using the device interface, users enter questions about treatment in text format, such as "What are the side effects of radiation therapy?"

[0544] input:

[0545] Text questions.

[0546] output:

[0547] Text data captured on the device.

[0548] Specific behavior:

[0549] A user enters a question into a form built with HTML and JavaScript and clicks the "Submit" button.

[0550] Step 2:

[0551] The device converts the question into JSON format and sends it to the server.

[0552] What happens:

[0553] The terminal converts the entered text data into JSON format and sends it to the server using AJAX.

[0554] input:

[0555] Text questions.

[0556] output:

[0557] The question converted to JSON format, and the HTTP POST request.

[0558] Specific behavior:

[0559] JavaScript on the terminal side converts the text data into the following JSON format.

[0560] json

[0561] {

[0562] "question": "What are the side effects of radiation therapy?"

[0563] }

[0564] Step 3:

[0565] The server passes the question to a natural language processing engine for analysis.

[0566] What happens:

[0567] The server parses the received JSON-formatted question data and passes it to a natural language processing engine (e.g., Google Cloud Natural Language API or Hugging Face Transformers).

[0568] input:

[0569] Question data in JSON format.

[0570] output:

[0571] Keywords and their context as analysis results.

[0572] Specific behavior:

[0573] The server-side Python code passes the query to the Google Cloud Natural Language API, which extracts the keywords "radiation therapy" and "side effects" as analysis results.

[0574] Step 4:

[0575] The server searches the database based on the analysis results.

[0576] What happens:

[0577] The server searches a database (e.g., MySQL or PostgreSQL) based on keywords obtained from the natural language processing engine to retrieve relevant treatment experiences.

[0578] input:

[0579] Keywords and their context.

[0580] output:

[0581] Treatment experiences as search results.

[0582] Specific behavior:

[0583] A server-side SQL query searches the database to retrieve treatment experiences related to "radiation therapy" and "side effects."

[0584] Step 5:

[0585] The server generates an appropriate answer based on the search results.

[0586] What happens:

[0587] The server generates appropriate answers to the user's questions based on the treatment experiences obtained from the search results.

[0588] input:

[0589] Search results for treatment experiences.

[0590] output:

[0591] The generated answer.

[0592] Specific behavior:

[0593] Server-side Python code combines the search results to generate answers such as, "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0594] Step 6:

[0595] The server generates a response and sends it to the device in JSON format.

[0596] What happens:

[0597] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[0598] input:

[0599] The generated answer.

[0600] output:

[0601] Response data in JSON format.

[0602] Specific behavior:

[0603] The server-side Python code converts the generated response into a JSON format like this:

[0604] json

[0605] {

[0606] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0607] }

[0608] Step 7:

[0609] The terminal analyzes the answer and displays it to the user.

[0610] What happens:

[0611] The terminal analyzes the JSON formatted response data received from the server and displays it in a format that is easy for the user to understand.

[0612] input:

[0613] Response data in JSON format.

[0614] output:

[0615] The final answer that is shown to the user.

[0616] Specific behavior:

[0617] JavaScript on the device analyzes the JSON data and displays the following on the web page: "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[0618] (Application example 1)

[0619] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0620] Patients suffering from serious illnesses such as cancer often have concerns and questions about their treatment, but with limited reliable information sources, it is difficult to get appropriate answers.In addition, information about treatment-related products and services is not provided in a centralized manner, making it difficult for patients to find the supplies they need.This creates an issue of increasing mental burden during treatment.

[0621] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0622] In this invention, the server includes means for storing past treatment experiences in a database, means for searching for treatment experiences from the database, means for analyzing questions from users using a natural language processing engine, means for operating the search means based on the analysis results, means for displaying search results from the database to the user, and means for presenting product information relevant to the user based on the search results, thereby enabling patients to easily obtain reliable information about treatments and efficiently find related products they need.

[0623] "Past treatment experiences" refers to information about the specific progress, side effects, and life after treatment experienced by patients who have undergone treatment for cancer or other conditions.

[0624] The "database" is an information management system that efficiently stores a vast amount of treatment experiences and related information and allows for quick retrieval.

[0625] A "natural language processing engine" is a technology and its implementation system that analyzes questions entered by users and understands the intent and keywords of the questions.

[0626] "Questions from users" refers to text data entered by patients who have questions or concerns about treatment for cancer or other conditions.

[0627] "Analysis results" refers to information about the intent of the question and keywords obtained when the natural language processing engine analyzes the user's question.

[0628] "Related product information" refers to information about products and services that a patient may need during or after treatment, based on the user's questions and analysis results.

[0629] "Search tools" refer to the technology and its implementation system for searching for appropriate treatment experiences and product information from the database.

[0630] "Display means" refers to the technology and its implementation system for presenting search results obtained from the server on the user's terminal in an easy-to-understand manner.

[0631] This invention is a system that allows patients suffering from serious illnesses such as cancer to obtain treatment experiences and related product information. The system is composed of a user terminal, a server, and a database.

[0632] Overall system configuration

[0633] Users access the system using a smartphone. The server is built with a front-end React Native application and a back-end Flask application, and works in conjunction with a natural language processing engine and database. The database stores past treatment experiences and related product information.

[0634] The primary software used includes natural language processing engines (e.g., SpaCy and BERT-based models), Flask (a Python-based web framework), and React Native (a framework for mobile application development).

[0635] Process Overview

[0636] 1. Enter and submit your question:

[0637] Users enter questions about their treatment in text format on their smartphones, such as "What are the side effects of chemotherapy?", and when they press the send button, the question is sent in JSON format to the backend server.

[0638] 2. Question Analysis:

[0639] The server passes the received question to a natural language processing engine, which extracts keywords and analyzes the context to understand the intent of the question. For example, keywords such as "chemotherapy" and "side effects" are extracted.

[0640] 3. Search for testimonials and product information:

[0641] Based on the analysis results, the server's search means searches a database that stores past treatment experiences and product information related to treatment, and searches for appropriate experiences and product information.

[0642] 4. Generate and display results:

[0643] The server generates an appropriate answer based on the searched treatment experiences and related product information. For example, it generates a specific answer such as, "Many patients experience nausea and fatigue. There are supplements that can reduce side effects." This is then sent to the smartphone device in JSON format. The device receives this and displays it in a format that is easy for the user to understand.

[0644] Specific examples

[0645] 1. Example 1:

[0646] A user types a question: "What are the side effects of chemotherapy?"

[0647] The question is sent to a server, where a natural language processing engine analyzes "chemotherapy" and "side effects."

[0648] The database is searched for related testimonials and product information, and the answer generated is, "Many patients experience nausea and fatigue. There are also supplements available to reduce side effects."

[0649] This response is displayed on the user's terminal.

[0650] 2. Example 2:

[0651] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[0652] The questions are sent to a server, where a natural language processing engine analyzes them into "post-radiation therapy" and "lifestyle advice."

[0653] The database is searched for related testimonials and product information, and the answer generated is, "It is important to get enough rest after radiation therapy. Relaxation products are also recommended."

[0654] This response is displayed on the user's terminal.

[0655] Prompt Sentence Examples

[0656] What are the side effects of chemotherapy?

[0657] I want advice on lifestyle after radiation therapy.

[0658] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0659] Step 1:

[0660] Users use their smartphone devices to input questions about treatment in text format, such as "What are the side effects of chemotherapy?" The question text is sent from the device by clicking the "Send" button.

[0661] Step 2:

[0662] The device detects the click event of the submit button and converts the entered question into JSON format. For example, this JSON data looks like this:

[0663] json

[0664] {

[0665] "question": "What are the side effects of chemotherapy?"

[0666] }

[0667] The converted JSON data is sent to the backend server via an HTTP POST request.

[0668] Step 3:

[0669] The server analyzes the received HTTP POST request and extracts the question text from the JSON data. The extracted question text is passed to a natural language processing engine. The natural language processing engine analyzes the input question text and extracts keywords and context. For example, the keywords "chemotherapy" and "side effects" are extracted.

[0670] Step 4:

[0671] The server generates a search query for the database based on the analysis results (keywords and context) obtained from the natural language processing engine. Using the generated query, the server searches the database to retrieve relevant treatment experiences and product information.

[0672] Step 5:

[0673] The searched treatment experience stories and related product information are converted into an appropriate format by the server and generated as a JSON response to be provided to the user. This JSON data includes, for example, the following information:

[0674] json

[0675] {

[0676] "answer": "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects."

[0677] }

[0678] The generated JSON data is sent to the user terminal.

[0679] Step 6:

[0680] The device parses the JSON data received from the server and displays the provided answers in an easy-to-read format for the user. For example, a specific answer such as "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects" is displayed on the user interface.

[0681] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0682] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion engine, it is possible to recognize the user's emotions and provide more appropriate information. Specific embodiments of this invention are described below.

[0683] Overall system configuration

[0684] The system consists of a user's device, a server, a database, and an emotion engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[0685] Detailed process flow

[0686] 1. User enters a question

[0687] What happens:

[0688] Users enter their questions and concerns about treatment in text format. For example, a user might enter, "What are the side effects of radiation therapy?" This can be done manually or by voice input. The user presses the "Send" button to send the question.

[0689] 2. Send the question to the server

[0690] What happens:

[0691] When a user submits a question, the device sends the question to the server in JSON format, for example as an HTTP POST request in the following format:

[0692] json

[0693] {

[0694] "question": "What are the side effects of radiation therapy?"

[0695] }

[0696] 3. Analyze the question using natural language processing

[0697] What happens:

[0698] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0699] 4. Recognize user emotions

[0700] What happens:

[0701] The server analyzes the user's emotions using an emotion engine, which determines the user's emotional state based on the context, punctuation, and vocabulary of the input text. For example, it can recognize if the user is feeling anxious or seeking encouragement.

[0702] 5. Generate appropriate answers based on the analysis results

[0703] What happens:

[0704] The server searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and searches for appropriate experiences. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored according to the results of the emotion engine.

[0705] 6. Send the answer to the user's device

[0706] What happens:

[0707] The server generates a response and sends it back to the user's device. The response is in JSON format and includes information that takes into account the user's emotional state. For example, it might say, "Many patients have reported dry skin and fatigue. However, please rest assured that medical staff are always available to assist you."

[0708] 7. Show the answer to the user

[0709] What happens:

[0710] The device parses the response received from the server and displays it in a user-friendly format. The user can then check the response displayed on the device screen and obtain information about treatment.

[0711] Specific examples

[0712] Example 1:

[0713] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which uses a natural language processing engine to analyze the keywords "chemotherapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue as side effects of chemotherapy. However, don't worry, your medical team is always there to support you," is generated and displayed on the user's device.

[0714] Example 2:

[0715] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and an emotion engine recognizes the user's feelings of encouragement. For example, a response such as "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment" is generated and displayed on the user's device.

[0716] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden. Furthermore, by combining it with an emotion engine, support is provided that takes into consideration the user's emotions.

[0717] The processing flow will be explained below.

[0718] Step 1:

[0719] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[0720] Step 2:

[0721] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[0722] json

[0723] {

[0724] "question": "What are the side effects of radiation therapy?"

[0725] }

[0726] Step 3:

[0727] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0728] Step 4:

[0729] The server uses an emotion engine to recognize the user's emotion from the text provided by the user. The emotion engine determines the emotion from the context, punctuation, and wording in the text, and analyzes whether the user is feeling anxious.

[0730] Step 5:

[0731] The server searches the database based on the analysis results from the NLP engine and emotion engine. The database stores past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects." An example search query would be:

[0732] sql

[0733] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[0734] Step 6:

[0735] The server retrieves the treatment experiences searched from the database and generates appropriate answers based on this information. Based on the analysis results of the emotion engine, the answer is adjusted to take into account the user's emotional state. For example, the answer might be something like, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to help."

[0736] Step 7:

[0737] The server generates a response and sends it back to the user's device. The response is sent in JSON format. For example, it might look something like this:

[0738] json

[0739] {

[0740] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[0741] }

[0742] Step 8:

[0743] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[0744] Example 2

[0745] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0746] When patients are facing a serious illness such as cancer, they can obtain information about treatment by searching the internet or consulting with a medical institution. However, these methods make it difficult to obtain information that is tailored to the patient's specific questions and emotional state. Furthermore, psychological support is often insufficient. Therefore, there is a need for a system that can reduce the anxiety and stress felt by patients and provide more appropriate information tailored to each individual's situation.

[0747] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0748] In this invention, the server includes a means for storing past treatment experiences in a database, a means for analyzing questions from users using a natural language processing engine, and a means for recognizing the emotional state of users using an emotion analysis engine, which makes it possible to provide appropriate answers and encouraging messages according to the emotional state of the user when the patient seeks information on a specific question.

[0749] "Past treatment experiences" are recorded information about the experiences, progress, and results of patients who have received treatment.

[0750] A "database" is a system for efficiently searching, storing, managing, and updating information.

[0751] A "natural language processing engine" is a program that analyzes the language that humans use on a daily basis and understands its meaning.

[0752] "Questions from users" are text messages entered by users of the system expressing specific questions or concerns about treatment.

[0753] An "emotion analysis engine" is a program that determines a user's emotional state (e.g., anxiety, joy, sadness, etc.) from text data and voice data and identifies those emotions.

[0754] A "search means" is a function or program for finding desired information from a database.

[0755] "Adjustment" refers to the functions or processes that modify or optimize the search results based on the user's emotional state.

[0756] "Means for displaying messages" refers to functions and interfaces for presenting search results and adjusted information so that users can visually confirm them.

[0757] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion analysis engine, it is possible to recognize the emotions of users and provide more appropriate information. Specific embodiments of this invention are described below.

[0758] Overall system configuration

[0759] The system consists of a user's device, a server, a database, and an emotion analysis engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion analysis engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[0760] Hardware and software used

[0761] User devices: PCs, smartphones, tablets, etc.

[0762] Server: High-performance computer (such as a cloud server)

[0763] Database: MySQL, MongoDB, etc.

[0764] Natural language processing engines: spaCy, Google NLP API, etc.

[0765] Sentiment analysis engine: IBM Watson Tone Analyzer, etc.

[0766] Data processing and calculation

[0767] 1. The user enters a question

[0768] The user uses the device to enter a question about treatment in text format, for example, "What are the side effects of radiation therapy?" Voice input is also possible.

[0769] 2. Send the question to the server

[0770] The device converts the question entered by the user into JSON format and sends it to the server. For example, the following HTTP POST request is sent:

[0771] json

[0772] {

[0773] "question": "What are the side effects of radiation therapy?"

[0774] }

[0775] 3. Analyze the question using natural language processing

[0776] The server passes the received question to a natural language processing engine for analysis. During the analysis process, keywords are extracted to understand the intent of the question, and the keywords "radiation therapy" and "side effects" are identified.

[0777] 4. Recognize user emotions

[0778] The server uses a sentiment analysis engine to analyze the user's emotional state, determining their feelings from the context of the text, punctuation, and vocabulary, and recognizing emotions such as anxiety or worry.

[0779] 5. Generate appropriate answers based on the analysis results

[0780] The server searches a database based on keywords obtained from a natural language processing engine and emotional states obtained from an emotion analysis engine, extracts relevant information from a database storing past treatment experiences, and generates an answer that takes emotions into consideration.

[0781] 6. Send the answer to the user's device

[0782] The server generates a response and sends it to the user's device in JSON format, such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to help."

[0783] 7. Show the answer to the user

[0784] The terminal converts the answers received from the server into a display format and provides it to the user. The user can check the answers displayed on the screen and obtain information about treatment.

[0785] Specific examples

[0786] Example 1:

[0787] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which then uses a natural language processing engine to analyze "chemotherapy" and "side effects." The sentiment analysis engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue, but don't worry, your medical team is always here to support you" is generated and displayed on the user's device.

[0788] Example 2:

[0789] A user inputs a question such as, "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and the sentiment analysis engine recognizes that the user is seeking encouragement. For example, a response such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment," is generated and displayed on the user's device.

[0790] This allows users to obtain the necessary treatment information while reducing their mental burden. By combining it with an emotion analysis engine, it is possible to provide support that takes into consideration the user's emotions.

[0791] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] The user inputs a question into the terminal. The user inputs questions or concerns about treatment in text format and presses the send button. Specific actions include the user typing "What are the side effects of radiation therapy?" into the keyboard. The input is text data.

[0794] Step 2:

[0795] The device sends the question to the server. The device converts the question entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, the following JSON data is sent to the server:

[0796] json

[0797] {

[0798] "question": "What are the side effects of radiation therapy?"

[0799] }

[0800] The input is the user question and the output is JSON data.

[0801] Step 3:

[0802] The server passes the question to a natural language processing engine for analysis. The server analyzes the received JSON data and extracts keywords to understand the intent of the question. Specifically, the server uses a natural language processing engine (e.g., spaCy) to extract the keywords "radiation therapy" and "side effects." The input is JSON data, and the output is a list of keywords.

[0803] Step 4:

[0804] The server passes the text to a sentiment analysis engine to recognize the user's emotional state. The server uses keywords to pass the text entered by the user to a sentiment analysis engine (for example, IBM Watson Tone Analyzer) to analyze the emotional state. Specifically, emotions such as anxiety or worry are identified from the context and punctuation of the text. The input is the user's question text, and the output is information about the emotional state.

[0805] Step 5:

[0806] The server searches the database based on the analysis results and generates an appropriate answer. The server searches the database using keywords obtained from the natural language processing engine and the emotional state obtained from the sentiment analysis engine. Specifically, it extracts personal stories related to "radiation therapy" and "side effects" from the database (for example, MySQL) and generates an answer that takes emotions into consideration. The input is a list of keywords and emotional state information, and the output is a tailored answer.

[0807] Step 6:

[0808] The server sends the generated response to the user's device. The server converts the generated response into JSON format and sends it to the user's device as an HTTP response. Specifically, the following JSON data is sent to the device:

[0809] json

[0810] {

[0811] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[0812] }

[0813] The input is the tailored answer and the output is JSON data.

[0814] Step 7:

[0815] The device displays the answer received from the server. The device parses the received JSON data and displays it in a format that is easy for the user to understand. Specifically, it converts it into HTML or app UI components and displays it on the user's screen. The input is the JSON data received from the server, and the output is information that can be displayed visually. The user can check this displayed answer and obtain the information they need.

[0816] (Application example 2)

[0817] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] Patients suffering from serious illnesses such as cancer are prone to anxiety and stress when collecting treatment-related stories and information. Furthermore, when the information provided is general, it may not be tailored to the individual patient's feelings and circumstances, making it difficult to provide appropriate support. Furthermore, because traditional methods of collecting treatment information do not include psychological support, even if patients obtain the necessary information, it is difficult to alleviate the psychological burden. To address these issues, a system is needed that provides information on treatment stories while also providing an interface that takes patients' feelings into consideration.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0820] In this invention, the server includes means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing user emotions using an emotion engine, means for generating answers using a generative AI model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display, thereby enabling the provision of specific, emotion-sensitive information about treatment.

[0821] The "database" is an information management system that systematically stores past treatment experiences and allows them to be quickly searched and retrieved as needed.

[0822] A "natural language processing engine" is a technology that analyzes text data entered by a user to extract keywords and understand the context.

[0823] An "emotion engine" is a technology that analyzes the user's emotional state from input data and generates an appropriate response.

[0824] A "generative artificial intelligence model" is an artificial intelligence technology that automatically generates text and information based on input data.

[0825] A "head-mounted display (HMD)" is a display device worn on the head, and is hardware that displays a virtual space to the user.

[0826] A "virtual space" is a virtual three-dimensional space generated by a computer that users can experience interactively.

[0827] A system for carrying out this invention includes the following elements: means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing the user's emotions using an emotion engine, means for generating answers using a generative artificial intelligence model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display.

[0828] First, the user puts on a head-mounted display (HMD) and enters a virtual cafe-like environment. When the user enters questions or concerns about treatment in text format, the questions are sent from the HMD to the server. The submitted questions are passed to the server as an HTTP POST request.

[0829] The server first analyzes the received question using a natural language processing engine. The natural language processing engine extracts keywords from the question and understands its intent. Next, the server uses an emotion engine to analyze the user's emotional state from the input text data. This analysis determines whether the user is anxious or seeking encouragement.

[0830] The server then searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and can search for relevant experiences and advice. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored to the results of the emotion engine.

[0831] Finally, the server generates a response that is sent to the user's head-mounted display and displayed in the virtual space. This allows the user to obtain information about treatment in a relaxing environment that feels like being in a real cafe. Furthermore, by combining it with an emotion engine, support that takes the user's emotions into consideration is also possible.

[0832] As a concrete example, consider the case where a user inputs the question, "What are the side effects of radiation therapy?" The server analyzes this question and extracts the keywords "radiation therapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. The answer generated is, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to support you," and is displayed on the user's head-mounted display.

[0833] Example prompt sentence:

[0834] "What are the side effects of radiation therapy?"

[0835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0836] Step 1:

[0837] The user puts on a head-mounted display and enters a virtual space (virtual cafe). They input specific questions or concerns in text format. The input data includes questions typed by the user, such as "What are the side effects of radiation therapy?" After completing the input, the user presses the "Submit" button. The output is the question data in text format.

[0838] Step 2:

[0839] The device sends the question entered by the user to the server as an HTTP POST request. The entered question is converted to JSON format and sent to the server. The input is the user's question data, and the output is an HTTP request to the server. For example, a JSON-formatted request would look like this: { "question": "What are the side effects of radiation therapy?"}

[0840] Step 3:

[0841] The server passes the received question data to a natural language processing engine (NLP engine) to analyze the question. First, it extracts keywords from the question, and then performs context understanding. For example, it extracts the keywords "radiation therapy" and "side effects" from the question. The input is the question data in the HTTP POST request, and the output is the analyzed keywords.

[0842] Step 4:

[0843] The server uses an emotion engine to analyze the user's emotional state from their input. The input is the question data itself, but the emotion engine analyzes the context, wording, and punctuation to determine the user's emotional state. For example, the emotion "anxiety" may be recognized. The input is the question text, and the output is the emotional state (e.g., "anxious").

[0844] Step 5:

[0845] The server searches the database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. It searches the database of past treatment experiences and extracts the most relevant experiences. The input is keywords and emotional states, and the output is the relevant treatment experience.

[0846] Step 6:

[0847] The server generates an appropriate answer using a generative artificial intelligence model (generative AI model) based on the analyzed keywords and emotional state. For example, it generates an answer such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to provide support." The input is the relevant treatment experience and emotional state, and the output is the tailored answer text.

[0848] Step 7:

[0849] The server converts the generated answer into JSON format and sends it to the user's head-mounted display. The input is the generated answer text, and the output is the HTTP response sent to the device.

[0850] Step 8:

[0851] The device parses the received answer and displays it in the virtual space via the user's head-mounted display. For example, the user can view the answer while relaxing in the virtual space. The input is the answer data from the server, and the output is the answer displayed on the HMD display screen.

[0852] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0853] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0854] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0855] [Third embodiment]

[0856] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0857] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0858] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0859] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0860] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0861] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0862] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0863] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0864] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0865] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0866] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0867] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0868] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. Specific embodiments of the system are described below.

[0869] Overall system configuration

[0870] This system consists of a user terminal, a server, and a database. Users input questions using their terminal, and the server analyzes and processes the questions to provide appropriate treatment experience stories.

[0871] Detailed process flow

[0872] 1. User enters a question

[0873] What happens:

[0874] The user enters questions or concerns about the treatment in text format. For example, they might enter, "What are the side effects of radiation therapy?" The question text entered by the user is sent to the server via the device's UI.

[0875] 2. Send the question to the server

[0876] What happens:

[0877] When a user submits a question, the device sends it to the server in JSON format, for example as an HTTP POST request in the following format:

[0878] json

[0879] {

[0880] "question": "What are the side effects of radiation therapy?"

[0881] }

[0882] 3. Analyze the question using natural language processing

[0883] What happens:

[0884] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. Specifically, the keywords "radiation therapy" and "side effects" are extracted.

[0885] 4. Generate appropriate answers based on the analysis results

[0886] What happens:

[0887] The server searches a database based on the analysis results obtained from the natural language processing engine. The database contains past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects."

[0888] 5. Send the answer to the user's device

[0889] What happens:

[0890] Based on the searched data of treatment experiences, the server generates an appropriate answer, which is then sent to the user's device in JSON format.

[0891] 6. Show the answer to the user

[0892] What happens:

[0893] The terminal receives the response from the server and displays it in a user-friendly format, such as "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[0894] Specific examples

[0895] Example 1:

[0896] A user types a question: "What are the side effects of chemotherapy?"

[0897] Process flow:

[0898] The device sends the question to the server.

[0899] The server analyzes "chemotherapy" and "side effects" using a natural language processing engine.

[0900] The database is searched for relevant testimonials and answers such as "Many patients experience nausea and fatigue as side effects of chemotherapy" are generated.

[0901] The answer will be displayed on the device.

[0902] Example 2:

[0903] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[0904] Process flow:

[0905] The device sends the question to the server.

[0906] The server analyzes "after radiation therapy" and "lifestyle advice" using a natural language processing engine.

[0907] The system searches the database for relevant experiences and generates answers such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended."

[0908] The answer will be displayed on the device.

[0909] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[0913] Step 2:

[0914] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[0915] json

[0916] {

[0917] "question": "What are the side effects of radiation therapy?"

[0918] }

[0919] Step 3:

[0920] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[0921] Step 4:

[0922] The server searches a database based on the analysis results obtained from the NLP engine. The database stores past treatment experiences and searches for experiences that match the analyzed keywords. For example, the following SQL query is executed:

[0923] sql

[0924] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[0925] Step 5:

[0926] The server retrieves the treatment experiences searched from the database and generates an appropriate answer based on this information. For example, it might generate an answer such as, "Many patients report dry skin and fatigue. In rare cases, they experience nausea and loss of appetite."

[0927] Step 6:

[0928] The server generates a response and sends it back to the user's device in JSON format, for example:

[0929] json

[0930] {

[0931] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[0932] }

[0933] Step 7:

[0934] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[0935] Example 1

[0936] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0937] In recent years, the number of patients suffering from serious illnesses such as cancer has been increasing, and they often have various questions and anxieties during their treatment. These patients can obtain valuable information about their treatment by requesting past treatment experiences, but conducting such research manually requires time and effort. Current systems have difficulty properly analyzing questions and providing treatment experiences quickly and accurately. Furthermore, they lack a means to effectively extract appropriate information from large amounts of data, making it difficult to quickly provide users with the information they are looking for. Therefore, there is a need for a system that allows users to easily and quickly obtain treatment experiences.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0939] In this invention, the server includes means for inputting a question from a user as text data, means for converting the input text data into JSON format and transmitting it, means for passing the received question to a natural language processing engine for analysis, means for searching a database based on extracted keywords, means for generating an appropriate answer based on the search results, means for transmitting the generated answer to a user terminal, and means for displaying the transmitted answer to the user, thereby enabling users to easily and quickly obtain treatment experiences.

[0940] "Means for inputting user questions as text data" refers to an interface function that allows the user to input doubts or concerns about treatment as questions in text format.

[0941] "Means for converting input text data into JSON format and sending it" refers to the function of converting the question entered by the user into a data format called JavaScript Object Notation (JSON) and sending it to the server.

[0942] "Means of passing received questions to a natural language processing engine for analysis" refers to the analysis function in which the server provides the received questions to a natural language processing engine, extracting keywords and understanding the context.

[0943] "Means for searching the database based on extracted keywords" refers to the function of searching for related information and experiences within the database based on keywords obtained from the natural language processing engine.

[0944] "Means for generating appropriate answers based on search results" refers to a function for utilizing database search results to generate appropriate answers to user questions.

[0945] "Means for sending the generated answer to the user terminal" refers to the function of converting the generated answer back into JSON format and sending it to the user terminal.

[0946] "Means for displaying the transmitted answers to the user" refers to a function for the user terminal to analyze the answers received and display them in a format that is easy for the user to understand.

[0947] A "generative AI model" is a model that uses artificial intelligence technology to perform natural language processing, and is used to analyze the intent of a question and extract appropriate keywords.

[0948] A "prompt sentence" refers to an input sentence used to convey questions or instructions to a generative AI model.

[0949] This invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. The system accepts questions from users, analyzes them, and provides appropriate treatment testimonials, thereby alleviating patients' doubts and anxieties. A specific embodiment of this system is described below.

[0950] This system consists of a user's terminal, a server, and a database. The user uses the terminal to input questions about treatment in text format. For example, they might input a question like, "What are the side effects of radiation therapy?"

[0951] When a user enters a question, the device converts it into JSON format and sends it as an HTTP POST request to the server. The server then passes the received question to a natural language processing engine for analysis. This natural language processing engine uses Google Cloud Natural Language API and Hugging Face's Transformers. Based on keywords extracted through the analysis (e.g., "radiation therapy" and "side effects"), the server queries a database to search for related treatment experiences.

[0952] The database stores past treatment experiences. It is often a relational database such as MySQL or PostgreSQL. The server generates appropriate answers based on the search results and sends them to the device in JSON format. The device then analyzes the received answers and displays them in a user-friendly format.

[0953] For example, if a user enters the question "What are the side effects of chemotherapy?", the process is as follows: The device sends the question to the server, which analyzes the question using a natural language processing engine. The keywords "chemotherapy" and "side effects" are extracted, and related treatment experiences are searched for in the database. For example, an answer such as "Many patients experience nausea and fatigue as side effects of chemotherapy" is generated and displayed on the device.

[0954] An example of a prompt sentence would be "Please tell me about the side effects of chemotherapy," which would be input to the generative AI model. This prompt sentence would then be passed to a natural language processing engine for analysis.

[0955] This system allows patients to easily and quickly obtain reliable treatment experiences, reducing anxiety about treatment. It is also easy to use and can be accessed even by users who do not have a lot of medical knowledge.

[0956] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The user enters a question.

[0959] What happens:

[0960] Using the device interface, users enter questions about treatment in text format, such as "What are the side effects of radiation therapy?"

[0961] input:

[0962] Text questions.

[0963] output:

[0964] Text data captured on the device.

[0965] Specific behavior:

[0966] A user enters a question into a form built with HTML and JavaScript and clicks the "Submit" button.

[0967] Step 2:

[0968] The device converts the question into JSON format and sends it to the server.

[0969] What happens:

[0970] The terminal converts the entered text data into JSON format and sends it to the server using AJAX.

[0971] input:

[0972] Text questions.

[0973] output:

[0974] The question converted to JSON format, and the HTTP POST request.

[0975] Specific behavior:

[0976] JavaScript on the terminal side converts the text data into the following JSON format.

[0977] json

[0978] {

[0979] "question": "What are the side effects of radiation therapy?"

[0980] }

[0981] Step 3:

[0982] The server passes the question to a natural language processing engine for analysis.

[0983] What happens:

[0984] The server parses the received JSON-formatted question data and passes it to a natural language processing engine (e.g., Google Cloud Natural Language API or Hugging Face Transformers).

[0985] input:

[0986] Question data in JSON format.

[0987] output:

[0988] Keywords and their context as analysis results.

[0989] Specific behavior:

[0990] The server-side Python code passes the query to the Google Cloud Natural Language API, which extracts the keywords "radiation therapy" and "side effects" as analysis results.

[0991] Step 4:

[0992] The server searches the database based on the analysis results.

[0993] What happens:

[0994] The server searches a database (e.g., MySQL or PostgreSQL) based on keywords obtained from the natural language processing engine to retrieve relevant treatment experiences.

[0995] input:

[0996] Keywords and their context.

[0997] output:

[0998] Treatment experiences as search results.

[0999] Specific behavior:

[1000] A server-side SQL query searches the database to retrieve treatment experiences related to "radiation therapy" and "side effects."

[1001] Step 5:

[1002] The server generates an appropriate answer based on the search results.

[1003] What happens:

[1004] The server generates appropriate answers to the user's questions based on the treatment experiences obtained from the search results.

[1005] input:

[1006] Search results for treatment experiences.

[1007] output:

[1008] The generated answer.

[1009] Specific behavior:

[1010] Server-side Python code combines the search results to generate answers such as, "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[1011] Step 6:

[1012] The server generates a response and sends it to the device in JSON format.

[1013] What happens:

[1014] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[1015] input:

[1016] The generated answer.

[1017] output:

[1018] Response data in JSON format.

[1019] Specific behavior:

[1020] The server-side Python code converts the generated response into a JSON format like this:

[1021] json

[1022] {

[1023] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[1024] }

[1025] Step 7:

[1026] The terminal analyzes the answer and displays it to the user.

[1027] What happens:

[1028] The terminal analyzes the JSON formatted response data received from the server and displays it in a format that is easy for the user to understand.

[1029] input:

[1030] Response data in JSON format.

[1031] output:

[1032] The final answer that is shown to the user.

[1033] Specific behavior:

[1034] JavaScript on the device analyzes the JSON data and displays the following on the web page: "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[1035] (Application example 1)

[1036] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1037] Patients suffering from serious illnesses such as cancer often have concerns and questions about their treatment, but with limited reliable information sources, it is difficult to get appropriate answers.In addition, information about treatment-related products and services is not provided in a centralized manner, making it difficult for patients to find the supplies they need.This creates an issue of increasing mental burden during treatment.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1039] In this invention, the server includes means for storing past treatment experiences in a database, means for searching for treatment experiences from the database, means for analyzing questions from users using a natural language processing engine, means for operating the search means based on the analysis results, means for displaying search results from the database to the user, and means for presenting product information relevant to the user based on the search results, thereby enabling patients to easily obtain reliable information about treatments and efficiently find related products they need.

[1040] "Past treatment experiences" refers to information about the specific progress, side effects, and life after treatment experienced by patients who have undergone treatment for cancer or other conditions.

[1041] The "database" is an information management system that efficiently stores a vast amount of treatment experiences and related information and allows for quick retrieval.

[1042] A "natural language processing engine" is a technology and its implementation system that analyzes questions entered by users and understands the intent and keywords of the questions.

[1043] "Questions from users" refers to text data entered by patients who have questions or concerns about treatment for cancer or other conditions.

[1044] "Analysis results" refers to information about the intent of the question and keywords obtained when the natural language processing engine analyzes the user's question.

[1045] "Related product information" refers to information about products and services that a patient may need during or after treatment, based on the user's questions and analysis results.

[1046] "Search tools" refer to the technology and its implementation system for searching for appropriate treatment experiences and product information from the database.

[1047] "Display means" refers to the technology and its implementation system for presenting search results obtained from the server on the user's terminal in an easy-to-understand manner.

[1048] This invention is a system that allows patients suffering from serious illnesses such as cancer to obtain treatment experiences and related product information. The system is composed of a user terminal, a server, and a database.

[1049] Overall system configuration

[1050] Users access the system using a smartphone. The server is built with a front-end React Native application and a back-end Flask application, and works in conjunction with a natural language processing engine and database. The database stores past treatment experiences and related product information.

[1051] The primary software used includes natural language processing engines (e.g., SpaCy and BERT-based models), Flask (a Python-based web framework), and React Native (a framework for mobile application development).

[1052] Process Overview

[1053] 1. Enter and submit your question:

[1054] Users enter questions about their treatment in text format on their smartphones, such as "What are the side effects of chemotherapy?", and when they press the send button, the question is sent in JSON format to the backend server.

[1055] 2. Question Analysis:

[1056] The server passes the received question to a natural language processing engine, which extracts keywords and analyzes the context to understand the intent of the question. For example, keywords such as "chemotherapy" and "side effects" are extracted.

[1057] 3. Search for testimonials and product information:

[1058] Based on the analysis results, the server's search means searches a database that stores past treatment experiences and product information related to treatment, and searches for appropriate experiences and product information.

[1059] 4. Generate and display results:

[1060] The server generates an appropriate answer based on the searched treatment experiences and related product information. For example, it generates a specific answer such as, "Many patients experience nausea and fatigue. There are supplements that can reduce side effects." This is then sent to the smartphone device in JSON format. The device receives this and displays it in a format that is easy for the user to understand.

[1061] Specific examples

[1062] 1. Example 1:

[1063] A user types a question: "What are the side effects of chemotherapy?"

[1064] The question is sent to a server, where a natural language processing engine analyzes "chemotherapy" and "side effects."

[1065] The database is searched for related testimonials and product information, and the answer generated is, "Many patients experience nausea and fatigue. There are also supplements available to reduce side effects."

[1066] This response is displayed on the user's terminal.

[1067] 2. Example 2:

[1068] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[1069] The questions are sent to a server, where a natural language processing engine analyzes them into "post-radiation therapy" and "lifestyle advice."

[1070] The database is searched for related testimonials and product information, and the answer generated is, "It is important to get enough rest after radiation therapy. Relaxation products are also recommended."

[1071] This response is displayed on the user's terminal.

[1072] Prompt Sentence Examples

[1073] What are the side effects of chemotherapy?

[1074] I want advice on lifestyle after radiation therapy.

[1075] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1076] Step 1:

[1077] Users use their smartphone devices to input questions about treatment in text format, such as "What are the side effects of chemotherapy?" The question text is sent from the device by clicking the "Send" button.

[1078] Step 2:

[1079] The device detects the click event of the submit button and converts the entered question into JSON format. For example, this JSON data looks like this:

[1080] json

[1081] {

[1082] "question": "What are the side effects of chemotherapy?"

[1083] }

[1084] The converted JSON data is sent to the backend server via an HTTP POST request.

[1085] Step 3:

[1086] The server analyzes the received HTTP POST request and extracts the question text from the JSON data. The extracted question text is passed to a natural language processing engine. The natural language processing engine analyzes the input question text and extracts keywords and context. For example, the keywords "chemotherapy" and "side effects" are extracted.

[1087] Step 4:

[1088] The server generates a search query for the database based on the analysis results (keywords and context) obtained from the natural language processing engine. Using the generated query, the server searches the database to retrieve relevant treatment experiences and product information.

[1089] Step 5:

[1090] The searched treatment experience stories and related product information are converted into an appropriate format by the server and generated as a JSON response to be provided to the user. This JSON data includes, for example, the following information:

[1091] json

[1092] {

[1093] "answer": "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects."

[1094] }

[1095] The generated JSON data is sent to the user terminal.

[1096] Step 6:

[1097] The device parses the JSON data received from the server and displays the provided answers in an easy-to-read format for the user. For example, a specific answer such as "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects" is displayed on the user interface.

[1098] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1099] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion engine, it is possible to recognize the user's emotions and provide more appropriate information. Specific embodiments of this invention are described below.

[1100] Overall system configuration

[1101] The system consists of a user's device, a server, a database, and an emotion engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[1102] Detailed process flow

[1103] 1. User enters a question

[1104] What happens:

[1105] Users enter their questions and concerns about treatment in text format. For example, a user might enter, "What are the side effects of radiation therapy?" This can be done manually or by voice input. The user presses the "Send" button to send the question.

[1106] 2. Send the question to the server

[1107] What happens:

[1108] When a user submits a question, the device sends the question to the server in JSON format, for example as an HTTP POST request in the following format:

[1109] json

[1110] {

[1111] "question": "What are the side effects of radiation therapy?"

[1112] }

[1113] 3. Analyze the question using natural language processing

[1114] What happens:

[1115] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[1116] 4. Recognize user emotions

[1117] What happens:

[1118] The server analyzes the user's emotions using an emotion engine, which determines the user's emotional state based on the context, punctuation, and vocabulary of the input text. For example, it can recognize if the user is feeling anxious or seeking encouragement.

[1119] 5. Generate appropriate answers based on the analysis results

[1120] What happens:

[1121] The server searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and searches for appropriate experiences. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored according to the results of the emotion engine.

[1122] 6. Send the answer to the user's device

[1123] What happens:

[1124] The server generates a response and sends it back to the user's device. The response is in JSON format and includes information that takes into account the user's emotional state. For example, it might say, "Many patients have reported dry skin and fatigue. However, please rest assured that medical staff are always available to assist you."

[1125] 7. Show the answer to the user

[1126] What happens:

[1127] The device parses the response received from the server and displays it in a user-friendly format. The user can then check the response displayed on the device screen and obtain information about treatment.

[1128] Specific examples

[1129] Example 1:

[1130] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which uses a natural language processing engine to analyze the keywords "chemotherapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue as side effects of chemotherapy. However, don't worry, your medical team is always there to support you," is generated and displayed on the user's device.

[1131] Example 2:

[1132] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and an emotion engine recognizes the user's feelings of encouragement. For example, a response such as "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment" is generated and displayed on the user's device.

[1133] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden. Furthermore, by combining it with an emotion engine, support is provided that takes into consideration the user's emotions.

[1134] The processing flow will be explained below.

[1135] Step 1:

[1136] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[1137] Step 2:

[1138] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[1139] json

[1140] {

[1141] "question": "What are the side effects of radiation therapy?"

[1142] }

[1143] Step 3:

[1144] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[1145] Step 4:

[1146] The server uses an emotion engine to recognize the user's emotion from the text provided by the user. The emotion engine determines the emotion from the context, punctuation, and wording in the text, and analyzes whether the user is feeling anxious.

[1147] Step 5:

[1148] The server searches the database based on the analysis results from the NLP engine and emotion engine. The database stores past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects." An example search query would be:

[1149] sql

[1150] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[1151] Step 6:

[1152] The server retrieves the treatment experiences searched from the database and generates appropriate answers based on this information. Based on the analysis results of the emotion engine, the answer is adjusted to take into account the user's emotional state. For example, the answer might be something like, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to help."

[1153] Step 7:

[1154] The server generates a response and sends it back to the user's device. The response is sent in JSON format. For example, it might look something like this:

[1155] json

[1156] {

[1157] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[1158] }

[1159] Step 8:

[1160] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[1161] Example 2

[1162] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] When patients are facing a serious illness such as cancer, they can obtain information about treatment by searching the internet or consulting with a medical institution. However, these methods make it difficult to obtain information that is tailored to the patient's specific questions and emotional state. Furthermore, psychological support is often insufficient. Therefore, there is a need for a system that can reduce the anxiety and stress felt by patients and provide more appropriate information tailored to each individual's situation.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1165] In this invention, the server includes a means for storing past treatment experiences in a database, a means for analyzing questions from users using a natural language processing engine, and a means for recognizing the emotional state of users using an emotion analysis engine, which makes it possible to provide appropriate answers and encouraging messages according to the emotional state of the user when the patient seeks information on a specific question.

[1166] "Past treatment experiences" are recorded information about the experiences, progress, and results of patients who have received treatment.

[1167] A "database" is a system for efficiently searching, storing, managing, and updating information.

[1168] A "natural language processing engine" is a program that analyzes the language that humans use on a daily basis and understands its meaning.

[1169] "Questions from users" are text messages entered by users of the system expressing specific questions or concerns about treatment.

[1170] An "emotion analysis engine" is a program that determines a user's emotional state (e.g., anxiety, joy, sadness, etc.) from text data and voice data and identifies those emotions.

[1171] A "search means" is a function or program for finding desired information from a database.

[1172] "Adjustment" refers to the functions or processes that modify or optimize the search results based on the user's emotional state.

[1173] "Means for displaying messages" refers to functions and interfaces for presenting search results and adjusted information so that users can visually confirm them.

[1174] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion analysis engine, it is possible to recognize the emotions of users and provide more appropriate information. Specific embodiments of this invention are described below.

[1175] Overall system configuration

[1176] The system consists of a user's device, a server, a database, and an emotion analysis engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion analysis engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[1177] Hardware and software used

[1178] User devices: PCs, smartphones, tablets, etc.

[1179] Server: High-performance computer (such as a cloud server)

[1180] Database: MySQL, MongoDB, etc.

[1181] Natural language processing engines: spaCy, Google NLP API, etc.

[1182] Sentiment analysis engine: IBM Watson Tone Analyzer, etc.

[1183] Data processing and calculation

[1184] 1. The user enters a question

[1185] The user uses the device to enter a question about treatment in text format, for example, "What are the side effects of radiation therapy?" Voice input is also possible.

[1186] 2. Send the question to the server

[1187] The device converts the question entered by the user into JSON format and sends it to the server. For example, the following HTTP POST request is sent:

[1188] json

[1189] {

[1190] "question": "What are the side effects of radiation therapy?"

[1191] }

[1192] 3. Analyze the question using natural language processing

[1193] The server passes the received question to a natural language processing engine for analysis. During the analysis process, keywords are extracted to understand the intent of the question, and the keywords "radiation therapy" and "side effects" are identified.

[1194] 4. Recognize user emotions

[1195] The server uses a sentiment analysis engine to analyze the user's emotional state, determining their feelings from the context of the text, punctuation, and vocabulary, and recognizing emotions such as anxiety or worry.

[1196] 5. Generate appropriate answers based on the analysis results

[1197] The server searches a database based on keywords obtained from a natural language processing engine and emotional states obtained from an emotion analysis engine, extracts relevant information from a database storing past treatment experiences, and generates an answer that takes emotions into consideration.

[1198] 6. Send the answer to the user's device

[1199] The server generates a response and sends it to the user's device in JSON format, such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to help."

[1200] 7. Show the answer to the user

[1201] The terminal converts the answers received from the server into a display format and provides it to the user. The user can check the answers displayed on the screen and obtain information about treatment.

[1202] Specific examples

[1203] Example 1:

[1204] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which then uses a natural language processing engine to analyze "chemotherapy" and "side effects." The sentiment analysis engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue, but don't worry, your medical team is always here to support you" is generated and displayed on the user's device.

[1205] Example 2:

[1206] A user inputs a question such as, "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and the sentiment analysis engine recognizes that the user is seeking encouragement. For example, a response such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment," is generated and displayed on the user's device.

[1207] This allows users to obtain the necessary treatment information while reducing their mental burden. By combining it with an emotion analysis engine, it is possible to provide support that takes into consideration the user's emotions.

[1208] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1209] Step 1:

[1210] The user inputs a question into the terminal. The user inputs questions or concerns about treatment in text format and presses the send button. Specific actions include the user typing "What are the side effects of radiation therapy?" into the keyboard. The input is text data.

[1211] Step 2:

[1212] The device sends the question to the server. The device converts the question entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, the following JSON data is sent to the server:

[1213] json

[1214] {

[1215] "question": "What are the side effects of radiation therapy?"

[1216] }

[1217] The input is the user question and the output is JSON data.

[1218] Step 3:

[1219] The server passes the question to a natural language processing engine for analysis. The server analyzes the received JSON data and extracts keywords to understand the intent of the question. Specifically, the server uses a natural language processing engine (e.g., spaCy) to extract the keywords "radiation therapy" and "side effects." The input is JSON data, and the output is a list of keywords.

[1220] Step 4:

[1221] The server passes the text to a sentiment analysis engine to recognize the user's emotional state. The server uses keywords to pass the text entered by the user to a sentiment analysis engine (for example, IBM Watson Tone Analyzer) to analyze the emotional state. Specifically, emotions such as anxiety or worry are identified from the context and punctuation of the text. The input is the user's question text, and the output is information about the emotional state.

[1222] Step 5:

[1223] The server searches the database based on the analysis results and generates an appropriate answer. The server searches the database using keywords obtained from the natural language processing engine and the emotional state obtained from the sentiment analysis engine. Specifically, it extracts personal stories related to "radiation therapy" and "side effects" from the database (for example, MySQL) and generates an answer that takes emotions into consideration. The input is a list of keywords and emotional state information, and the output is a tailored answer.

[1224] Step 6:

[1225] The server sends the generated response to the user's device. The server converts the generated response into JSON format and sends it to the user's device as an HTTP response. Specifically, the following JSON data is sent to the device:

[1226] json

[1227] {

[1228] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[1229] }

[1230] The input is the tailored answer and the output is JSON data.

[1231] Step 7:

[1232] The device displays the answer received from the server. The device parses the received JSON data and displays it in a format that is easy for the user to understand. Specifically, it converts it into HTML or app UI components and displays it on the user's screen. The input is the JSON data received from the server, and the output is information that can be displayed visually. The user can check this displayed answer and obtain the information they need.

[1233] (Application example 2)

[1234] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1235] Patients suffering from serious illnesses such as cancer are prone to anxiety and stress when collecting treatment-related stories and information. Furthermore, when the information provided is general, it may not be tailored to the individual patient's feelings and circumstances, making it difficult to provide appropriate support. Furthermore, because traditional methods of collecting treatment information do not include psychological support, even if patients obtain the necessary information, it is difficult to alleviate the psychological burden. To address these issues, a system is needed that provides information on treatment stories while also providing an interface that takes patients' feelings into consideration.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1237] In this invention, the server includes means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing user emotions using an emotion engine, means for generating answers using a generative AI model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display, thereby enabling the provision of specific, emotion-sensitive information about treatment.

[1238] The "database" is an information management system that systematically stores past treatment experiences and allows them to be quickly searched and retrieved as needed.

[1239] A "natural language processing engine" is a technology that analyzes text data entered by a user to extract keywords and understand the context.

[1240] An "emotion engine" is a technology that analyzes the user's emotional state from input data and generates an appropriate response.

[1241] A "generative artificial intelligence model" is an artificial intelligence technology that automatically generates text and information based on input data.

[1242] A "head-mounted display (HMD)" is a display device worn on the head, and is hardware that displays a virtual space to the user.

[1243] A "virtual space" is a virtual three-dimensional space generated by a computer that users can experience interactively.

[1244] A system for carrying out this invention includes the following elements: means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing the user's emotions using an emotion engine, means for generating answers using a generative artificial intelligence model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display.

[1245] First, the user puts on a head-mounted display (HMD) and enters a virtual cafe-like environment. When the user enters questions or concerns about treatment in text format, the questions are sent from the HMD to the server. The submitted questions are passed to the server as an HTTP POST request.

[1246] The server first analyzes the received question using a natural language processing engine. The natural language processing engine extracts keywords from the question and understands its intent. Next, the server uses an emotion engine to analyze the user's emotional state from the input text data. This analysis determines whether the user is anxious or seeking encouragement.

[1247] The server then searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and can search for relevant experiences and advice. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored to the results of the emotion engine.

[1248] Finally, the server generates a response that is sent to the user's head-mounted display and displayed in the virtual space. This allows the user to obtain information about treatment in a relaxing environment that feels like being in a real cafe. Furthermore, by combining it with an emotion engine, support that takes the user's emotions into consideration is also possible.

[1249] As a concrete example, consider the case where a user inputs the question, "What are the side effects of radiation therapy?" The server analyzes this question and extracts the keywords "radiation therapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. The answer generated is, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to support you," and is displayed on the user's head-mounted display.

[1250] Example prompt sentence:

[1251] "What are the side effects of radiation therapy?"

[1252] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1253] Step 1:

[1254] The user puts on a head-mounted display and enters a virtual space (virtual cafe). They input specific questions or concerns in text format. The input data includes questions typed by the user, such as "What are the side effects of radiation therapy?" After completing the input, the user presses the "Submit" button. The output is the question data in text format.

[1255] Step 2:

[1256] The device sends the question entered by the user to the server as an HTTP POST request. The entered question is converted to JSON format and sent to the server. The input is the user's question data, and the output is an HTTP request to the server. For example, a JSON-formatted request would look like this: { "question": "What are the side effects of radiation therapy?"}

[1257] Step 3:

[1258] The server passes the received question data to a natural language processing engine (NLP engine) to analyze the question. First, it extracts keywords from the question, and then performs context understanding. For example, it extracts the keywords "radiation therapy" and "side effects" from the question. The input is the question data in the HTTP POST request, and the output is the analyzed keywords.

[1259] Step 4:

[1260] The server uses an emotion engine to analyze the user's emotional state from their input. The input is the question data itself, but the emotion engine analyzes the context, wording, and punctuation to determine the user's emotional state. For example, the emotion "anxiety" may be recognized. The input is the question text, and the output is the emotional state (e.g., "anxious").

[1261] Step 5:

[1262] The server searches the database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. It searches the database of past treatment experiences and extracts the most relevant experiences. The input is keywords and emotional states, and the output is the relevant treatment experience.

[1263] Step 6:

[1264] The server generates an appropriate answer using a generative artificial intelligence model (generative AI model) based on the analyzed keywords and emotional state. For example, it generates an answer such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to provide support." The input is the relevant treatment experience and emotional state, and the output is the tailored answer text.

[1265] Step 7:

[1266] The server converts the generated answer into JSON format and sends it to the user's head-mounted display. The input is the generated answer text, and the output is the HTTP response sent to the device.

[1267] Step 8:

[1268] The device parses the received answer and displays it in the virtual space via the user's head-mounted display. For example, the user can view the answer while relaxing in the virtual space. The input is the answer data from the server, and the output is the answer displayed on the HMD display screen.

[1269] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1270] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1271] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1272] [Fourth embodiment]

[1273] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1274] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1275] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1276] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1277] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1278] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1279] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1280] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1281] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1282] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1283] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1284] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1285] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1286] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. Specific embodiments of the system are described below.

[1287] Overall system configuration

[1288] This system consists of a user terminal, a server, and a database. Users input questions using their terminal, and the server analyzes and processes the questions to provide appropriate treatment experience stories.

[1289] Detailed process flow

[1290] 1. User enters a question

[1291] What happens:

[1292] The user enters questions or concerns about the treatment in text format. For example, they might enter, "What are the side effects of radiation therapy?" The question text entered by the user is sent to the server via the device's UI.

[1293] 2. Send the question to the server

[1294] What happens:

[1295] When a user submits a question, the device sends it to the server in JSON format, for example as an HTTP POST request in the following format:

[1296] json

[1297] {

[1298] "question": "What are the side effects of radiation therapy?"

[1299] }

[1300] 3. Analyze the question using natural language processing

[1301] What happens:

[1302] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. Specifically, the keywords "radiation therapy" and "side effects" are extracted.

[1303] 4. Generate appropriate answers based on the analysis results

[1304] What happens:

[1305] The server searches a database based on the analysis results obtained from the natural language processing engine. The database contains past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects."

[1306] 5. Send the answer to the user's device

[1307] What happens:

[1308] Based on the searched data of treatment experiences, the server generates an appropriate answer, which is then sent to the user's device in JSON format.

[1309] 6. Show the answer to the user

[1310] What happens:

[1311] The terminal receives the response from the server and displays it in a user-friendly format, such as "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[1312] Specific examples

[1313] Example 1:

[1314] A user types a question: "What are the side effects of chemotherapy?"

[1315] Process flow:

[1316] The device sends the question to the server.

[1317] The server analyzes "chemotherapy" and "side effects" using a natural language processing engine.

[1318] The database is searched for relevant testimonials and answers such as "Many patients experience nausea and fatigue as side effects of chemotherapy" are generated.

[1319] The answer will be displayed on the device.

[1320] Example 2:

[1321] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[1322] Process flow:

[1323] The device sends the question to the server.

[1324] The server analyzes "after radiation therapy" and "lifestyle advice" using a natural language processing engine.

[1325] The system searches the database for relevant experiences and generates answers such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended."

[1326] The answer will be displayed on the device.

[1327] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden.

[1328] The processing flow will be explained below.

[1329] Step 1:

[1330] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[1331] Step 2:

[1332] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[1333] json

[1334] {

[1335] "question": "What are the side effects of radiation therapy?"

[1336] }

[1337] Step 3:

[1338] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[1339] Step 4:

[1340] The server searches a database based on the analysis results obtained from the NLP engine. The database stores past treatment experiences and searches for experiences that match the analyzed keywords. For example, the following SQL query is executed:

[1341] sql

[1342] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[1343] Step 5:

[1344] The server retrieves the treatment experiences searched from the database and generates an appropriate answer based on this information. For example, it might generate an answer such as, "Many patients report dry skin and fatigue. In rare cases, they experience nausea and loss of appetite."

[1345] Step 6:

[1346] The server generates a response and sends it back to the user's device in JSON format, for example:

[1347] json

[1348] {

[1349] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[1350] }

[1351] Step 7:

[1352] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[1353] Example 1

[1354] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1355] In recent years, the number of patients suffering from serious illnesses such as cancer has been increasing, and they often have various questions and anxieties during their treatment. These patients can obtain valuable information about their treatment by requesting past treatment experiences, but conducting such research manually requires time and effort. Current systems have difficulty properly analyzing questions and providing treatment experiences quickly and accurately. Furthermore, they lack a means to effectively extract appropriate information from large amounts of data, making it difficult to quickly provide users with the information they are looking for. Therefore, there is a need for a system that allows users to easily and quickly obtain treatment experiences.

[1356] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1357] In this invention, the server includes means for inputting a question from a user as text data, means for converting the input text data into JSON format and transmitting it, means for passing the received question to a natural language processing engine for analysis, means for searching a database based on extracted keywords, means for generating an appropriate answer based on the search results, means for transmitting the generated answer to a user terminal, and means for displaying the transmitted answer to the user, thereby enabling users to easily and quickly obtain treatment experiences.

[1358] "Means for inputting user questions as text data" refers to an interface function that allows the user to input doubts or concerns about treatment as questions in text format.

[1359] "Means for converting input text data into JSON format and sending it" refers to the function of converting the question entered by the user into a data format called JavaScript Object Notation (JSON) and sending it to the server.

[1360] "Means of passing received questions to a natural language processing engine for analysis" refers to the analysis function in which the server provides the received questions to a natural language processing engine, extracting keywords and understanding the context.

[1361] "Means for searching the database based on extracted keywords" refers to the function of searching for related information and experiences within the database based on keywords obtained from the natural language processing engine.

[1362] "Means for generating appropriate answers based on search results" refers to a function for utilizing database search results to generate appropriate answers to user questions.

[1363] "Means for sending the generated answer to the user terminal" refers to the function of converting the generated answer back into JSON format and sending it to the user terminal.

[1364] "Means for displaying the transmitted answers to the user" refers to a function for the user terminal to analyze the answers received and display them in a format that is easy for the user to understand.

[1365] A "generative AI model" is a model that uses artificial intelligence technology to perform natural language processing, and is used to analyze the intent of a question and extract appropriate keywords.

[1366] A "prompt sentence" refers to an input sentence used to convey questions or instructions to a generative AI model.

[1367] This invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials. The system accepts questions from users, analyzes them, and provides appropriate treatment testimonials, thereby alleviating patients' doubts and anxieties. A specific embodiment of this system is described below.

[1368] This system consists of a user's terminal, a server, and a database. The user uses the terminal to input questions about treatment in text format. For example, they might input a question like, "What are the side effects of radiation therapy?"

[1369] When a user enters a question, the device converts it into JSON format and sends it as an HTTP POST request to the server. The server then passes the received question to a natural language processing engine for analysis. This natural language processing engine uses Google Cloud Natural Language API and Hugging Face's Transformers. Based on keywords extracted through the analysis (e.g., "radiation therapy" and "side effects"), the server queries a database to search for related treatment experiences.

[1370] The database stores past treatment experiences. It is often a relational database such as MySQL or PostgreSQL. The server generates appropriate answers based on the search results and sends them to the device in JSON format. The device then analyzes the received answers and displays them in a user-friendly format.

[1371] For example, if a user enters the question "What are the side effects of chemotherapy?", the process is as follows: The device sends the question to the server, which analyzes the question using a natural language processing engine. The keywords "chemotherapy" and "side effects" are extracted, and related treatment experiences are searched for in the database. For example, an answer such as "Many patients experience nausea and fatigue as side effects of chemotherapy" is generated and displayed on the device.

[1372] An example of a prompt sentence would be "Please tell me about the side effects of chemotherapy," which would be input to the generative AI model. This prompt sentence would then be passed to a natural language processing engine for analysis.

[1373] This system allows patients to easily and quickly obtain reliable treatment experiences, reducing anxiety about treatment. It is also easy to use and can be accessed even by users who do not have a lot of medical knowledge.

[1374] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1375] Step 1:

[1376] The user enters a question.

[1377] What happens:

[1378] Using the device interface, users enter questions about treatment in text format, such as "What are the side effects of radiation therapy?"

[1379] input:

[1380] Text questions.

[1381] output:

[1382] Text data captured on the device.

[1383] Specific behavior:

[1384] A user enters a question into a form built with HTML and JavaScript and clicks the "Submit" button.

[1385] Step 2:

[1386] The device converts the question into JSON format and sends it to the server.

[1387] What happens:

[1388] The terminal converts the entered text data into JSON format and sends it to the server using AJAX.

[1389] input:

[1390] Text questions.

[1391] output:

[1392] The question converted to JSON format, and the HTTP POST request.

[1393] Specific behavior:

[1394] JavaScript on the terminal side converts the text data into the following JSON format.

[1395] json

[1396] {

[1397] "question": "What are the side effects of radiation therapy?"

[1398] }

[1399] Step 3:

[1400] The server passes the question to a natural language processing engine for analysis.

[1401] What happens:

[1402] The server parses the received JSON-formatted question data and passes it to a natural language processing engine (e.g., Google Cloud Natural Language API or Hugging Face Transformers).

[1403] input:

[1404] Question data in JSON format.

[1405] output:

[1406] Keywords and their context as analysis results.

[1407] Specific behavior:

[1408] The server-side Python code passes the query to the Google Cloud Natural Language API, which extracts the keywords "radiation therapy" and "side effects" as analysis results.

[1409] Step 4:

[1410] The server searches the database based on the analysis results.

[1411] What happens:

[1412] The server searches a database (e.g., MySQL or PostgreSQL) based on keywords obtained from the natural language processing engine to retrieve relevant treatment experiences.

[1413] input:

[1414] Keywords and their context.

[1415] output:

[1416] Treatment experiences as search results.

[1417] Specific behavior:

[1418] A server-side SQL query searches the database to retrieve treatment experiences related to "radiation therapy" and "side effects."

[1419] Step 5:

[1420] The server generates an appropriate answer based on the search results.

[1421] What happens:

[1422] The server generates appropriate answers to the user's questions based on the treatment experiences obtained from the search results.

[1423] input:

[1424] Search results for treatment experiences.

[1425] output:

[1426] The generated answer.

[1427] Specific behavior:

[1428] Server-side Python code combines the search results to generate answers such as, "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[1429] Step 6:

[1430] The server generates a response and sends it to the device in JSON format.

[1431] What happens:

[1432] The server converts the generated response into JSON format and sends it to the terminal as an HTTP response.

[1433] input:

[1434] The generated answer.

[1435] output:

[1436] Response data in JSON format.

[1437] Specific behavior:

[1438] The server-side Python code converts the generated response into a JSON format like this:

[1439] json

[1440] {

[1441] "answer": "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite."

[1442] }

[1443] Step 7:

[1444] The terminal analyzes the answer and displays it to the user.

[1445] What happens:

[1446] The terminal analyzes the JSON formatted response data received from the server and displays it in a format that is easy for the user to understand.

[1447] input:

[1448] Response data in JSON format.

[1449] output:

[1450] The final answer that is shown to the user.

[1451] Specific behavior:

[1452] JavaScript on the device analyzes the JSON data and displays the following on the web page: "Many patients report dry skin and fatigue. Rarely, nausea and loss of appetite occur."

[1453] (Application example 1)

[1454] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1455] Patients suffering from serious illnesses such as cancer often have concerns and questions about their treatment, but with limited reliable information sources, it is difficult to get appropriate answers.In addition, information about treatment-related products and services is not provided in a centralized manner, making it difficult for patients to find the supplies they need.This creates an issue of increasing mental burden during treatment.

[1456] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1457] In this invention, the server includes means for storing past treatment experiences in a database, means for searching for treatment experiences from the database, means for analyzing questions from users using a natural language processing engine, means for operating the search means based on the analysis results, means for displaying search results from the database to the user, and means for presenting product information relevant to the user based on the search results, thereby enabling patients to easily obtain reliable information about treatments and efficiently find related products they need.

[1458] "Past treatment experiences" refers to information about the specific progress, side effects, and life after treatment experienced by patients who have undergone treatment for cancer or other conditions.

[1459] The "database" is an information management system that efficiently stores a vast amount of treatment experiences and related information and allows for quick retrieval.

[1460] A "natural language processing engine" is a technology and its implementation system that analyzes questions entered by users and understands the intent and keywords of the questions.

[1461] "Questions from users" refers to text data entered by patients who have questions or concerns about treatment for cancer or other conditions.

[1462] "Analysis results" refers to information about the intent of the question and keywords obtained when the natural language processing engine analyzes the user's question.

[1463] "Related product information" refers to information about products and services that a patient may need during or after treatment, based on the user's questions and analysis results.

[1464] "Search tools" refer to the technology and its implementation system for searching for appropriate treatment experiences and product information from the database.

[1465] "Display means" refers to the technology and its implementation system for presenting search results obtained from the server on the user's terminal in an easy-to-understand manner.

[1466] This invention is a system that allows patients suffering from serious illnesses such as cancer to obtain treatment experiences and related product information. The system is composed of a user terminal, a server, and a database.

[1467] Overall system configuration

[1468] Users access the system using a smartphone. The server is built with a front-end React Native application and a back-end Flask application, and works in conjunction with a natural language processing engine and database. The database stores past treatment experiences and related product information.

[1469] The primary software used includes natural language processing engines (e.g., SpaCy and BERT-based models), Flask (a Python-based web framework), and React Native (a framework for mobile application development).

[1470] Process Overview

[1471] 1. Enter and submit your question:

[1472] Users enter questions about their treatment in text format on their smartphones, such as "What are the side effects of chemotherapy?", and when they press the send button, the question is sent in JSON format to the backend server.

[1473] 2. Question Analysis:

[1474] The server passes the received question to a natural language processing engine, which extracts keywords and analyzes the context to understand the intent of the question. For example, keywords such as "chemotherapy" and "side effects" are extracted.

[1475] 3. Search for testimonials and product information:

[1476] Based on the analysis results, the server's search means searches a database that stores past treatment experiences and product information related to treatment, and searches for appropriate experiences and product information.

[1477] 4. Generate and display results:

[1478] The server generates an appropriate answer based on the searched treatment experiences and related product information. For example, it generates a specific answer such as, "Many patients experience nausea and fatigue. There are supplements that can reduce side effects." This is then sent to the smartphone device in JSON format. The device receives this and displays it in a format that is easy for the user to understand.

[1479] Specific examples

[1480] 1. Example 1:

[1481] A user types a question: "What are the side effects of chemotherapy?"

[1482] The question is sent to a server, where a natural language processing engine analyzes "chemotherapy" and "side effects."

[1483] The database is searched for related testimonials and product information, and the answer generated is, "Many patients experience nausea and fatigue. There are also supplements available to reduce side effects."

[1484] This response is displayed on the user's terminal.

[1485] 2. Example 2:

[1486] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy."

[1487] The questions are sent to a server, where a natural language processing engine analyzes them into "post-radiation therapy" and "lifestyle advice."

[1488] The database is searched for related testimonials and product information, and the answer generated is, "It is important to get enough rest after radiation therapy. Relaxation products are also recommended."

[1489] This response is displayed on the user's terminal.

[1490] Prompt Sentence Examples

[1491] What are the side effects of chemotherapy?

[1492] I want advice on lifestyle after radiation therapy.

[1493] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1494] Step 1:

[1495] Users use their smartphone devices to input questions about treatment in text format, such as "What are the side effects of chemotherapy?" The question text is sent from the device by clicking the "Send" button.

[1496] Step 2:

[1497] The device detects the click event of the submit button and converts the entered question into JSON format. For example, this JSON data looks like this:

[1498] json

[1499] {

[1500] "question": "What are the side effects of chemotherapy?"

[1501] }

[1502] The converted JSON data is sent to the backend server via an HTTP POST request.

[1503] Step 3:

[1504] The server analyzes the received HTTP POST request and extracts the question text from the JSON data. The extracted question text is passed to a natural language processing engine. The natural language processing engine analyzes the input question text and extracts keywords and context. For example, the keywords "chemotherapy" and "side effects" are extracted.

[1505] Step 4:

[1506] The server generates a search query for the database based on the analysis results (keywords and context) obtained from the natural language processing engine. Using the generated query, the server searches the database to retrieve relevant treatment experiences and product information.

[1507] Step 5:

[1508] The searched treatment experience stories and related product information are converted into an appropriate format by the server and generated as a JSON response to be provided to the user. This JSON data includes, for example, the following information:

[1509] json

[1510] {

[1511] "answer": "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects."

[1512] }

[1513] The generated JSON data is sent to the user terminal.

[1514] Step 6:

[1515] The device parses the JSON data received from the server and displays the provided answers in an easy-to-read format for the user. For example, a specific answer such as "Many patients experience nausea and fatigue. There are supplements available to reduce these side effects" is displayed on the user interface.

[1516] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1517] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion engine, it is possible to recognize the user's emotions and provide more appropriate information. Specific embodiments of this invention are described below.

[1518] Overall system configuration

[1519] The system consists of a user's device, a server, a database, and an emotion engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[1520] Detailed process flow

[1521] 1. User enters a question

[1522] What happens:

[1523] Users enter their questions and concerns about treatment in text format. For example, a user might enter, "What are the side effects of radiation therapy?" This can be done manually or by voice input. The user presses the "Send" button to send the question.

[1524] 2. Send the question to the server

[1525] What happens:

[1526] When a user submits a question, the device sends the question to the server in JSON format, for example as an HTTP POST request in the following format:

[1527] json

[1528] {

[1529] "question": "What are the side effects of radiation therapy?"

[1530] }

[1531] 3. Analyze the question using natural language processing

[1532] What happens:

[1533] The server passes the received question to a natural language processing engine for analysis. During the analysis, keywords are extracted and context is understood to understand the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[1534] 4. Recognize user emotions

[1535] What happens:

[1536] The server analyzes the user's emotions using an emotion engine, which determines the user's emotional state based on the context, punctuation, and vocabulary of the input text. For example, it can recognize if the user is feeling anxious or seeking encouragement.

[1537] 5. Generate appropriate answers based on the analysis results

[1538] What happens:

[1539] The server searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and searches for appropriate experiences. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored according to the results of the emotion engine.

[1540] 6. Send the answer to the user's device

[1541] What happens:

[1542] The server generates a response and sends it back to the user's device. The response is in JSON format and includes information that takes into account the user's emotional state. For example, it might say, "Many patients have reported dry skin and fatigue. However, please rest assured that medical staff are always available to assist you."

[1543] 7. Show the answer to the user

[1544] What happens:

[1545] The device parses the response received from the server and displays it in a user-friendly format. The user can then check the response displayed on the device screen and obtain information about treatment.

[1546] Specific examples

[1547] Example 1:

[1548] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which uses a natural language processing engine to analyze the keywords "chemotherapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue as side effects of chemotherapy. However, don't worry, your medical team is always there to support you," is generated and displayed on the user's device.

[1549] Example 2:

[1550] The user inputs a question such as "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and an emotion engine recognizes the user's feelings of encouragement. For example, a response such as "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment" is generated and displayed on the user's device.

[1551] This allows patients to obtain the necessary treatment information in a highly convenient format while reducing the mental burden. Furthermore, by combining it with an emotion engine, support is provided that takes into consideration the user's emotions.

[1552] The processing flow will be explained below.

[1553] Step 1:

[1554] A user uses a terminal to enter a text question, for example, "What are the side effects of radiation therapy?" The user presses a "Submit" button to submit the question.

[1555] Step 2:

[1556] The device sends the user's question to the server as an HTTP POST request. This request is sent in JSON format, for example:

[1557] json

[1558] {

[1559] "question": "What are the side effects of radiation therapy?"

[1560] }

[1561] Step 3:

[1562] The server passes the received question to a natural language processing engine for analysis. The natural language processing engine extracts keywords from the question and understands the intent of the question. For example, the keywords "radiation therapy" and "side effects" are extracted.

[1563] Step 4:

[1564] The server uses an emotion engine to recognize the user's emotion from the text provided by the user. The emotion engine determines the emotion from the context, punctuation, and wording in the text, and analyzes whether the user is feeling anxious.

[1565] Step 5:

[1566] The server searches the database based on the analysis results from the NLP engine and emotion engine. The database stores past treatment experiences, and searches for experiences related to "radiation therapy" and "side effects." An example search query would be:

[1567] sql

[1568] SELECT FROM treatment_experiences WHERE treatment = 'radiotherapy' AND symptom = 'side effects';

[1569] Step 6:

[1570] The server retrieves the treatment experiences searched from the database and generates appropriate answers based on this information. Based on the analysis results of the emotion engine, the answer is adjusted to take into account the user's emotional state. For example, the answer might be something like, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to help."

[1571] Step 7:

[1572] The server generates a response and sends it back to the user's device. The response is sent in JSON format. For example, it might look something like this:

[1573] json

[1574] {

[1575] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[1576] }

[1577] Step 8:

[1578] The device parses the answer received from the server and displays it in a user-friendly format. The user can check the answer displayed on the device screen and obtain information about treatment.

[1579] Example 2

[1580] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1581] When patients are facing a serious illness such as cancer, they can obtain information about treatment by searching the internet or consulting with a medical institution. However, these methods make it difficult to obtain information that is tailored to the patient's specific questions and emotional state. Furthermore, psychological support is often insufficient. Therefore, there is a need for a system that can reduce the anxiety and stress felt by patients and provide more appropriate information tailored to each individual's situation.

[1582] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1583] In this invention, the server includes a means for storing past treatment experiences in a database, a means for analyzing questions from users using a natural language processing engine, and a means for recognizing the emotional state of users using an emotion analysis engine, which makes it possible to provide appropriate answers and encouraging messages according to the emotional state of the user when the patient seeks information on a specific question.

[1584] "Past treatment experiences" are recorded information about the experiences, progress, and results of patients who have received treatment.

[1585] A "database" is a system for efficiently searching, storing, managing, and updating information.

[1586] A "natural language processing engine" is a program that analyzes the language that humans use on a daily basis and understands its meaning.

[1587] "Questions from users" are text messages entered by users of the system expressing specific questions or concerns about treatment.

[1588] An "emotion analysis engine" is a program that determines a user's emotional state (e.g., anxiety, joy, sadness, etc.) from text data and voice data and identifies those emotions.

[1589] A "search means" is a function or program for finding desired information from a database.

[1590] "Adjustment" refers to the functions or processes that modify or optimize the search results based on the user's emotional state.

[1591] "Means for displaying messages" refers to functions and interfaces for presenting search results and adjusted information so that users can visually confirm them.

[1592] The present invention provides a system for patients suffering from serious illnesses such as cancer to obtain treatment testimonials, and by combining this system with an emotion analysis engine, it is possible to recognize the emotions of users and provide more appropriate information. Specific embodiments of this invention are described below.

[1593] Overall system configuration

[1594] The system consists of a user's device, a server, a database, and an emotion analysis engine. Users input questions using their device, and the server analyzes and processes the questions to provide appropriate treatment experiences. The emotion analysis engine also recognizes the user's emotional state and adjusts search results and encouraging messages accordingly.

[1595] Hardware and software used

[1596] User devices: PCs, smartphones, tablets, etc.

[1597] Server: High-performance computer (such as a cloud server)

[1598] Database: MySQL, MongoDB, etc.

[1599] Natural language processing engines: spaCy, Google NLP API, etc.

[1600] Sentiment analysis engine: IBM Watson Tone Analyzer, etc.

[1601] Data processing and calculation

[1602] 1. The user enters a question

[1603] The user uses the device to enter a question about treatment in text format, for example, "What are the side effects of radiation therapy?" Voice input is also possible.

[1604] 2. Send the question to the server

[1605] The device converts the question entered by the user into JSON format and sends it to the server. For example, the following HTTP POST request is sent:

[1606] json

[1607] {

[1608] "question": "What are the side effects of radiation therapy?"

[1609] }

[1610] 3. Analyze the question using natural language processing

[1611] The server passes the received question to a natural language processing engine for analysis. During the analysis process, keywords are extracted to understand the intent of the question, and the keywords "radiation therapy" and "side effects" are identified.

[1612] 4. Recognize user emotions

[1613] The server uses a sentiment analysis engine to analyze the user's emotional state, determining their feelings from the context of the text, punctuation, and vocabulary, and recognizing emotions such as anxiety or worry.

[1614] 5. Generate appropriate answers based on the analysis results

[1615] The server searches a database based on keywords obtained from a natural language processing engine and emotional states obtained from an emotion analysis engine, extracts relevant information from a database storing past treatment experiences, and generates an answer that takes emotions into consideration.

[1616] 6. Send the answer to the user's device

[1617] The server generates a response and sends it to the user's device in JSON format, such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to help."

[1618] 7. Show the answer to the user

[1619] The terminal converts the answers received from the server into a display format and provides it to the user. The user can check the answers displayed on the screen and obtain information about treatment.

[1620] Specific examples

[1621] Example 1:

[1622] The user types a question such as, "What are the side effects of chemotherapy?" The device sends the question to the server, which then uses a natural language processing engine to analyze "chemotherapy" and "side effects." The sentiment analysis engine recognizes the user's anxiety and searches the database based on the results. For example, a response such as, "Many patients experience nausea and fatigue, but don't worry, your medical team is always here to support you" is generated and displayed on the user's device.

[1623] Example 2:

[1624] A user inputs a question such as, "I would like some advice on lifestyle after radiation therapy." The server uses a natural language processing engine to analyze "after radiation therapy" and "advice on lifestyle," and the sentiment analysis engine recognizes that the user is seeking encouragement. For example, a response such as, "It is important to get plenty of rest after radiation therapy. Light exercise and a balanced diet are also recommended. Do your best to enjoy life after treatment," is generated and displayed on the user's device.

[1625] This allows users to obtain the necessary treatment information while reducing their mental burden. By combining it with an emotion analysis engine, it is possible to provide support that takes into consideration the user's emotions.

[1626] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1627] Step 1:

[1628] The user inputs a question into the terminal. The user inputs questions or concerns about treatment in text format and presses the send button. Specific actions include the user typing "What are the side effects of radiation therapy?" into the keyboard. The input is text data.

[1629] Step 2:

[1630] The device sends the question to the server. The device converts the question entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, the following JSON data is sent to the server:

[1631] json

[1632] {

[1633] "question": "What are the side effects of radiation therapy?"

[1634] }

[1635] The input is the user question and the output is JSON data.

[1636] Step 3:

[1637] The server passes the question to a natural language processing engine for analysis. The server analyzes the received JSON data and extracts keywords to understand the intent of the question. Specifically, the server uses a natural language processing engine (e.g., spaCy) to extract the keywords "radiation therapy" and "side effects." The input is JSON data, and the output is a list of keywords.

[1638] Step 4:

[1639] The server passes the text to a sentiment analysis engine to recognize the user's emotional state. The server uses keywords to pass the text entered by the user to a sentiment analysis engine (for example, IBM Watson Tone Analyzer) to analyze the emotional state. Specifically, emotions such as anxiety or worry are identified from the context and punctuation of the text. The input is the user's question text, and the output is information about the emotional state.

[1640] Step 5:

[1641] The server searches the database based on the analysis results and generates an appropriate answer. The server searches the database using keywords obtained from the natural language processing engine and the emotional state obtained from the sentiment analysis engine. Specifically, it extracts personal stories related to "radiation therapy" and "side effects" from the database (for example, MySQL) and generates an answer that takes emotions into consideration. The input is a list of keywords and emotional state information, and the output is a tailored answer.

[1642] Step 6:

[1643] The server sends the generated response to the user's device. The server converts the generated response into JSON format and sends it to the user's device as an HTTP response. Specifically, the following JSON data is sent to the device:

[1644] json

[1645] {

[1646] "answer": "Many patients report dry skin and fatigue. However, rest assured that medical staff are always available to help."

[1647] }

[1648] The input is the tailored answer and the output is JSON data.

[1649] Step 7:

[1650] The device displays the answer received from the server. The device parses the received JSON data and displays it in a format that is easy for the user to understand. Specifically, it converts it into HTML or app UI components and displays it on the user's screen. The input is the JSON data received from the server, and the output is information that can be displayed visually. The user can check this displayed answer and obtain the information they need.

[1651] (Application example 2)

[1652] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1653] Patients suffering from serious illnesses such as cancer are prone to anxiety and stress when collecting treatment-related stories and information. Furthermore, when the information provided is general, it may not be tailored to the individual patient's feelings and circumstances, making it difficult to provide appropriate support. Furthermore, because traditional methods of collecting treatment information do not include psychological support, even if patients obtain the necessary information, it is difficult to alleviate the psychological burden. To address these issues, a system is needed that provides information on treatment stories while also providing an interface that takes patients' feelings into consideration.

[1654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1655] In this invention, the server includes means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing user emotions using an emotion engine, means for generating answers using a generative AI model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display, thereby enabling the provision of specific, emotion-sensitive information about treatment.

[1656] The "database" is an information management system that systematically stores past treatment experiences and allows them to be quickly searched and retrieved as needed.

[1657] A "natural language processing engine" is a technology that analyzes text data entered by a user to extract keywords and understand the context.

[1658] An "emotion engine" is a technology that analyzes the user's emotional state from input data and generates an appropriate response.

[1659] A "generative artificial intelligence model" is an artificial intelligence technology that automatically generates text and information based on input data.

[1660] A "head-mounted display (HMD)" is a display device worn on the head, and is hardware that displays a virtual space to the user.

[1661] A "virtual space" is a virtual three-dimensional space generated by a computer that users can experience interactively.

[1662] A system for carrying out this invention includes the following elements: means for storing past treatment experiences in a database, means for analyzing questions from users using a natural language processing engine, means for analyzing the user's emotions using an emotion engine, means for generating answers using a generative artificial intelligence model based on the analyzed emotions, and means for displaying the answers to the user in a virtual space via a head-mounted display.

[1663] First, the user puts on a head-mounted display (HMD) and enters a virtual cafe-like environment. When the user enters questions or concerns about treatment in text format, the questions are sent from the HMD to the server. The submitted questions are passed to the server as an HTTP POST request.

[1664] The server first analyzes the received question using a natural language processing engine. The natural language processing engine extracts keywords from the question and understands its intent. Next, the server uses an emotion engine to analyze the user's emotional state from the input text data. This analysis determines whether the user is anxious or seeking encouragement.

[1665] The server then searches a database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. The database stores past treatment experiences and can search for relevant experiences and advice. For example, it searches for experiences related to "radiation therapy" and "side effects" and generates answers tailored to the results of the emotion engine.

[1666] Finally, the server generates a response that is sent to the user's head-mounted display and displayed in the virtual space. This allows the user to obtain information about treatment in a relaxing environment that feels like being in a real cafe. Furthermore, by combining it with an emotion engine, support that takes the user's emotions into consideration is also possible.

[1667] As a concrete example, consider the case where a user inputs the question, "What are the side effects of radiation therapy?" The server analyzes this question and extracts the keywords "radiation therapy" and "side effects." The emotion engine recognizes the user's anxiety and searches the database based on the results. The answer generated is, "Many patients report dry skin and fatigue. However, don't worry, medical staff are always available to support you," and is displayed on the user's head-mounted display.

[1668] Example prompt sentence:

[1669] "What are the side effects of radiation therapy?"

[1670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1671] Step 1:

[1672] The user puts on a head-mounted display and enters a virtual space (virtual cafe). They input specific questions or concerns in text format. The input data includes questions typed by the user, such as "What are the side effects of radiation therapy?" After completing the input, the user presses the "Submit" button. The output is the question data in text format.

[1673] Step 2:

[1674] The device sends the question entered by the user to the server as an HTTP POST request. The entered question is converted to JSON format and sent to the server. The input is the user's question data, and the output is an HTTP request to the server. For example, a JSON-formatted request would look like this: { "question": "What are the side effects of radiation therapy?"}

[1675] Step 3:

[1676] The server passes the received question data to a natural language processing engine (NLP engine) to analyze the question. First, it extracts keywords from the question, and then performs context understanding. For example, it extracts the keywords "radiation therapy" and "side effects" from the question. The input is the question data in the HTTP POST request, and the output is the analyzed keywords.

[1677] Step 4:

[1678] The server uses an emotion engine to analyze the user's emotional state from their input. The input is the question data itself, but the emotion engine analyzes the context, wording, and punctuation to determine the user's emotional state. For example, the emotion "anxiety" may be recognized. The input is the question text, and the output is the emotional state (e.g., "anxious").

[1679] Step 5:

[1680] The server searches the database based on keywords obtained from the natural language processing engine and emotional states obtained from the emotion engine. It searches the database of past treatment experiences and extracts the most relevant experiences. The input is keywords and emotional states, and the output is the relevant treatment experience.

[1681] Step 6:

[1682] The server generates an appropriate answer using a generative artificial intelligence model (generative AI model) based on the analyzed keywords and emotional state. For example, it generates an answer such as, "Many patients report dry skin and fatigue. However, please rest assured that medical staff are always available to provide support." The input is the relevant treatment experience and emotional state, and the output is the tailored answer text.

[1683] Step 7:

[1684] The server converts the generated answer into JSON format and sends it to the user's head-mounted display. The input is the generated answer text, and the output is the HTTP response sent to the device.

[1685] Step 8:

[1686] The device parses the received answer and displays it in the virtual space via the user's head-mounted display. For example, the user can view the answer while relaxing in the virtual space. The input is the answer data from the server, and the output is the answer displayed on the HMD display screen.

[1687] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1688] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1689] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1690] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1691] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1692] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1693] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1694] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1695] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1696] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1697] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1698] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1699] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1700] 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.

[1701] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1702] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1703] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1704] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1705] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1706] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1707] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1708] The following is further disclosed regarding the above embodiment.

[1709] (Claim 1)

[1710] A means of storing past treatment experiences in a database;

[1711] a means for searching the database for treatment experiences;

[1712] A means for analyzing a question from a user using a natural language processing engine;

[1713] means for operating the search means based on the analysis results;

[1714] means for displaying search results from said database to a user;

[1715] A system including:

[1716] (Claim 2)

[1717] 2. The system according to claim 1, wherein the question from the user is input as text data.

[1718] (Claim 3)

[1719] 2. The system according to claim 1, wherein the natural language processing engine extracts keywords and searches a database based on the keywords.

[1720] "Example 1"

[1721] (Claim 1)

[1722] A means for inputting a question from a user as text data;

[1723] A means of converting input text data into JSON format and sending it;

[1724] A means for passing the received question to a natural language processing engine for analysis;

[1725] means for extracting keywords from the analysis results;

[1726] a means for searching a database based on the extracted keywords;

[1727] A means of generating appropriate answers based on search results;

[1728] means for transmitting the generated answer to a user terminal;

[1729] means for displaying the submitted answers to the user;

[1730] A system including:

[1731] (Claim 2)

[1732] The system of claim 1, wherein the natural language processing engine uses a generative AI model to analyze questions and extract keywords.

[1733] (Claim 3)

[1734] 10. The system of claim 1, wherein the user terminal receives the answer and displays it to the user.

[1735] "Application Example 1"

[1736] (Claim 1)

[1737] A means of storing past treatment experiences in a database;

[1738] a means for searching the database for treatment experiences;

[1739] A means for analyzing a question from a user using a natural language processing engine;

[1740] means for operating the search means based on the analysis results;

[1741] means for displaying search results from said database to a user;

[1742] means for presenting product information relevant to the user based on the search results;

[1743] A system including:

[1744] (Claim 2)

[1745] 2. The system according to claim 1, wherein the question from the user is input as text data.

[1746] (Claim 3)

[1747] 2. The system according to claim 1, wherein the natural language processing engine extracts keywords and searches a database based on the keywords.

[1748] "Example 2: Combining Emotion Engines"

[1749] (Claim 1)

[1750] A means of storing past treatment experiences in a database;

[1751] a means for searching the database for treatment experiences;

[1752] A means for analyzing questions from users using a natural language processing engine;

[1753] means for operating the search means based on the analysis results;

[1754] means for recognizing a user's emotional state using an emotion analysis engine;

[1755] means for adjusting the search results based on the recognition results;

[1756] means for displaying search results from said database and sentiment-sensitive messages to a user;

[1757] A system including:

[1758] (Claim 2)

[1759] 2. The system according to claim 1, wherein the question from the user is input as text data.

[1760] (Claim 3)

[1761] 2. The system according to claim 1, wherein the natural language processing engine extracts keywords and searches a database based on the keywords.

[1762] "Application example 2 when combining emotion engines"

[1763] (Claim 1)

[1764] A means of storing past treatment experiences in a database;

[1765] a means for searching the database for treatment experiences;

[1766] A means for analyzing a question from a user using a natural language processing engine;

[1767] means for operating the search means based on the analysis results;

[1768] means for displaying search results from said database to a user;

[1769] means for analyzing a user's emotions using an emotion engine;

[1770] A means for generating an answer using a generative artificial intelligence model based on the analyzed emotions;

[1771] a means for displaying the answer to the user in a virtual space via a head-mounted display;

[1772] A system including:

[1773] (Claim 2)

[1774] 2. The system according to claim 1, wherein the question from the user is input as text data.

[1775] (Claim 3)

[1776] 2. The system according to claim 1, wherein the natural language processing engine extracts keywords and searches a database based on the keywords. [Explanation of symbols]

[1777] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of storing past treatment experiences in a database; a means for searching the database for treatment experiences; A means for analyzing a question from a user using a natural language processing engine; means for operating the search means based on the analysis results; means for displaying search results from said database to a user; A system including:

2. 2. The system according to claim 1, wherein the question from the user is input as text data.

3. 2. The system according to claim 1, wherein the natural language processing engine extracts keywords and searches a database based on the keywords.

Images