system

Abstract

Provide a system. [Solution] and how to gather information from the hospital's official website. How to collect information from review sites, A means of collecting medical data held by medical institutions; a means for analyzing the collected information to generate reputation scores, median prices, and medical statistics; means for querying the database based on user search criteria and filtering information that meets the criteria; means for providing the filtered information 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] Currently, pregnant women must gather a large amount of information to select the appropriate maternity hospital, but this information gathering is extremely time-consuming. Furthermore, the information on official hospital websites varies, and review sites have a limited number of samples, resulting in a lack of reliability and comprehensiveness. Furthermore, it is difficult to compare multiple evaluation criteria, such as the reputation of each hospital and doctor, fees, and facility facilities. To solve these issues, a system is needed that collects and analyzes comprehensive and reliable information to enable users to easily select the appropriate maternity hospital. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting information from official hospital websites, review sites, and medical data held by medical institutions. The system analyzes the collected information and generates evaluation scores, median fees, and medical statistical information. The system also includes a means for querying a database based on a user's search criteria and filtering information that matches the criteria. The system also includes a means for providing the filtered information to the user, allowing the user to quickly and easily select a maternity hospital that meets their criteria. As a result, the user's information gathering effort is significantly reduced, and the appropriate selection can be made based on reliable information.

[0006] Below are definitions of important terms included in the claims.

[0007] "Hospital" refers to any facility that provides medical care, and specifically includes obstetrics and gynecology departments where births and medical treatment are performed.

[0008] "Official website" refers to an information page on the Internet officially operated by a hospital or medical institution.

[0009] A "review site" refers to a website where users can post their experiences and reviews and other users can view them.

[0010] "Medical data" refers to past medical records and statistical information held by medical institutions, including the number of births, delivery times, and doctor experience.

[0011] "Collection methods" refers to the processes and techniques used to obtain the required data from the source.

[0012] "Means of analysis" refers to the technologies and algorithms used to convert collected data into an appropriate format and generate statistics and evaluation scores.

[0013] A "reputation score" refers to a numerical evaluation of a hospital or doctor calculated from review sites and other evaluation data.

[0014] "Median price" refers to a statistic calculated by collecting multiple price data and calculating the median.

[0015] "Medical statistical information" refers to statistical information such as the number of births, delivery time, and doctor experience level generated based on medical data.

[0016] "Search Criteria" refers to the specific criteria or requests that a User enters into the System, including location, price, ratings, etc.

[0017] "Means of querying a database" refers to the techniques and processes that extract relevant information from a database based on a user's search criteria.

[0018] "Filtering means" refers to the technology that selects data that meets the user's criteria based on information extracted from a database.

[0019] "Means of delivery" refers to the technology or process by which the filtered information is presented in a form accessible to the user.

[0020] "User" refers to an individual who uses the system to select a maternity hospital. [Brief explanation of the drawings]

[0021] [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 showing 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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, and provides the user with the analysis results to help them select an appropriate maternity hospital.

[0043] The system has three main components: the server, the terminal, and the user. The server has the main computing function of collecting and analyzing information, and the terminal provides the interface through which the user can access the system. The user accesses the system through the terminal and obtains the information they need.

[0044] Server Operation

[0045] Data collection

[0046] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries, delivery times, and doctor experience from medical institution databases.

[0047] Data analysis

[0048] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes the medical data to generate medical statistics such as the number of deliveries, average delivery time, and success rate for each doctor.

[0049] Providing information

[0050] The server queries the database based on the search criteria entered by the user through the device, filters out information that matches the criteria, and formats the filtered information in JSON format or similar before sending it to the device.

[0051] Device behavior

[0052] User Interface

[0053] The terminal provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The information received from the server is formatted into an easy-to-read format (e.g., a list or graph) by the terminal and displayed to the user.

[0054] User Actions

[0055] Search and Select

[0056] Users access the system using a web browser on their device or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. The search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0057] Specific examples

[0058] Example 1: Collecting data from a hospital's official website

[0059] The server accesses the official website of "Hospital A" and scrapes pricing information from the HTML of the page. The extracted pricing data is temporarily stored and used for analysis.

[0060] Example 2: Review analysis

[0061] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0062] Example 3: Analysis of medical data

[0063] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0064] Example 4: Search and display

[0065] Using a device, the user enters the search criteria "maternity hospitals in Shibuya Ward with a good reputation and average fees." The device sends this information to the server, which then queries a database for maternity hospitals that meet the criteria. The device analyzes the results sent from the server and displays them to the user as a list. The user can use this list to check the detailed information of each maternity hospital and select the one that best suits them.

[0066] According to the embodiment of the present invention, the user can quickly obtain reliable information and easily select an appropriate maternity hospital, which is expected to have the effect of significantly reducing the burden on the user.

[0067] The processing flow will be explained below.

[0068] Step 1: Data collection (hospital official website)

[0069] The server accesses the official website of each hospital based on the preset list.

[0070] The server scrapes HTML data from pricing information pages and facility introduction pages.

[0071] The server stores the scraped raw data in a temporary folder for analysis.

[0072] Step 2: Data collection (review sites)

[0073] The server accesses the APIs of major review sites to obtain review data about specific hospitals and doctors.

[0074] The server formats the acquired word-of-mouth data and stores it in a database.

[0075] Step 3: Data collection (medical data)

[0076] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[0077] The server normalizes this data and stores it in a database.

[0078] Step 4: Analyze review data

[0079] The server analyzes the collected word-of-mouth data using natural language processing technology.

[0080] The server extracts the positive or negative ratings from each review and calculates an overall reputation score.

[0081] The server stores the calculated reputation scores in a database.

[0082] Step 5: Analyze pricing data

[0083] The server analyzes the fee information collected from the hospital.

[0084] The server calculates the median and variance of the charges and calculates the standard charge based on this.

[0085] The server stores the calculated fee information in a database.

[0086] Step 6: Analyze medical data

[0087] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[0088] The server stores the generated statistics in a database.

[0089] Step 7: Receiving a search request

[0090] Users access the search screen using their device's web browser or a dedicated app.

[0091] The user enters search criteria such as area, price, reputation score, etc. and presses the search button.

[0092] The terminal transmits the input search conditions to the server.

[0093] Step 8: Filtering data based on conditions

[0094] The server queries the database based on the user's search criteria.

[0095] The server extracts data on hospitals and doctors that meet the criteria and generates a list.

[0096] Step 9: Generate a response with search results

[0097] The server converts the extracted data into an appropriate format, such as JSON.

[0098] The server transmits the converted data to the terminal.

[0099] Step 10: Displaying search results

[0100] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (such as a list or graph).

[0101] The user selects the most suitable maternity hospital based on the displayed information.

[0102] The above are the specific processing steps in this system.

[0103] Example 1

[0104] 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."

[0105] Conventional medical information systems often lack the reliability and comprehensiveness of their information, and are unable to provide sufficient support for users in selecting appropriate medical institutions. Furthermore, they have problems such as difficult-to-use user interfaces and insufficient information analysis, making it difficult for users to interpret the information. As a result, users may end up selecting an inappropriate medical institution, which can lead to anxiety and dissatisfaction.

[0106] 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.

[0107] In this invention, the server includes: means for collecting information from official hospital websites; means for collecting information from review sites; means for collecting medical data held by medical institutions; means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information; means for querying a database based on user search criteria and filtering information that matches the criteria; means for providing the filtered information to the user; and means for the terminal to provide a graphical user interface for inputting search criteria, inputting the user's desired criteria, and displaying search results. This allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution. Furthermore, the easy-to-use user interface allows users to easily input and view the information they need.

[0108] An "official hospital website" is an official internet page provided by a particular medical institution, which is intended to provide information about that medical institution.

[0109] A "word-of-mouth site" is a platform where users can post their own experiences and opinions and share them with other users, and in particular, it collects ratings and reviews of medical institutions and doctors.

[0110] "Medical data held by medical institutions" refers to specific information about medical treatment collected and accumulated by medical institutions, including data such as the number of patients, treatment details, and treatment results.

[0111] "Information collection means" refers to the techniques and methods used to obtain the required information from designated websites and databases.

[0112] "Means for analyzing information" refers to the technology or method for processing collected data and calculating specific indicators (e.g., reputation score, median price, medical statistics).

[0113] A "reputation score" is an evaluation value calculated based on word-of-mouth data, etc., and numerically indicates the quality and reliability of medical institutions and doctors.

[0114] "Median price" is a median calculated based on collected price data and indicates the average cost of the medical service in question.

[0115] "Medical statistical information" refers to statistical indicators calculated based on medical data, and includes specific data such as the number of past births, average birth times, and doctors' success rates.

[0116] "Means of querying a database based on search criteria" refers to the technology or method of searching for information in a database based on criteria specified by the user and extracting the relevant data.

[0117] "Filtering means" refers to the technology or method for sorting out information that matches the user's criteria from search results and extracting only the necessary information.

[0118] "Means for providing to the user" refers to the technology and method for presenting information to the user through a terminal or display device.

[0119] A "graphical user interface" is an interface that provides a screen format that is visually easy for users to operate, and allows them to easily input search conditions and display results.

[0120] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used for sentiment analysis of word-of-mouth data and calculating reputation scores.

[0121] "Statistical analysis tools" refers to software or programs used to analyze numerical data and calculate statistical information such as the mean, median, and distribution.

[0122] A "server" refers to a primary computer that provides services to other computers and terminals on a network, and in this system plays a central role in collecting, analyzing, and providing data.

[0123] "Terminal" refers to a device that a user uses to access and operate the system, including a personal computer or smartphone.

[0124] "User" refers to an individual or organization that uses this system to obtain information and select an appropriate medical institution.

[0125] MODE FOR CARRYING OUT THE INVENTION

[0126] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, analyzes the information, and provides it to users to help them select an appropriate medical institution. The system has three main components: a server, a terminal, and a user.

[0127] Server Operation

[0128] Data collection

[0129] The server accesses the official website of a pre-configured hospital and sends an HTTP request to retrieve the HTML of the page. This is done using a scraping tool such as Python's BeautifulSoup. Specific HTML tags (e.g., " 」、「 "). It also uses the API of the specified review site to send an HTTP request and obtain review data about hospitals and doctors in JSON format. It also executes SQL queries against the medical database held by the medical institution to obtain data such as the number of deliveries, delivery times, and doctor experience.

[0130] Data analysis

[0131] The server analyzes the collected data. First, it uses Python's natural language processing library (e.g., NLTK, spaCy) to analyze the review data and calculate a reputation score. It then performs a sentiment analysis on each review and calculates the frequency of positive and negative words. Next, it uses Pandas to perform statistical analysis on the fee data and calculates the median and average. For the medical treatment data, it uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate.

[0132] Providing information

[0133] The server queries the database based on the search criteria entered by the user through the device, filters the information that matches the criteria, and then formats the filtered information into JSON format and sends it to the device.

[0134] Device behavior

[0135] User Interface

[0136] The terminal provides a graphical user interface for users to enter search criteria. A form is created using HTML and JavaScript (registered trademark) on a web browser, allowing users to enter criteria such as the desired area, price, and reputation score. When the user presses the search button, the information is sent to the server as an HTTP request. The terminal formats the information received from the server as a list or graph and displays it to the user. This is done using JavaScript and libraries such as D3.js.

[0137] User Actions

[0138] Search and Select

[0139] Users access the system using a personal computer or smartphone, enter the necessary search criteria through the interface provided on the browser, and click the search button. The search results are displayed, and the user can select the most suitable medical institution based on the results.

[0140] Specific examples

[0141] Example 1: Collecting data from a hospital's official website

[0142] The server accesses the official website of the "specified hospital" and uses BeautifulSoup to scrape the fee information from the HTML. For example, or Text information is extracted from the data, temporarily stored, and used for analysis.

[0143] Example 2: Review analysis

[0144] The server retrieves review data about a "certain obstetrics and gynecology clinic" through the API of a "specific review site." The retrieved JSON data is analyzed using Python's NLTK, and sentiment analysis is performed to calculate a reputation score. For example, each review is tokenized and a score is calculated based on the frequency of positive and negative words.

[0145] Example 3: Analysis of medical data

[0146] The server accesses the medical institution's medical database and retrieves data on deliveries handled by a specific doctor over the past year. The retrieved data on the number of deliveries and delivery times is analyzed using Pandas to calculate average values ​​and success rates.

[0147] Example 4: Search and display

[0148] The user enters the search criteria "maternity hospitals with a good reputation and average fees in a specific area." The device sends this information to the server, which queries a database for medical institutions that meet the criteria. The device analyzes the resulting information and displays it to the user in a list format. The user can then use this list to check the details of each medical institution and select the location that best suits them.

[0149] Prompt Sentence Examples

[0150] "I'm looking for a maternity hospital with a good reputation and average prices in a specific area. Please provide details on pricing, number of deliveries, delivery times, and reputation scores."

[0151] The present invention provides an easy-to-use user interface that allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Step 1: Data collection

[0154] The server sends an HTTP request to the official website of a pre-listed hospital and retrieves the HTML content. Specifically, it uses Python's BeautifulSoup to extract specific HTML tags (e.g., " 」、「 "). It also sends an HTTP request to the API of the specified review site to obtain the review data in JSON format. It also executes an SQL query to the medical institution's medical database to obtain data such as the number of deliveries, delivery time, and doctor's experience. This input data is temporarily saved and used for later analysis.

[0155] Step 2: Data analysis

[0156] The server analyzes the data collected in step 1. First, it uses natural language processing tools (e.g., NLTK, spaCy) to perform sentiment analysis on the review data and calculate a reputation score. Specifically, it tokenizes positive and negative words and calculates their occurrence rate. Next, it converts the fee data into a Pandas data frame and calculates the median and mean. For the medical treatment data, it also uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate. These analysis results are output as JSON format data.

[0157] Step 3: Enter search criteria

[0158] The terminal provides a graphical user interface for users to input search criteria. Specifically, a form is created using HTML and JavaScript, and users are prompted to input search criteria such as area, price, and reputation score. The search criteria entered by the user are sent to the server as an HTTP request. The input data is in text or numeric format, and is used as search criteria for subsequent query processing.

[0159] Step 4: Execute a database query

[0160] The server executes a query to the database based on the user's search criteria received in step 3. Specifically, it generates and executes an SQL query to filter information on medical institutions that match the criteria. This filtering extracts matching information, formats it in JSON format, and sends it to the terminal. The query input is the search criteria, and the output is information on medical institutions that match the criteria.

[0161] Step 5: Viewing search results

[0162] The device analyzes the filtered information received from the server and displays it in a format that is easy for the user to view. Specifically, it uses JavaScript and D3.js to display search results as lists and graphs. The user can scroll through these results to check detailed information about each medical institution. The displayed information includes the name of the medical institution, reputation score, median fee, and statistical information on the number of deliveries and delivery times, and the user can select the most suitable medical institution based on this information.

[0163] Step 6: Select a medical institution

[0164] The user selects the most suitable medical institution based on the information displayed on the device. Specifically, by clicking on each entry in the list or graph, the user can check detailed information and narrow down the options. The information selected by the user is sent to the server and saved as the final selection result. This selection process allows the user to find the medical institution that is most suitable for them.

[0165] Through the above steps, users can efficiently obtain information on reliable medical institutions and select the location that is best suited to them.

[0166] (Application example 1)

[0167] 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."

[0168] Conventional hospital selection support systems are limited to collecting and analyzing information from limited data sources and are unable to improve the efficiency of large-scale production facilities and factories. The objective of this invention is to apply the usefulness of hospital selection support systems to factories and achieve improved production efficiency. Specifically, the objective is to maximize the operating efficiency of the entire factory by collecting and analyzing production line data and providing users with optimal production settings.

[0169] 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.

[0170] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information, means for querying a database based on a user's search criteria and filtering information that matches the criteria, means for providing the filtered information to the user, means for collecting production data from sensors in a factory, means for analyzing the collected data and calculating production efficiency and error rates, and means for providing optimal production line settings based on the analysis results. This makes it possible to apply the technology of the hospital selection support system to improving factory production efficiency.

[0171] "Means for collecting information from the hospital's official website" refers to technologies and methods for automatically obtaining fee information and facility information published on the hospital's official website.

[0172] "Means of collecting information from review sites" refers to technologies and methods for automatically obtaining data about hospitals and doctors from review sites that aggregate user experiences and opinions.

[0173] "Means for collecting medical data held by medical institutions" refers to technologies and methods for automatically obtaining information about medical treatment from medical record databases managed by hospitals and clinics.

[0174] "Means for analyzing collected information and generating reputation scores, median fees, and medical statistical information" refers to technologies and methods for analyzing collected data and generating numerical scores for the reputation of hospitals and doctors, median fees, and medical statistical data for each doctor.

[0175] "Means of querying a database based on user search criteria and filtering information that matches the criteria" refers to a technology or method that searches a database according to search criteria specified by the user and extracts information that matches those criteria.

[0176] The "means for providing filtered information to a user" refers to an interface or method for providing information extracted by a query to a user.

[0177] "Means of collecting production data from sensors within the factory" refers to technologies and methods for automatically acquiring production-related data from various sensors installed within the factory.

[0178] "Means for analyzing collected data and calculating production efficiency and error rate" refers to techniques and methods for analyzing data collected from sensors and calculating production efficiency and error rate as a result.

[0179] "Means for providing optimal production line settings based on analysis results" refers to technologies and methods that propose optimal production line settings and improvement measures to users based on the results of data analysis.

[0180] The following describes in detail the mode for carrying out the present invention. This system is designed to optimize production efficiency within a factory and applies the technology of a hospital selection support system. The system has three main components: a server, a terminal, and a user.

[0181] Server Operation

[0182] Data collection

[0183] The server collects production data from various sensors installed in the factory, such as production volume, production speed, error rate, equipment usage, etc. It also collects visual and audio information from cameras and microphones in the factory.

[0184] Data analysis

[0185] The server analyzes the collected data. For example, visual information is analyzed using image analysis software to identify production efficiency and error patterns. This is done using Google® Cloud's BigQuery and AWS® Athena, which analyze the data in real time. Natural language processing (NLP) technology is applied to the collected data, and spacy and NLTK are used to extract problems and areas for improvement from communication logs within the factory.

[0186] Providing information

[0187] The server generates optimal production line settings based on the analysis results and provides this information to the user, using Python's Dash and Tableau to visualize the analysis results and present them to managers as reports.

[0188] Device behavior

[0189] User Interface

[0190] The terminal provides an interface where the user can enter search criteria. For example, when the user enters "Please tell me the optimization settings for the production line next week," the terminal sends this information to the server. The information received from the server is formatted into an easy-to-read format (e.g., list or graph) and displayed to the user.

[0191] User Actions

[0192] Search and Select

[0193] Users access the system using a web browser on their device or a dedicated app, input search criteria such as desired production efficiency and error rate, and adjust the production line based on the optimal settings returned by the server.

[0194] Specific examples

[0195] When a factory wants to optimize the production time of a new production line, the system generates the following prompt:

[0196] "What settings can we use to optimize the average production time and reduce the error rate on our new production line? Please suggest optimal settings taking into account the current production volume, machine usage, and worker communication log data."

[0197] Based on the analysis results of these prompts, the system provides the user with optimal production settings, ensuring efficient operation of the factory's production line. This system allows users to quickly obtain reliable information to improve factory production efficiency.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] Users access the system via a terminal and input search criteria to find the optimal settings for a specific production line. These criteria include requests for improving production efficiency and reducing error rates. The search criteria are then sent from the terminal to the server.

[0201] Input: Search criteria entered by the user

[0202] Output: Sending search criteria to the server

[0203] Step 2:

[0204] The server collects real-time production data from various sensors in the factory, including production volume, production speed, error rate, equipment usage, etc. It also captures visual and audio information from cameras and microphones in the factory.

[0205] Input: Data from various sensors and cameras in the factory

[0206] Output: Acquired production data and visual and audio information

[0207] Step 3:

[0208] The server stores the collected data in a database and prepares it for analysis: production data is stored in a database, visual information is analyzed using image analysis software, and audio information is converted into text using natural language processing technology.

[0209] Input: Collected production data, and visual and audio information

[0210] Output: A dataset ready for analysis

[0211] Step 4:

[0212] The server uses Google Cloud's BigQuery and AWS's Athena to perform real-time analysis of the collected data, extracting important indicators such as production efficiency and error rates. It also uses spacy and NLTK to analyze communication logs and identify problems and areas for improvement.

[0213] Input: A prepared dataset

[0214] Output: Extracted indicators such as production efficiency, error rate, problems and areas for improvement

[0215] Step 5:

[0216] The server generates optimal production line settings based on the analysis results, including revisions to machine operation settings and work procedures. The generated settings along with the analysis results are converted into a format that can be displayed on the user interface.

[0217] Input: Extracted metrics

[0218] Output: Optimal production line settings and data for user interface display

[0219] Step 6:

[0220] The server sends the optimal production line settings to the terminal. The terminal receives this information and displays it in a format that is easy for the user to view (lists and graphs). For example, the dashboard displays trends in production efficiency and error rates, and provides specific improvement suggestions.

[0221] Input: Data for optimal production line configuration and user interface display

[0222] Output: User-friendly formatted display content

[0223] Step 7:

[0224] The user can then confirm and implement the optimal settings for the production line provided via the terminal. Implementing the settings is expected to improve the production efficiency of the entire factory and reduce the error rate. Furthermore, the user can adjust the search conditions again as needed and obtain the optimal settings again.

[0225] Input: Optimal production line settings (display content)

[0226] Output: Implemented optimal settings and increased production efficiency

[0227] 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.

[0228] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[0229] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[0230] Server Operation

[0231] Data collection

[0232] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries and delivery times in the past, and the experience of each doctor, from medical institution databases.

[0233] Data analysis

[0234] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes medical data to generate medical statistics such as the number of deliveries per doctor, average delivery time, and success rate.

[0235] Emotion recognition by emotion engine

[0236] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, input text, etc. while using the system. Based on this emotional data, it determines whether the user is relaxed, anxious, or impatient.

[0237] Providing information

[0238] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is very anxious, it will prioritize recommending highly rated hospitals that will give them a sense of security. The filtered information is then formatted in JSON format or similar and sent to the device.

[0239] Device behavior

[0240] User Interface

[0241] The device provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The device then formats the information received from the server into an easy-to-read format (e.g., list or graph) and displays it to the user. The device also uses a camera to recognize the user's facial expressions and a microphone to analyze their voice, collecting emotional data about the user.

[0242] User Actions

[0243] Search and Select

[0244] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0245] Specific examples

[0246] Example 1: Collecting data from a hospital's official website

[0247] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[0248] Example 2: Review analysis

[0249] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0250] Example 3: Analysis of medical data

[0251] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0252] Example 4: Emotional awareness and adaptation

[0253] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that are committed to providing a relaxing environment.

[0254] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[0255] The processing flow will be explained below.

[0256] Step 1: Data collection (hospital official website)

[0257] The server accesses the official website of each hospital based on a pre-configured list.

[0258] The server scrapes HTML data from pricing information pages and facility introduction pages.

[0259] The server temporarily stores the scraped raw data.

[0260] Step 2: Data collection (review sites)

[0261] The server obtains review data about specific hospitals and doctors through the APIs of major review sites.

[0262] The server formats the acquired word-of-mouth data and stores it in a database.

[0263] Step 3: Data collection (medical data)

[0264] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[0265] The server converts the collected data into a standard format and stores it in a database.

[0266] Step 4: Analyze review data

[0267] The server analyzes the stored word-of-mouth data using natural language processing technology.

[0268] The server extracts positive or negative ratings from each review and aggregates them to calculate an overall reputation score.

[0269] The server stores the calculated reputation scores in a database.

[0270] Step 5: Analyze pricing data

[0271] The server analyzes the fee information collected from the hospitals.

[0272] The server calculates the median and variance of each fee item and generates basic data for calculating the standard fee.

[0273] The server stores the calculated fee data in a database.

[0274] Step 6: Analyze medical data

[0275] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[0276] The server stores the calculated statistical information in a database.

[0277] Step 7: Collect emotion data

[0278] The device captures the user's facial expression data through a camera.

[0279] The terminal uses a microphone to collect voice data of the user.

[0280] The device analyzes the collected facial and voice data in real time to determine the user's emotional state.

[0281] Step 8: Receiving a search request

[0282] Users access the search screen using their device's web browser or a dedicated app.

[0283] The user enters search criteria such as the desired area, price, reputation score, etc., and presses the search button.

[0284] The terminal transmits the user's emotion data to the server together with the input search conditions.

[0285] Step 9: Filtering data based on conditions and sentiment

[0286] The server queries the database based on the user's search criteria and emotional state.

[0287] The server extracts data on hospitals and doctors that match the conditions and emotions and generates a list.

[0288] For example, if the user is in an anxious state, the server will preferentially filter out hospitals that are highly reliable and have a high rating that can alleviate the user's anxiety.

[0289] Step 10: Generate a response with search results

[0290] The server converts the extracted data into an appropriate format, such as JSON.

[0291] The server transmits the converted data to the terminal.

[0292] Step 11: Viewing search results

[0293] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (e.g., list, graph, table).

[0294] The user selects the most suitable maternity hospital and doctor based on the displayed information.

[0295] The above are the specific processing steps in this system.

[0296] Example 2

[0297] 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."

[0298] Conventional maternity hospital recommendation systems do not take into account the user's emotional state when making recommendations, making it difficult for users to select a maternity hospital with confidence. Childbirth is a particularly stressful event for users, so providing appropriate information based on the user's emotional state is essential. Furthermore, the difficulty of incorporating human opinions and emotions into the analysis of collected data limits the improvement of user satisfaction. Therefore, a system that can filter information based on the user's emotional state and provide highly reliable recommendations is needed.

[0299] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median prices, and medical statistical information, means for analyzing the review data using natural language processing technology in analyzing the collected information, means for recognizing user emotions and filtering based on the emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions, and means for providing the filtered information to the user. This allows for more reliable information to be provided quickly, taking the user's emotional state into consideration, enabling the user to select an appropriate maternity hospital with peace of mind.

[0300] An "official hospital website" is an online information site officially operated by a medical institution, where various information such as medical departments, medical staff, facilities, and fees is made public.

[0301] A "review site" is a website where users can post their own experiences and opinions, which can then be viewed and evaluated by other users. Medical review sites often post reviews and ratings of specific medical institutions and doctors.

[0302] "Medical data" refers to patient medical records, medical procedure histories, and data related to the work of doctors and other medical staff held by medical institutions. Examples include the number of deliveries and delivery times in the past, and the experience of each doctor.

[0303] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze natural language, enabling semantic analysis and emotional assessment of text data.

[0304] An "emotion engine" is software or algorithm for determining a user's emotional state, collecting and analyzing emotional data from the user's facial expressions, voice, and input text.

[0305] A "reputation score" is a number calculated from word-of-mouth data and other evaluation data, and indicates the overall evaluation of a particular medical institution or doctor.

[0306] The "median price" is the median value of the collected price data, and is a statistical value that represents the center of the distribution.

[0307] "Medical statistical information" is statistical information generated based on collected medical data, and includes the number of births per doctor, average birth time, success rate, etc.

[0308] "Filtering" is the process of selecting necessary information from collected and analyzed data based on specific criteria.

[0309] "User search conditions" are conditions such as desired area, price, reputation score, etc. that a user inputs when using the system.

[0310] "User emotional state" refers to the psychological state the user experiences when using the system, including relaxation, anxiety, tension, etc.

[0311] "Querying a database" is the operation of making an inquiry to a database to retrieve specific data.

[0312] "User Device" means a device used by a user to access the system and input and retrieve information, including a personal computer, smartphone, tablet, etc.

[0313] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[0314] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[0315] Server Operation

[0316] Data collection

[0317] The server accesses the official websites of pre-configured hospitals and collects information on fees and facilities by scraping. Examples of software used include Python's "Beautiful Soup" and "Scrapy." It also uses the APIs of major review sites to obtain review data on hospitals and doctors. For example, "TrustPilot API" and "Google Places API" are used. Furthermore, data such as the number of past deliveries, delivery times, and the experience of each doctor is obtained from medical institution databases. "SQLAlchemy" and "Pandas" are used to connect to the database.

[0318] Data analysis

[0319] The server analyzes the collected data. For example, natural language processing technology is used to calculate reputation scores from the collected review data. Specific tools include "NLTK" and "spaCy." Statistical processing tools such as "NumPy" and "Pandas" are also used to calculate median prices from fee information. "SciPy" and "Matplotlib" are used to analyze medical data and generate medical statistics.

[0320] Emotion recognition by emotion engine

[0321] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, and input text while using the system. To achieve this, OpenCV is used for facial expression recognition, Google Cloud Speech-to-Text for voice analysis, and BERT for text analysis.

[0322] Providing information

[0323] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is feeling anxious, SQLAlchemy and Flask are used to recognize this state and prioritize recommendations of highly rated hospitals that will give the user a sense of security. The filtered information is formatted in JSON format or similar and sent to the device as an HTTP response.

[0324] Device behavior

[0325] User Interface

[0326] The terminal provides an interface that allows the user to enter search criteria. This interface is built using the web application frameworks "React.js" and "Vue.js." When the user enters criteria and presses the search button, the information is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format (for example, a list or graph) on the terminal and displayed to the user using "Chart.js" or "D3.js."

[0327] The device also has the ability to recognize the user's facial expressions with a camera and analyze their voice using a microphone, thereby collecting emotional data about the user. These functions allow the server to filter and provide the user with information about the most suitable maternity hospitals based on the user's search criteria and emotional state.

[0328] User Actions

[0329] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0330] Specific examples

[0331] Example 1: Collecting data from a hospital's official website

[0332] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[0333] Example 2: Review analysis

[0334] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0335] Example 3: Analysis of medical data

[0336] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0337] Example 4: Emotional awareness and adaptation

[0338] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that offer a relaxing environment.

[0339] Prompt Sentence Examples

[0340] If the user is unsure, prioritize recommending highly rated hospitals that will give them peace of mind. Additionally, provide information on the best maternity hospitals based on the user's search criteria (area, price, reputation score).

[0341] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: Data collection

[0344] The server accesses the hospital's official website and collects pricing and facility information by scraping. A URL is given as input, and HTML data is retrieved and analyzed based on this. Specifically, pricing information is extracted from the HTML using Python's "Beautiful Soup" and "Scrapy" and temporarily stored in a database. Similarly, review data is collected using the APIs of major review sites, and the JSON data obtained from the review sites is analyzed and stored. Furthermore, the server accesses the medical institution's medical database and obtains medical data such as the number of past deliveries and delivery times. SQLAlchemy and Pandas are used to extract the necessary information from the database and temporarily store it. This completes the server's collection of information from the official website, review sites, and medical database.

[0345] Step 2: Data analysis

[0346] The server analyzes the data collected in step 1. The collected data is used as input, and based on this, a reputation score, median price, and medical statistical information are generated. First, the review data is analyzed using natural language processing technology to calculate a reputation score. Specifically, tools such as "NLTK" and "spaCy" are used to extract sentiment from the review text and score it. In addition, to calculate the median price from the fee information, statistical processing of the fee data is performed using "NumPy" and "Pandas." For the medical treatment data, "SciPy" and "Matplotlib" are used to generate medical statistical information such as the number of deliveries per doctor, average delivery time, and success rate. As a result, the server obtains a reputation score, median price, and medical statistical information as the analysis results.

[0347] Step 3: Emotion Recognition

[0348] The server receives input data (facial expressions, voice, and text) sent from the device to recognize the user's emotional data. Specifically, the device captures facial expressions with a camera and performs facial expression analysis using OpenCV. It also collects audio data with a microphone, converts the audio into text using Google Cloud Speech-to-Text, and analyzes the text using a natural language processing model such as BERT. This process identifies the user's emotional state and sends it to the server as emotional data, such as relaxed, anxious, or nervous. This allows the server to recognize the user's emotional state.

[0349] Step 4: Provide information

[0350] The server provides information based on the analysis results obtained in step 2 and the emotional state recognized in step 3. Specifically, it queries the database for matching maternity hospital information using the user's search criteria and emotional state as input. Filtering is performed based on the criteria, and if the user is feeling anxious, for example, it prioritizes recommendations of highly rated hospitals that offer a relaxing environment. The filtered information is formatted into JSON using "SQLAlchemy" and "Flask" and sent to the terminal as an HTTP response. This allows the server to provide the user with appropriate maternity hospital information.

[0351] Step 5: User Interface

[0352] The device provides an interface for users to enter search criteria and display information received from the server. As input, it receives the search criteria entered by the user (area, price, reputation score) and JSON data from the server. Specifically, a search form is built using "React.js" or "Vue.js," and when the search button is pressed, a request is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format using "Chart.js" or "D3.js" and displayed to the user. In addition, by allowing access to the camera and microphone, appropriate emotional data is sent to the server. This allows the device to provide the maternity hospital information searched for by the user in an easy-to-read format.

[0353] (Application example 2)

[0354] 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."

[0355] Conventional maternity hospital recommendation systems and customer service systems provide uniform information and services without considering the user's emotional state, making it impossible to adequately alleviate the user's anxiety and tension. It is also difficult to recognize the user's emotions in real time and provide appropriate responses based on them. This reduces user satisfaction and poses the risk of inappropriate information and customer service being provided.

[0356] 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.

[0357] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating evaluation indexes, median fees, and medical statistical information, means for recognizing customer emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions and emotions, and means for providing the filtered information to the user, thereby enabling appropriate information provision and customer service that takes the user's emotional state into consideration.

[0358] The "method of collecting information from the official hospital website" refers to the method of collecting fee information and facility information from the official internet page of the health institution by scraping.

[0359] "Means for collecting information from review sites" refers to methods for obtaining evaluation data and comments from online review sites.

[0360] "Means for collecting medical data held by medical institutions" refers to methods for extracting past treatment records and doctor experience from medical databases held by medical facilities.

[0361] "Means for analyzing collected information and generating evaluation indexes, median prices, and medical statistical information" refers to a method for processing acquired data using natural language processing and statistical analysis to generate hospital reputation scores, median prices, and medical statistical information.

[0362] "Means for recognizing customer emotions" refers to technology for determining a user's emotional state from their facial expressions and voice.

[0363] "Means for querying a database based on a user's search criteria and emotional state, and filtering information that matches the criteria and emotions" refers to a method for searching a database based on the user's specified criteria and emotions at the time, and extracting appropriate information.

[0364] A "means for providing filtered information to a user" is a method for organizing and displaying selected information on a user's device.

[0365] "Natural language processing" is a technology that allows computers to analyze and understand human language.

[0366] "Evaluation index" is a numerical representation of reviews and reputations of hospital and medical institution services.

[0367] "Median price" refers to the median of a statistical price for a particular service.

[0368] "Medical statistical information" refers to statistical data such as the number of births and average treatment times generated based on medical data.

[0369] A "search condition" indicates a specific attribute or requirement that a user is looking for.

[0370] "Emotional state" refers to the emotions felt by the user when using the system.

[0371] The following describes in detail the mode for carrying out the present invention. The present invention is a system that utilizes an emotion engine that recognizes user emotions, collects and analyzes medical data held by official hospital websites, review sites, and medical institutions, and provides appropriate information based on the user's search criteria and emotional state. It also includes an application example specifically aimed at improving customer service in brick-and-mortar stores.

[0372] Server Operation

[0373] The server operates using the following hardware and software.

[0374] 1. Data Collection

[0375] The server uses scraping technology to collect pricing and facility information from hospitals' official websites, obtains reviews and comments via APIs of review sites, and extracts medical data held by medical institutions from databases.

[0376] 2. Data Analysis

[0377] The collected data is analyzed using natural language processing techniques, such as Python libraries OpenCV and TENSORFLOW (registered trademark), to generate review metrics, median prices, and medical statistics.

[0378] 3. Emotion Recognition by Emotion Engine

[0379] The server analyzes facial expression and voice data sent from the user's device to determine the user's emotional state, using a generative AI model, a machine learning model.

[0380] 4. Information provision

[0381] It queries the database based on the user's search criteria and emotional state, and filters out information that matches the criteria. The filtered information is formatted in JSON format or similar and sent to the user's device.

[0382] Device behavior

[0383] The terminal provides an interface for users to access the system and performs the following functions:

[0384] 1. User Interface

[0385] The terminal has an interface that accepts search criteria input by the user. When the user inputs search criteria and presses the search button, the information is sent to the server.

[0386] 2. Emotion recognition

[0387] The device uses sensors such as smart glasses and a microphone to capture the user's facial expressions and voice to collect emotional data, which is then sent to a server and analyzed by an emotion engine.

[0388] 3. Display results

[0389] The information received from the server is formatted into an easy-to-read format (list, graph, etc.) on the terminal and displayed to the user.

[0390] User Actions

[0391] A user accesses the system using a terminal and operates it in the following manner.

[0392] 1. Search and Select

[0393] Users enter search criteria such as the desired area, price, and reputation score, and then press the search button. By granting permission to access the camera and microphone, the device can collect emotional data.

[0394] Specific examples of hardware and software used

[0395] 1. Hardware

[0396] Smart Glasses

[0397] microphone

[0398] 2. Software

[0399] Python

[0400] OpenCV (face detection)

[0401] TensorFlow (emotion recognition)

[0402] SpeechRecognition

[0403] Examples and prompts

[0404] Specific examples

[0405] Example 1: If a customer's facial expression is recognized as "angry," real-time advice such as "The customer is angry. Please respond calmly" will be displayed on the smart glasses.

[0406] Example 2: When a customer says, "Please tell me more about this product," a template sentence such as "This product is ____ and has ____ features. Please take a look." is returned.

[0407] Prompt Sentence Examples

[0408] "What emotions is the person in this image experiencing?"

[0409] "Tell me what the customer is saying in this audio."

[0410] "Please suggest the best way to serve customers based on the results."

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1:

[0413] The server collects fee and facility information from the hospital's official website by scraping it. At this stage, it accesses the web, obtains HTML data, and extracts the necessary information from specific DOM elements.

[0414] Input: Hospital's official website URL

[0415] Data processing: Parsing HTML documents and extracting data from the DOM

[0416] Output: List of fee information and facility information

[0417] Step 2:

[0418] The server retrieves review and comment data using the review site's API. At this stage, it makes an API request and receives the response data in JSON format. It then extracts the review content and rating score.

[0419] Input: API endpoint and authentication information of the review site

[0420] Data processing: Parsing JSON responses and extracting data from specific fields

[0421] Output: List of reviews and rating scores

[0422] Step 3:

[0423] The server accesses the medical database held by the medical institution and extracts medical data such as treatment records and doctor experience. At this stage, SQL queries are executed to retrieve the necessary data from the database.

[0424] Input: Connection information to medical institution's medical database

[0425] Data processing: Data extraction and aggregation using SQL queries

[0426] Output: List of treatment records and doctor experience data

[0427] Step 4:

[0428] The server analyzes the collected data using natural language processing technology to generate evaluation indicators, median prices, and medical statistics. At this stage, Python libraries are used to perform sentiment analysis on text data and statistical analysis on numerical data.

[0429] Input: Price information, reviews, treatment records

[0430] Data processing: text analysis, statistical calculations

[0431] Output: Evaluation index, median price, medical statistics

[0432] Step 5:

[0433] The server receives facial expression and voice data sent from the user's device and analyzes it using a generative AI model, thereby determining the user's emotional state.

[0434] Input: facial expression data, voice data

[0435] Data Processing: Sentiment Analysis with Generative AI Models

[0436] Output: User's emotional state (e.g., relaxed, anxious, angry)

[0437] Step 6:

[0438] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. At this stage, SQL queries or NoSQL are used to extract data that matches the criteria.

[0439] Input: User search criteria, user emotional state, medical database

[0440] Data processing: conditional filtering, sentiment matching

[0441] Output: A list of matching medical information

[0442] Step 7:

[0443] The server formats the filtered information in JSON format or similar and sends it to the user's device. At this stage, data format conversion and API response generation are performed.

[0444] Input: Filtered information

[0445] Data processing: format conversion, API response generation

[0446] Output: Medical information in JSON format

[0447] Step 8:

[0448] The terminal provides an interface for users to access the system and input search criteria. At this stage, form input and search button operations are accepted.

[0449] Input: User search criteria

[0450] Data processing: Validating search conditions and converting to JSON format

[0451] Output: Sending search criteria to the server

[0452] Step 9:

[0453] The device formats the information received from the server into an easy-to-read format (list, graph, etc.) and displays it to the user. At this stage, data parsing and UI rendering are performed.

[0454] Input: JSON response from the server

[0455] Data Transformation: Parsing data and mapping it to UI elements

[0456] Output: Display of results on the user interface

[0457] Step 10:

[0458] The device uses smart glasses and a microphone to capture the user's facial expressions and voice data, collecting emotional data, which is then sent to a server.

[0459] Input: Camera video, microphone audio

[0460] Data processing: Real-time capture, data compression, server transmission

[0461] Output: Sending facial expression data and voice data to the server

[0462] 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.

[0463] 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.

[0464] 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.

[0465] [Second embodiment]

[0466] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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."

[0478] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, and provides the user with the analysis results to help them select an appropriate maternity hospital.

[0479] The system has three main components: the server, the terminal, and the user. The server has the main computing function of collecting and analyzing information, and the terminal provides the interface through which the user can access the system. The user accesses the system through the terminal and obtains the information they need.

[0480] Server Operation

[0481] Data collection

[0482] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries, delivery times, and doctor experience from medical institution databases.

[0483] Data analysis

[0484] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes the medical data to generate medical statistics such as the number of deliveries, average delivery time, and success rate for each doctor.

[0485] Providing information

[0486] The server queries the database based on the search criteria entered by the user through the device, filters out information that matches the criteria, and formats the filtered information in JSON format or similar before sending it to the device.

[0487] Device behavior

[0488] User Interface

[0489] The terminal provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The information received from the server is formatted into an easy-to-read format (e.g., a list or graph) by the terminal and displayed to the user.

[0490] User Actions

[0491] Search and Select

[0492] Users access the system using a web browser on their device or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. The search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0493] Specific examples

[0494] Example 1: Collecting data from a hospital's official website

[0495] The server accesses the official website of "Hospital A" and scrapes pricing information from the HTML of the page. The extracted pricing data is temporarily stored and used for analysis.

[0496] Example 2: Review analysis

[0497] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0498] Example 3: Analysis of medical data

[0499] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0500] Example 4: Search and display

[0501] Using a device, the user enters the search criteria "maternity hospitals in Shibuya Ward with a good reputation and average fees." The device sends this information to the server, which then queries a database for maternity hospitals that meet the criteria. The device analyzes the results sent from the server and displays them to the user as a list. The user can use this list to check the detailed information of each maternity hospital and select the one that best suits them.

[0502] According to the embodiment of the present invention, the user can quickly obtain reliable information and easily select an appropriate maternity hospital, which is expected to have the effect of significantly reducing the burden on the user.

[0503] The processing flow will be explained below.

[0504] Step 1: Data collection (hospital official website)

[0505] The server accesses the official website of each hospital based on the preset list.

[0506] The server scrapes HTML data from pricing information pages and facility introduction pages.

[0507] The server stores the scraped raw data in a temporary folder for analysis.

[0508] Step 2: Data collection (review sites)

[0509] The server accesses the APIs of major review sites to obtain review data about specific hospitals and doctors.

[0510] The server formats the acquired word-of-mouth data and stores it in a database.

[0511] Step 3: Data collection (medical data)

[0512] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[0513] The server normalizes this data and stores it in a database.

[0514] Step 4: Analyze review data

[0515] The server analyzes the collected word-of-mouth data using natural language processing technology.

[0516] The server extracts the positive or negative ratings from each review and calculates an overall reputation score.

[0517] The server stores the calculated reputation scores in a database.

[0518] Step 5: Analyze pricing data

[0519] The server analyzes the fee information collected from the hospital.

[0520] The server calculates the median and variance of the charges and calculates the standard charge based on this.

[0521] The server stores the calculated fee information in a database.

[0522] Step 6: Analyze medical data

[0523] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[0524] The server stores the generated statistics in a database.

[0525] Step 7: Receiving a search request

[0526] Users access the search screen using their device's web browser or a dedicated app.

[0527] The user enters search criteria such as area, price, reputation score, etc. and presses the search button.

[0528] The terminal transmits the input search conditions to the server.

[0529] Step 8: Filtering data based on conditions

[0530] The server queries the database based on the user's search criteria.

[0531] The server extracts data on hospitals and doctors that meet the criteria and generates a list.

[0532] Step 9: Generate a response with search results

[0533] The server converts the extracted data into an appropriate format, such as JSON.

[0534] The server transmits the converted data to the terminal.

[0535] Step 10: Displaying search results

[0536] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (such as a list or graph).

[0537] The user selects the most suitable maternity hospital based on the displayed information.

[0538] The above are the specific processing steps in this system.

[0539] Example 1

[0540] 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."

[0541] Conventional medical information systems often lack the reliability and comprehensiveness of their information, and are unable to provide sufficient support for users in selecting appropriate medical institutions. Furthermore, they have problems such as difficult-to-use user interfaces and insufficient information analysis, making it difficult for users to interpret the information. As a result, users may end up selecting an inappropriate medical institution, which can lead to anxiety and dissatisfaction.

[0542] 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.

[0543] In this invention, the server includes: means for collecting information from official hospital websites; means for collecting information from review sites; means for collecting medical data held by medical institutions; means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information; means for querying a database based on user search criteria and filtering information that matches the criteria; means for providing the filtered information to the user; and means for the terminal to provide a graphical user interface for inputting search criteria, inputting the user's desired criteria, and displaying search results. This allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution. Furthermore, the easy-to-use user interface allows users to easily input and view the information they need.

[0544] An "official hospital website" is an official internet page provided by a particular medical institution, which is intended to provide information about that medical institution.

[0545] A "word-of-mouth site" is a platform where users can post their own experiences and opinions and share them with other users, and in particular, it collects ratings and reviews of medical institutions and doctors.

[0546] "Medical data held by medical institutions" refers to specific information about medical treatment collected and accumulated by medical institutions, including data such as the number of patients, treatment details, and treatment results.

[0547] "Information collection means" refers to the techniques and methods used to obtain the required information from designated websites and databases.

[0548] "Means for analyzing information" refers to the technology or method for processing collected data and calculating specific indicators (e.g., reputation score, median price, medical statistics).

[0549] A "reputation score" is an evaluation value calculated based on word-of-mouth data, etc., and numerically indicates the quality and reliability of medical institutions and doctors.

[0550] "Median price" is a median calculated based on collected price data and indicates the average cost of the medical service in question.

[0551] "Medical statistical information" refers to statistical indicators calculated based on medical data, and includes specific data such as the number of past births, average birth times, and doctors' success rates.

[0552] "Means of querying a database based on search criteria" refers to the technology or method of searching for information in a database based on criteria specified by the user and extracting the relevant data.

[0553] "Filtering means" refers to the technology or method for sorting out information that matches the user's criteria from search results and extracting only the necessary information.

[0554] "Means for providing to the user" refers to the technology and method for presenting information to the user through a terminal or display device.

[0555] A "graphical user interface" is an interface that provides a screen format that is visually easy for users to operate, and allows them to easily input search conditions and display results.

[0556] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used for sentiment analysis of word-of-mouth data and calculating reputation scores.

[0557] "Statistical analysis tools" refers to software or programs used to analyze numerical data and calculate statistical information such as the mean, median, and distribution.

[0558] A "server" refers to a primary computer that provides services to other computers and terminals on a network, and in this system plays a central role in collecting, analyzing, and providing data.

[0559] "Terminal" refers to a device that a user uses to access and operate the system, including a personal computer or smartphone.

[0560] "User" refers to an individual or organization that uses this system to obtain information and select an appropriate medical institution.

[0561] MODE FOR CARRYING OUT THE INVENTION

[0562] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, analyzes the information, and provides it to users to help them select an appropriate medical institution. The system has three main components: a server, a terminal, and a user.

[0563] Server Operation

[0564] Data collection

[0565] The server accesses the official website of a pre-configured hospital and sends an HTTP request to retrieve the HTML of the page. This is done using a scraping tool such as Python's BeautifulSoup. Specific HTML tags (e.g., " 」、「 "). It also uses the API of the specified review site to send an HTTP request and obtain review data about hospitals and doctors in JSON format. It also executes SQL queries against the medical database held by the medical institution to obtain data such as the number of deliveries, delivery times, and doctor experience.

[0566] Data analysis

[0567] The server analyzes the collected data. First, it uses Python's natural language processing library (e.g., NLTK, spaCy) to analyze the review data and calculate a reputation score. It then performs a sentiment analysis on each review and calculates the frequency of positive and negative words. Next, it uses Pandas to perform statistical analysis on the fee data and calculates the median and average. For the medical treatment data, it uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate.

[0568] Providing information

[0569] The server queries the database based on the search criteria entered by the user through the device, filters the information that matches the criteria, and then formats the filtered information into JSON format and sends it to the device.

[0570] Device behavior

[0571] User Interface

[0572] The terminal provides a graphical user interface for users to enter search criteria. A form is created using HTML and JavaScript on a web browser, allowing users to enter criteria such as the desired area, price, and reputation score. When the user presses the search button, the information is sent to the server as an HTTP request. The terminal formats the information received from the server as a list or graph and displays it to the user. This is done using JavaScript and libraries such as D3.js.

[0573] User Actions

[0574] Search and Select

[0575] Users access the system using a personal computer or smartphone, enter the necessary search criteria through the interface provided on the browser, and click the search button. The search results are displayed, and the user can select the most suitable medical institution based on the results.

[0576] Specific examples

[0577] Example 1: Collecting data from a hospital's official website

[0578] The server accesses the official website of the "specified hospital" and uses BeautifulSoup to scrape the fee information from the HTML. For example, or Text information is extracted from the data, temporarily stored, and used for analysis.

[0579] Example 2: Review analysis

[0580] The server retrieves review data about a "certain obstetrics and gynecology clinic" through the API of a "specific review site." The retrieved JSON data is analyzed using Python's NLTK, and sentiment analysis is performed to calculate a reputation score. For example, each review is tokenized and a score is calculated based on the frequency of positive and negative words.

[0581] Example 3: Analysis of medical data

[0582] The server accesses the medical institution's medical database and retrieves data on deliveries handled by a specific doctor over the past year. The retrieved data on the number of deliveries and delivery times is analyzed using Pandas to calculate average values ​​and success rates.

[0583] Example 4: Search and display

[0584] The user enters the search criteria "maternity hospitals with a good reputation and average fees in a specific area." The device sends this information to the server, which queries a database for medical institutions that meet the criteria. The device analyzes the resulting information and displays it to the user in a list format. The user can then use this list to check the details of each medical institution and select the location that best suits them.

[0585] Prompt Sentence Examples

[0586] "I'm looking for a maternity hospital with a good reputation and average prices in a specific area. Please provide details on pricing, number of deliveries, delivery times, and reputation scores."

[0587] The present invention provides an easy-to-use user interface that allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution.

[0588] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0589] Step 1: Data collection

[0590] The server sends an HTTP request to the official website of a pre-listed hospital and retrieves the HTML content. Specifically, it uses Python's BeautifulSoup to extract specific HTML tags (e.g., " 」、「 "). It also sends an HTTP request to the API of the specified review site to obtain the review data in JSON format. It also executes an SQL query to the medical institution's medical database to obtain data such as the number of deliveries, delivery time, and doctor's experience. This input data is temporarily saved and used for later analysis.

[0591] Step 2: Data analysis

[0592] The server analyzes the data collected in step 1. First, it uses natural language processing tools (e.g., NLTK, spaCy) to perform sentiment analysis on the review data and calculate a reputation score. Specifically, it tokenizes positive and negative words and calculates their occurrence rate. Next, it converts the fee data into a Pandas data frame and calculates the median and mean. For the medical treatment data, it also uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate. These analysis results are output as JSON format data.

[0593] Step 3: Enter search criteria

[0594] The terminal provides a graphical user interface for users to input search criteria. Specifically, a form is created using HTML and JavaScript, and users are prompted to input search criteria such as area, price, and reputation score. The search criteria entered by the user are sent to the server as an HTTP request. The input data is in text or numeric format, and is used as search criteria for subsequent query processing.

[0595] Step 4: Execute a database query

[0596] The server executes a query to the database based on the user's search criteria received in step 3. Specifically, it generates and executes an SQL query to filter information on medical institutions that match the criteria. This filtering extracts matching information, formats it in JSON format, and sends it to the terminal. The query input is the search criteria, and the output is information on medical institutions that match the criteria.

[0597] Step 5: Viewing search results

[0598] The device analyzes the filtered information received from the server and displays it in a format that is easy for the user to view. Specifically, it uses JavaScript and D3.js to display search results as lists and graphs. The user can scroll through these results to check detailed information about each medical institution. The displayed information includes the name of the medical institution, reputation score, median fee, and statistical information on the number of deliveries and delivery times, and the user can select the most suitable medical institution based on this information.

[0599] Step 6: Select a medical institution

[0600] The user selects the most suitable medical institution based on the information displayed on the device. Specifically, by clicking on each entry in the list or graph, the user can check detailed information and narrow down the options. The information selected by the user is sent to the server and saved as the final selection result. This selection process allows the user to find the medical institution that is most suitable for them.

[0601] Through the above steps, users can efficiently obtain information on reliable medical institutions and select the location that is best suited to them.

[0602] (Application example 1)

[0603] 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."

[0604] Conventional hospital selection support systems are limited to collecting and analyzing information from limited data sources and are unable to improve the efficiency of large-scale production facilities and factories. The objective of this invention is to apply the usefulness of hospital selection support systems to factories and achieve improved production efficiency. Specifically, the objective is to maximize the operating efficiency of the entire factory by collecting and analyzing production line data and providing users with optimal production settings.

[0605] 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.

[0606] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information, means for querying a database based on a user's search criteria and filtering information that matches the criteria, means for providing the filtered information to the user, means for collecting production data from sensors in a factory, means for analyzing the collected data and calculating production efficiency and error rates, and means for providing optimal production line settings based on the analysis results. This makes it possible to apply the technology of the hospital selection support system to improving factory production efficiency.

[0607] "Means for collecting information from the hospital's official website" refers to technologies and methods for automatically obtaining fee information and facility information published on the hospital's official website.

[0608] "Means of collecting information from review sites" refers to technologies and methods for automatically obtaining data about hospitals and doctors from review sites that aggregate user experiences and opinions.

[0609] "Means for collecting medical data held by medical institutions" refers to technologies and methods for automatically obtaining information about medical treatment from medical record databases managed by hospitals and clinics.

[0610] "Means for analyzing collected information and generating reputation scores, median fees, and medical statistical information" refers to technologies and methods for analyzing collected data and generating numerical scores for the reputation of hospitals and doctors, median fees, and medical statistical data for each doctor.

[0611] "Means of querying a database based on user search criteria and filtering information that matches the criteria" refers to a technology or method that searches a database according to search criteria specified by the user and extracts information that matches those criteria.

[0612] The "means for providing filtered information to a user" refers to an interface or method for providing information extracted by a query to a user.

[0613] "Means of collecting production data from sensors within the factory" refers to technologies and methods for automatically acquiring production-related data from various sensors installed within the factory.

[0614] "Means for analyzing collected data and calculating production efficiency and error rate" refers to techniques and methods for analyzing data collected from sensors and calculating production efficiency and error rate as a result.

[0615] "Means for providing optimal production line settings based on analysis results" refers to technologies and methods that propose optimal production line settings and improvement measures to users based on the results of data analysis.

[0616] The following describes in detail the mode for carrying out the present invention. This system is designed to optimize production efficiency within a factory and applies the technology of a hospital selection support system. The system has three main components: a server, a terminal, and a user.

[0617] Server Operation

[0618] Data collection

[0619] The server collects production data from various sensors installed in the factory, such as production volume, production speed, error rate, equipment usage, etc. It also collects visual and audio information from cameras and microphones in the factory.

[0620] Data analysis

[0621] The server analyzes the collected data. For example, visual information is analyzed using image analysis software to identify production efficiency and error patterns. This is done using Google Cloud's BigQuery and AWS's Athena, which analyze the data in real time. Natural language processing (NLP) technology is applied to the collected data, and spacy and NLTK are used to extract problems and areas for improvement from communication logs within the factory.

[0622] Providing information

[0623] The server generates optimal production line settings based on the analysis results and provides this information to the user, using Python's Dash and Tableau to visualize the analysis results and present them to managers as reports.

[0624] Device behavior

[0625] User Interface

[0626] The terminal provides an interface where the user can enter search criteria. For example, when the user enters "Please tell me the optimization settings for the production line next week," the terminal sends this information to the server. The information received from the server is formatted into an easy-to-read format (e.g., list or graph) and displayed to the user.

[0627] User Actions

[0628] Search and Select

[0629] Users access the system using a web browser on their device or a dedicated app, input search criteria such as desired production efficiency and error rate, and adjust the production line based on the optimal settings returned by the server.

[0630] Specific examples

[0631] When a factory wants to optimize the production time of a new production line, the system generates the following prompt:

[0632] "What settings can we use to optimize the average production time and reduce the error rate on our new production line? Please suggest optimal settings taking into account the current production volume, machine usage, and worker communication log data."

[0633] Based on the analysis results of these prompts, the system provides the user with optimal production settings, ensuring efficient operation of the factory's production line. This system allows users to quickly obtain reliable information to improve factory production efficiency.

[0634] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0635] Step 1:

[0636] Users access the system via a terminal and input search criteria to find the optimal settings for a specific production line. These criteria include requests for improving production efficiency and reducing error rates. The search criteria are then sent from the terminal to the server.

[0637] Input: Search criteria entered by the user

[0638] Output: Sending search criteria to the server

[0639] Step 2:

[0640] The server collects real-time production data from various sensors in the factory, including production volume, production speed, error rate, equipment usage, etc. It also captures visual and audio information from cameras and microphones in the factory.

[0641] Input: Data from various sensors and cameras in the factory

[0642] Output: Acquired production data and visual and audio information

[0643] Step 3:

[0644] The server stores the collected data in a database and prepares it for analysis: production data is stored in a database, visual information is analyzed using image analysis software, and audio information is converted into text using natural language processing technology.

[0645] Input: Collected production data, and visual and audio information

[0646] Output: A dataset ready for analysis

[0647] Step 4:

[0648] The server uses Google Cloud's BigQuery and AWS's Athena to perform real-time analysis of the collected data, extracting important indicators such as production efficiency and error rates. It also uses spacy and NLTK to analyze communication logs and identify problems and areas for improvement.

[0649] Input: A prepared dataset

[0650] Output: Extracted indicators such as production efficiency, error rate, problems and areas for improvement

[0651] Step 5:

[0652] The server generates optimal production line settings based on the analysis results, including revisions to machine operation settings and work procedures. The generated settings along with the analysis results are converted into a format that can be displayed on the user interface.

[0653] Input: Extracted metrics

[0654] Output: Optimal production line settings and data for user interface display

[0655] Step 6:

[0656] The server sends the optimal production line settings to the terminal. The terminal receives this information and displays it in a format that is easy for the user to view (lists and graphs). For example, the dashboard displays trends in production efficiency and error rates, and provides specific improvement suggestions.

[0657] Input: Data for optimal production line configuration and user interface display

[0658] Output: User-friendly formatted display content

[0659] Step 7:

[0660] The user can then confirm and implement the optimal settings for the production line provided via the terminal. Implementing the settings is expected to improve the production efficiency of the entire factory and reduce the error rate. Furthermore, the user can adjust the search conditions again as needed and obtain the optimal settings again.

[0661] Input: Optimal production line settings (display content)

[0662] Output: Implemented optimal settings and increased production efficiency

[0663] 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.

[0664] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[0665] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[0666] Server Operation

[0667] Data collection

[0668] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries and delivery times in the past, and the experience of each doctor, from medical institution databases.

[0669] Data analysis

[0670] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes medical data to generate medical statistics such as the number of deliveries per doctor, average delivery time, and success rate.

[0671] Emotion recognition by emotion engine

[0672] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, input text, etc. while using the system. Based on this emotional data, it determines whether the user is relaxed, anxious, or impatient.

[0673] Providing information

[0674] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is very anxious, it will prioritize recommending highly rated hospitals that will give them a sense of security. The filtered information is then formatted in JSON format or similar and sent to the device.

[0675] Device behavior

[0676] User Interface

[0677] The device provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The device then formats the information received from the server into an easy-to-read format (e.g., list or graph) and displays it to the user. The device also uses a camera to recognize the user's facial expressions and a microphone to analyze their voice, collecting emotional data about the user.

[0678] User Actions

[0679] Search and Select

[0680] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0681] Specific examples

[0682] Example 1: Collecting data from a hospital's official website

[0683] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[0684] Example 2: Review analysis

[0685] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0686] Example 3: Analysis of medical data

[0687] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0688] Example 4: Emotional awareness and adaptation

[0689] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that are committed to providing a relaxing environment.

[0690] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[0691] The processing flow will be explained below.

[0692] Step 1: Data collection (hospital official website)

[0693] The server accesses the official website of each hospital based on a pre-configured list.

[0694] The server scrapes HTML data from pricing information pages and facility introduction pages.

[0695] The server temporarily stores the scraped raw data.

[0696] Step 2: Data collection (review sites)

[0697] The server obtains review data about specific hospitals and doctors through the APIs of major review sites.

[0698] The server formats the acquired word-of-mouth data and stores it in a database.

[0699] Step 3: Data collection (medical data)

[0700] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[0701] The server converts the collected data into a standard format and stores it in a database.

[0702] Step 4: Analyze review data

[0703] The server analyzes the stored word-of-mouth data using natural language processing technology.

[0704] The server extracts positive or negative ratings from each review and aggregates them to calculate an overall reputation score.

[0705] The server stores the calculated reputation scores in a database.

[0706] Step 5: Analyze pricing data

[0707] The server analyzes the fee information collected from the hospitals.

[0708] The server calculates the median and variance of each fee item and generates basic data for calculating the standard fee.

[0709] The server stores the calculated fee data in a database.

[0710] Step 6: Analyze medical data

[0711] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[0712] The server stores the calculated statistical information in a database.

[0713] Step 7: Collect emotion data

[0714] The device captures the user's facial expression data through a camera.

[0715] The terminal uses a microphone to collect voice data of the user.

[0716] The device analyzes the collected facial and voice data in real time to determine the user's emotional state.

[0717] Step 8: Receiving a search request

[0718] Users access the search screen using their device's web browser or a dedicated app.

[0719] The user enters search criteria such as the desired area, price, reputation score, etc., and presses the search button.

[0720] The terminal transmits the user's emotion data to the server together with the input search conditions.

[0721] Step 9: Filtering data based on conditions and sentiment

[0722] The server queries the database based on the user's search criteria and emotional state.

[0723] The server extracts data on hospitals and doctors that match the conditions and emotions and generates a list.

[0724] For example, if the user is in an anxious state, the server will preferentially filter out hospitals that are highly reliable and have a high rating that can alleviate the user's anxiety.

[0725] Step 10: Generate a response with search results

[0726] The server converts the extracted data into an appropriate format, such as JSON.

[0727] The server transmits the converted data to the terminal.

[0728] Step 11: Viewing search results

[0729] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (e.g., list, graph, table).

[0730] The user selects the most suitable maternity hospital and doctor based on the displayed information.

[0731] The above are the specific processing steps in this system.

[0732] Example 2

[0733] 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."

[0734] Conventional maternity hospital recommendation systems do not take into account the user's emotional state when making recommendations, making it difficult for users to select a maternity hospital with confidence. Childbirth is a particularly stressful event for users, so providing appropriate information based on the user's emotional state is essential. Furthermore, the difficulty of incorporating human opinions and emotions into the analysis of collected data limits the improvement of user satisfaction. Therefore, a system that can filter information based on the user's emotional state and provide highly reliable recommendations is needed.

[0735] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median prices, and medical statistical information, means for analyzing the review data using natural language processing technology in analyzing the collected information, means for recognizing user emotions and filtering based on the emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions, and means for providing the filtered information to the user. This allows for more reliable information to be provided quickly, taking the user's emotional state into consideration, enabling the user to select an appropriate maternity hospital with peace of mind.

[0736] An "official hospital website" is an online information site officially operated by a medical institution, where various information such as medical departments, medical staff, facilities, and fees is made public.

[0737] A "review site" is a website where users can post their own experiences and opinions, which can then be viewed and evaluated by other users. Medical review sites often post reviews and ratings of specific medical institutions and doctors.

[0738] "Medical data" refers to patient medical records, medical procedure histories, and data related to the work of doctors and other medical staff held by medical institutions. Examples include the number of deliveries and delivery times in the past, and the experience of each doctor.

[0739] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze natural language, enabling semantic analysis and emotional assessment of text data.

[0740] An "emotion engine" is software or algorithm for determining a user's emotional state, collecting and analyzing emotional data from the user's facial expressions, voice, and input text.

[0741] A "reputation score" is a number calculated from word-of-mouth data and other evaluation data, and indicates the overall evaluation of a particular medical institution or doctor.

[0742] The "median price" is the median value of the collected price data, and is a statistical value that represents the center of the distribution.

[0743] "Medical statistical information" is statistical information generated based on collected medical data, and includes the number of births per doctor, average birth time, success rate, etc.

[0744] "Filtering" is the process of selecting necessary information from collected and analyzed data based on specific criteria.

[0745] "User search conditions" are conditions such as desired area, price, reputation score, etc. that a user inputs when using the system.

[0746] "User emotional state" refers to the psychological state the user experiences when using the system, including relaxation, anxiety, tension, etc.

[0747] "Querying a database" is the operation of making an inquiry to a database to retrieve specific data.

[0748] "User Device" means a device used by a user to access the system and input and retrieve information, including a personal computer, smartphone, tablet, etc.

[0749] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[0750] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[0751] Server Operation

[0752] Data collection

[0753] The server accesses the official websites of pre-configured hospitals and collects information on fees and facilities by scraping. Examples of software used include Python's "Beautiful Soup" and "Scrapy." It also uses the APIs of major review sites to obtain review data on hospitals and doctors. For example, "TrustPilot API" and "Google Places API" are used. Furthermore, data such as the number of past deliveries, delivery times, and the experience of each doctor is obtained from medical institution databases. "SQLAlchemy" and "Pandas" are used to connect to the database.

[0754] Data analysis

[0755] The server analyzes the collected data. For example, natural language processing technology is used to calculate reputation scores from the collected review data. Specific tools include "NLTK" and "spaCy." Statistical processing tools such as "NumPy" and "Pandas" are also used to calculate median prices from fee information. "SciPy" and "Matplotlib" are used to analyze medical data and generate medical statistics.

[0756] Emotion recognition by emotion engine

[0757] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, and input text while using the system. To achieve this, OpenCV is used for facial expression recognition, Google Cloud Speech-to-Text for voice analysis, and BERT for text analysis.

[0758] Providing information

[0759] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is feeling anxious, SQLAlchemy and Flask are used to recognize this state and prioritize recommendations of highly rated hospitals that will give the user a sense of security. The filtered information is formatted in JSON format or similar and sent to the device as an HTTP response.

[0760] Device behavior

[0761] User Interface

[0762] The terminal provides an interface that allows the user to enter search criteria. This interface is built using the web application frameworks "React.js" and "Vue.js." When the user enters criteria and presses the search button, the information is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format (for example, a list or graph) on the terminal and displayed to the user using "Chart.js" or "D3.js."

[0763] The device also has the ability to recognize the user's facial expressions with a camera and analyze their voice using a microphone, thereby collecting emotional data about the user. These functions allow the server to filter and provide the user with information about the most suitable maternity hospitals based on the user's search criteria and emotional state.

[0764] User Actions

[0765] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0766] Specific examples

[0767] Example 1: Collecting data from a hospital's official website

[0768] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[0769] Example 2: Review analysis

[0770] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0771] Example 3: Analysis of medical data

[0772] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0773] Example 4: Emotional awareness and adaptation

[0774] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that offer a relaxing environment.

[0775] Prompt Sentence Examples

[0776] If the user is unsure, prioritize recommending highly rated hospitals that will give them peace of mind. Additionally, provide information on the best maternity hospitals based on the user's search criteria (area, price, reputation score).

[0777] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[0778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0779] Step 1: Data collection

[0780] The server accesses the hospital's official website and collects pricing and facility information by scraping. A URL is given as input, and HTML data is retrieved and analyzed based on this. Specifically, pricing information is extracted from the HTML using Python's "Beautiful Soup" and "Scrapy" and temporarily stored in a database. Similarly, review data is collected using the APIs of major review sites, and the JSON data obtained from the review sites is analyzed and stored. Furthermore, the server accesses the medical institution's medical database and obtains medical data such as the number of past deliveries and delivery times. SQLAlchemy and Pandas are used to extract the necessary information from the database and temporarily store it. This completes the server's collection of information from the official website, review sites, and medical database.

[0781] Step 2: Data analysis

[0782] The server analyzes the data collected in step 1. The collected data is used as input, and based on this, a reputation score, median price, and medical statistical information are generated. First, the review data is analyzed using natural language processing technology to calculate a reputation score. Specifically, tools such as "NLTK" and "spaCy" are used to extract sentiment from the review text and score it. In addition, to calculate the median price from the fee information, statistical processing of the fee data is performed using "NumPy" and "Pandas." For the medical treatment data, "SciPy" and "Matplotlib" are used to generate medical statistical information such as the number of deliveries per doctor, average delivery time, and success rate. As a result, the server obtains a reputation score, median price, and medical statistical information as the analysis results.

[0783] Step 3: Emotion Recognition

[0784] The server receives input data (facial expressions, voice, and text) sent from the device to recognize the user's emotional data. Specifically, the device captures facial expressions with a camera and performs facial expression analysis using OpenCV. It also collects audio data with a microphone, converts the audio into text using Google Cloud Speech-to-Text, and analyzes the text using a natural language processing model such as BERT. This process identifies the user's emotional state and sends it to the server as emotional data, such as relaxed, anxious, or nervous. This allows the server to recognize the user's emotional state.

[0785] Step 4: Provide information

[0786] The server provides information based on the analysis results obtained in step 2 and the emotional state recognized in step 3. Specifically, it queries the database for matching maternity hospital information using the user's search criteria and emotional state as input. Filtering is performed based on the criteria, and if the user is feeling anxious, for example, it prioritizes recommendations of highly rated hospitals that offer a relaxing environment. The filtered information is formatted into JSON using "SQLAlchemy" and "Flask" and sent to the terminal as an HTTP response. This allows the server to provide the user with appropriate maternity hospital information.

[0787] Step 5: User Interface

[0788] The device provides an interface for users to enter search criteria and display information received from the server. As input, it receives the search criteria entered by the user (area, price, reputation score) and JSON data from the server. Specifically, a search form is built using "React.js" or "Vue.js," and when the search button is pressed, a request is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format using "Chart.js" or "D3.js" and displayed to the user. In addition, by allowing access to the camera and microphone, appropriate emotional data is sent to the server. This allows the device to provide the maternity hospital information searched for by the user in an easy-to-read format.

[0789] (Application example 2)

[0790] 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."

[0791] Conventional maternity hospital recommendation systems and customer service systems provide uniform information and services without considering the user's emotional state, making it impossible to adequately alleviate the user's anxiety and tension. It is also difficult to recognize the user's emotions in real time and provide appropriate responses based on them. This reduces user satisfaction and poses the risk of inappropriate information and customer service being provided.

[0792] 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.

[0793] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating evaluation indexes, median fees, and medical statistical information, means for recognizing customer emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions and emotions, and means for providing the filtered information to the user, thereby enabling appropriate information provision and customer service that takes the user's emotional state into consideration.

[0794] The "method of collecting information from the official hospital website" refers to the method of collecting fee information and facility information from the official internet page of the health institution by scraping.

[0795] "Means for collecting information from review sites" refers to methods for obtaining evaluation data and comments from online review sites.

[0796] "Means for collecting medical data held by medical institutions" refers to methods for extracting past treatment records and doctor experience from medical databases held by medical facilities.

[0797] "Means for analyzing collected information and generating evaluation indexes, median prices, and medical statistical information" refers to a method for processing acquired data using natural language processing and statistical analysis to generate hospital reputation scores, median prices, and medical statistical information.

[0798] "Means for recognizing customer emotions" refers to technology for determining a user's emotional state from their facial expressions and voice.

[0799] "Means for querying a database based on a user's search criteria and emotional state, and filtering information that matches the criteria and emotions" refers to a method for searching a database based on the user's specified criteria and emotions at the time, and extracting appropriate information.

[0800] A "means for providing filtered information to a user" is a method for organizing and displaying selected information on a user's device.

[0801] "Natural language processing" is a technology that allows computers to analyze and understand human language.

[0802] "Evaluation index" is a numerical representation of reviews and reputations of hospital and medical institution services.

[0803] "Median price" refers to the median of a statistical price for a particular service.

[0804] "Medical statistical information" refers to statistical data such as the number of births and average treatment times generated based on medical data.

[0805] A "search condition" indicates a specific attribute or requirement that a user is looking for.

[0806] "Emotional state" refers to the emotions felt by the user when using the system.

[0807] The following describes in detail the mode for carrying out the present invention. The present invention is a system that utilizes an emotion engine that recognizes user emotions, collects and analyzes medical data held by official hospital websites, review sites, and medical institutions, and provides appropriate information based on the user's search criteria and emotional state. It also includes an application example specifically aimed at improving customer service in brick-and-mortar stores.

[0808] Server Operation

[0809] The server operates using the following hardware and software.

[0810] 1. Data Collection

[0811] The server uses scraping technology to collect pricing and facility information from hospitals' official websites, obtains reviews and comments via APIs of review sites, and extracts medical data held by medical institutions from databases.

[0812] 2. Data Analysis

[0813] The collected data is analyzed using natural language processing techniques, using Python libraries OpenCV and TensorFlow to generate review metrics, median prices, and medical statistics.

[0814] 3. Emotion Recognition by Emotion Engine

[0815] The server analyzes facial expression and voice data sent from the user's device to determine the user's emotional state, using a generative AI model, a machine learning model.

[0816] 4. Information provision

[0817] It queries the database based on the user's search criteria and emotional state, and filters out information that matches the criteria. The filtered information is formatted in JSON format or similar and sent to the user's device.

[0818] Device behavior

[0819] The terminal provides an interface for users to access the system and performs the following functions:

[0820] 1. User Interface

[0821] The terminal has an interface that accepts search criteria input by the user. When the user inputs search criteria and presses the search button, the information is sent to the server.

[0822] 2. Emotion recognition

[0823] The device uses sensors such as smart glasses and a microphone to capture the user's facial expressions and voice to collect emotional data, which is then sent to a server and analyzed by an emotion engine.

[0824] 3. Display results

[0825] The information received from the server is formatted into an easy-to-read format (list, graph, etc.) on the terminal and displayed to the user.

[0826] User Actions

[0827] A user accesses the system using a terminal and operates it in the following manner.

[0828] 1. Search and Select

[0829] Users enter search criteria such as the desired area, price, and reputation score, and then press the search button. By granting permission to access the camera and microphone, the device can collect emotional data.

[0830] Specific examples of hardware and software used

[0831] 1. Hardware

[0832] Smart Glasses

[0833] microphone

[0834] 2. Software

[0835] Python

[0836] OpenCV (face detection)

[0837] TensorFlow (emotion recognition)

[0838] SpeechRecognition

[0839] Examples and prompts

[0840] Specific examples

[0841] Example 1: If a customer's facial expression is recognized as "angry," real-time advice such as "The customer is angry. Please respond calmly" will be displayed on the smart glasses.

[0842] Example 2: When a customer says, "Please tell me more about this product," a template sentence such as "This product is ____ and has ____ features. Please take a look." is returned.

[0843] Prompt Sentence Examples

[0844] "What emotions is the person in this image experiencing?"

[0845] "Tell me what the customer is saying in this audio."

[0846] "Please suggest the best way to serve customers based on the results."

[0847] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0848] Step 1:

[0849] The server collects fee and facility information from the hospital's official website by scraping it. At this stage, it accesses the web, obtains HTML data, and extracts the necessary information from specific DOM elements.

[0850] Input: Hospital's official website URL

[0851] Data processing: Parsing HTML documents and extracting data from the DOM

[0852] Output: List of fee information and facility information

[0853] Step 2:

[0854] The server retrieves review and comment data using the review site's API. At this stage, it makes an API request and receives the response data in JSON format. It then extracts the review content and rating score.

[0855] Input: API endpoint and authentication information of the review site

[0856] Data processing: Parsing JSON responses and extracting data from specific fields

[0857] Output: List of reviews and rating scores

[0858] Step 3:

[0859] The server accesses the medical database held by the medical institution and extracts medical data such as treatment records and doctor experience. At this stage, SQL queries are executed to retrieve the necessary data from the database.

[0860] Input: Connection information to medical institution's medical database

[0861] Data processing: Data extraction and aggregation using SQL queries

[0862] Output: List of treatment records and doctor experience data

[0863] Step 4:

[0864] The server analyzes the collected data using natural language processing technology to generate evaluation indicators, median prices, and medical statistics. At this stage, Python libraries are used to perform sentiment analysis on text data and statistical analysis on numerical data.

[0865] Input: Price information, reviews, treatment records

[0866] Data processing: text analysis, statistical calculations

[0867] Output: Evaluation index, median price, medical statistics

[0868] Step 5:

[0869] The server receives facial expression and voice data sent from the user's device and analyzes it using a generative AI model, thereby determining the user's emotional state.

[0870] Input: facial expression data, voice data

[0871] Data Processing: Sentiment Analysis with Generative AI Models

[0872] Output: User's emotional state (e.g., relaxed, anxious, angry)

[0873] Step 6:

[0874] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. At this stage, SQL queries or NoSQL are used to extract data that matches the criteria.

[0875] Input: User search criteria, user emotional state, medical database

[0876] Data processing: conditional filtering, sentiment matching

[0877] Output: A list of matching medical information

[0878] Step 7:

[0879] The server formats the filtered information in JSON format or similar and sends it to the user's device. At this stage, data format conversion and API response generation are performed.

[0880] Input: Filtered information

[0881] Data processing: format conversion, API response generation

[0882] Output: Medical information in JSON format

[0883] Step 8:

[0884] The terminal provides an interface for users to access the system and input search criteria. At this stage, form input and search button operations are accepted.

[0885] Input: User search criteria

[0886] Data processing: Validating search conditions and converting to JSON format

[0887] Output: Sending search criteria to the server

[0888] Step 9:

[0889] The device formats the information received from the server into an easy-to-read format (list, graph, etc.) and displays it to the user. At this stage, data parsing and UI rendering are performed.

[0890] Input: JSON response from the server

[0891] Data Transformation: Parsing data and mapping it to UI elements

[0892] Output: Display of results on the user interface

[0893] Step 10:

[0894] The device uses smart glasses and a microphone to capture the user's facial expressions and voice data, collecting emotional data, which is then sent to a server.

[0895] Input: Camera video, microphone audio

[0896] Data processing: Real-time capture, data compression, server transmission

[0897] Output: Sending facial expression data and voice data to the server

[0898] 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.

[0899] 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.

[0900] 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.

[0901] [Third embodiment]

[0902] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0903] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0904] 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).

[0905] 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.

[0906] 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.

[0907] 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).

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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."

[0914] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, and provides the user with the analysis results to help them select an appropriate maternity hospital.

[0915] The system has three main components: the server, the terminal, and the user. The server has the main computing function of collecting and analyzing information, and the terminal provides the interface through which the user can access the system. The user accesses the system through the terminal and obtains the information they need.

[0916] Server Operation

[0917] Data collection

[0918] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries, delivery times, and doctor experience from medical institution databases.

[0919] Data analysis

[0920] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes the medical data to generate medical statistics such as the number of deliveries, average delivery time, and success rate for each doctor.

[0921] Providing information

[0922] The server queries the database based on the search criteria entered by the user through the device, filters out information that matches the criteria, and formats the filtered information in JSON format or similar before sending it to the device.

[0923] Device behavior

[0924] User Interface

[0925] The terminal provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The information received from the server is formatted into an easy-to-read format (e.g., a list or graph) by the terminal and displayed to the user.

[0926] User Actions

[0927] Search and Select

[0928] Users access the system using a web browser on their device or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. The search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[0929] Specific examples

[0930] Example 1: Collecting data from a hospital's official website

[0931] The server accesses the official website of "Hospital A" and scrapes pricing information from the HTML of the page. The extracted pricing data is temporarily stored and used for analysis.

[0932] Example 2: Review analysis

[0933] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[0934] Example 3: Analysis of medical data

[0935] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[0936] Example 4: Search and display

[0937] Using a device, the user enters the search criteria "maternity hospitals in Shibuya Ward with a good reputation and average fees." The device sends this information to the server, which then queries a database for maternity hospitals that meet the criteria. The device analyzes the results sent from the server and displays them to the user as a list. The user can use this list to check the detailed information of each maternity hospital and select the one that best suits them.

[0938] According to the embodiment of the present invention, the user can quickly obtain reliable information and easily select an appropriate maternity hospital, which is expected to have the effect of significantly reducing the burden on the user.

[0939] The processing flow will be explained below.

[0940] Step 1: Data collection (hospital official website)

[0941] The server accesses the official website of each hospital based on the preset list.

[0942] The server scrapes HTML data from pricing information pages and facility introduction pages.

[0943] The server stores the scraped raw data in a temporary folder for analysis.

[0944] Step 2: Data collection (review sites)

[0945] The server accesses the APIs of major review sites to obtain review data about specific hospitals and doctors.

[0946] The server formats the acquired word-of-mouth data and stores it in a database.

[0947] Step 3: Data collection (medical data)

[0948] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[0949] The server normalizes this data and stores it in a database.

[0950] Step 4: Analyze review data

[0951] The server analyzes the collected word-of-mouth data using natural language processing technology.

[0952] The server extracts the positive or negative ratings from each review and calculates an overall reputation score.

[0953] The server stores the calculated reputation scores in a database.

[0954] Step 5: Analyze pricing data

[0955] The server analyzes the fee information collected from the hospital.

[0956] The server calculates the median and variance of the charges and calculates the standard charge based on this.

[0957] The server stores the calculated fee information in a database.

[0958] Step 6: Analyze medical data

[0959] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[0960] The server stores the generated statistics in a database.

[0961] Step 7: Receiving a search request

[0962] Users access the search screen using their device's web browser or a dedicated app.

[0963] The user enters search criteria such as area, price, reputation score, etc. and presses the search button.

[0964] The terminal transmits the input search conditions to the server.

[0965] Step 8: Filtering data based on conditions

[0966] The server queries the database based on the user's search criteria.

[0967] The server extracts data on hospitals and doctors that meet the criteria and generates a list.

[0968] Step 9: Generate a response with search results

[0969] The server converts the extracted data into an appropriate format, such as JSON.

[0970] The server transmits the converted data to the terminal.

[0971] Step 10: Displaying search results

[0972] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (such as a list or graph).

[0973] The user selects the most suitable maternity hospital based on the displayed information.

[0974] The above are the specific processing steps in this system.

[0975] Example 1

[0976] 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."

[0977] Conventional medical information systems often lack the reliability and comprehensiveness of their information, and are unable to provide sufficient support for users in selecting appropriate medical institutions. Furthermore, they have problems such as difficult-to-use user interfaces and insufficient information analysis, making it difficult for users to interpret the information. As a result, users may end up selecting an inappropriate medical institution, which can lead to anxiety and dissatisfaction.

[0978] 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.

[0979] In this invention, the server includes: means for collecting information from official hospital websites; means for collecting information from review sites; means for collecting medical data held by medical institutions; means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information; means for querying a database based on user search criteria and filtering information that matches the criteria; means for providing the filtered information to the user; and means for the terminal to provide a graphical user interface for inputting search criteria, inputting the user's desired criteria, and displaying search results. This allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution. Furthermore, the easy-to-use user interface allows users to easily input and view the information they need.

[0980] An "official hospital website" is an official internet page provided by a particular medical institution, which is intended to provide information about that medical institution.

[0981] A "word-of-mouth site" is a platform where users can post their own experiences and opinions and share them with other users, and in particular, it collects ratings and reviews of medical institutions and doctors.

[0982] "Medical data held by medical institutions" refers to specific information about medical treatment collected and accumulated by medical institutions, including data such as the number of patients, treatment details, and treatment results.

[0983] "Information collection means" refers to the techniques and methods used to obtain the required information from designated websites and databases.

[0984] "Means for analyzing information" refers to the technology or method for processing collected data and calculating specific indicators (e.g., reputation score, median price, medical statistics).

[0985] A "reputation score" is an evaluation value calculated based on word-of-mouth data, etc., and numerically indicates the quality and reliability of medical institutions and doctors.

[0986] "Median price" is a median calculated based on collected price data and indicates the average cost of the medical service in question.

[0987] "Medical statistical information" refers to statistical indicators calculated based on medical data, and includes specific data such as the number of past births, average birth times, and doctors' success rates.

[0988] "Means of querying a database based on search criteria" refers to the technology or method of searching for information in a database based on criteria specified by the user and extracting the relevant data.

[0989] "Filtering means" refers to the technology or method for sorting out information that matches the user's criteria from search results and extracting only the necessary information.

[0990] "Means for providing to the user" refers to the technology and method for presenting information to the user through a terminal or display device.

[0991] A "graphical user interface" is an interface that provides a screen format that is visually easy for users to operate, and allows them to easily input search conditions and display results.

[0992] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used for sentiment analysis of word-of-mouth data and calculating reputation scores.

[0993] "Statistical analysis tools" refers to software or programs used to analyze numerical data and calculate statistical information such as the mean, median, and distribution.

[0994] A "server" refers to a primary computer that provides services to other computers and terminals on a network, and in this system plays a central role in collecting, analyzing, and providing data.

[0995] "Terminal" refers to a device that a user uses to access and operate the system, including a personal computer or smartphone.

[0996] "User" refers to an individual or organization that uses this system to obtain information and select an appropriate medical institution.

[0997] MODE FOR CARRYING OUT THE INVENTION

[0998] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, analyzes the information, and provides it to users to help them select an appropriate medical institution. The system has three main components: a server, a terminal, and a user.

[0999] Server Operation

[1000] Data collection

[1001] The server accesses the official website of a pre-configured hospital and sends an HTTP request to retrieve the HTML of the page. This is done using a scraping tool such as Python's BeautifulSoup. Specific HTML tags (e.g., " 」、「 "). It also uses the API of the specified review site to send an HTTP request and obtain review data about hospitals and doctors in JSON format. It also executes SQL queries against the medical database held by the medical institution to obtain data such as the number of deliveries, delivery times, and doctor experience.

[1002] Data analysis

[1003] The server analyzes the collected data. First, it uses Python's natural language processing library (e.g., NLTK, spaCy) to analyze the review data and calculate a reputation score. It then performs a sentiment analysis on each review and calculates the frequency of positive and negative words. Next, it uses Pandas to perform statistical analysis on the fee data and calculates the median and average. For the medical treatment data, it uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate.

[1004] Providing information

[1005] The server queries the database based on the search criteria entered by the user through the device, filters the information that matches the criteria, and then formats the filtered information into JSON format and sends it to the device.

[1006] Device behavior

[1007] User Interface

[1008] The terminal provides a graphical user interface for users to enter search criteria. A form is created using HTML and JavaScript on a web browser, allowing users to enter criteria such as the desired area, price, and reputation score. When the user presses the search button, the information is sent to the server as an HTTP request. The terminal formats the information received from the server as a list or graph and displays it to the user. This is done using JavaScript and libraries such as D3.js.

[1009] User Actions

[1010] Search and Select

[1011] Users access the system using a personal computer or smartphone, enter the necessary search criteria through the interface provided on the browser, and click the search button. The search results are displayed, and the user can select the most suitable medical institution based on the results.

[1012] Specific examples

[1013] Example 1: Collecting data from a hospital's official website

[1014] The server accesses the official website of the "specified hospital" and uses BeautifulSoup to scrape the fee information from the HTML. For example, or Text information is extracted from the data, temporarily stored, and used for analysis.

[1015] Example 2: Review analysis

[1016] The server retrieves review data about a "certain obstetrics and gynecology clinic" through the API of a "specific review site." The retrieved JSON data is analyzed using Python's NLTK, and sentiment analysis is performed to calculate a reputation score. For example, each review is tokenized and a score is calculated based on the frequency of positive and negative words.

[1017] Example 3: Analysis of medical data

[1018] The server accesses the medical institution's medical database and retrieves data on deliveries handled by a specific doctor over the past year. The retrieved data on the number of deliveries and delivery times is analyzed using Pandas to calculate average values ​​and success rates.

[1019] Example 4: Search and display

[1020] The user enters the search criteria "maternity hospitals with a good reputation and average fees in a specific area." The device sends this information to the server, which queries a database for medical institutions that meet the criteria. The device analyzes the resulting information and displays it to the user in a list format. The user can then use this list to check the details of each medical institution and select the location that best suits them.

[1021] Prompt Sentence Examples

[1022] "I'm looking for a maternity hospital with a good reputation and average prices in a specific area. Please provide details on pricing, number of deliveries, delivery times, and reputation scores."

[1023] The present invention provides an easy-to-use user interface that allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution.

[1024] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1025] Step 1: Data collection

[1026] The server sends an HTTP request to the official website of a pre-listed hospital and retrieves the HTML content. Specifically, it uses Python's BeautifulSoup to extract specific HTML tags (e.g., " 」、「 "). It also sends an HTTP request to the API of the specified review site to obtain the review data in JSON format. It also executes an SQL query to the medical institution's medical database to obtain data such as the number of deliveries, delivery time, and doctor's experience. This input data is temporarily saved and used for later analysis.

[1027] Step 2: Data analysis

[1028] The server analyzes the data collected in step 1. First, it uses natural language processing tools (e.g., NLTK, spaCy) to perform sentiment analysis on the review data and calculate a reputation score. Specifically, it tokenizes positive and negative words and calculates their occurrence rate. Next, it converts the fee data into a Pandas data frame and calculates the median and mean. For the medical treatment data, it also uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate. These analysis results are output as JSON format data.

[1029] Step 3: Enter search criteria

[1030] The terminal provides a graphical user interface for users to input search criteria. Specifically, a form is created using HTML and JavaScript, and users are prompted to input search criteria such as area, price, and reputation score. The search criteria entered by the user are sent to the server as an HTTP request. The input data is in text or numeric format, and is used as search criteria for subsequent query processing.

[1031] Step 4: Execute a database query

[1032] The server executes a query to the database based on the user's search criteria received in step 3. Specifically, it generates and executes an SQL query to filter information on medical institutions that match the criteria. This filtering extracts matching information, formats it in JSON format, and sends it to the terminal. The query input is the search criteria, and the output is information on medical institutions that match the criteria.

[1033] Step 5: Viewing search results

[1034] The device analyzes the filtered information received from the server and displays it in a format that is easy for the user to view. Specifically, it uses JavaScript and D3.js to display search results as lists and graphs. The user can scroll through these results to check detailed information about each medical institution. The displayed information includes the name of the medical institution, reputation score, median fee, and statistical information on the number of deliveries and delivery times, and the user can select the most suitable medical institution based on this information.

[1035] Step 6: Select a medical institution

[1036] The user selects the most suitable medical institution based on the information displayed on the device. Specifically, by clicking on each entry in the list or graph, the user can check detailed information and narrow down the options. The information selected by the user is sent to the server and saved as the final selection result. This selection process allows the user to find the medical institution that is most suitable for them.

[1037] Through the above steps, users can efficiently obtain information on reliable medical institutions and select the location that is best suited to them.

[1038] (Application example 1)

[1039] 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."

[1040] Conventional hospital selection support systems are limited to collecting and analyzing information from limited data sources and are unable to improve the efficiency of large-scale production facilities and factories. The objective of this invention is to apply the usefulness of hospital selection support systems to factories and achieve improved production efficiency. Specifically, the objective is to maximize the operating efficiency of the entire factory by collecting and analyzing production line data and providing users with optimal production settings.

[1041] 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.

[1042] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information, means for querying a database based on a user's search criteria and filtering information that matches the criteria, means for providing the filtered information to the user, means for collecting production data from sensors in a factory, means for analyzing the collected data and calculating production efficiency and error rates, and means for providing optimal production line settings based on the analysis results. This makes it possible to apply the technology of the hospital selection support system to improving factory production efficiency.

[1043] "Means for collecting information from the hospital's official website" refers to technologies and methods for automatically obtaining fee information and facility information published on the hospital's official website.

[1044] "Means of collecting information from review sites" refers to technologies and methods for automatically obtaining data about hospitals and doctors from review sites that aggregate user experiences and opinions.

[1045] "Means for collecting medical data held by medical institutions" refers to technologies and methods for automatically obtaining information about medical treatment from medical record databases managed by hospitals and clinics.

[1046] "Means for analyzing collected information and generating reputation scores, median fees, and medical statistical information" refers to technologies and methods for analyzing collected data and generating numerical scores for the reputation of hospitals and doctors, median fees, and medical statistical data for each doctor.

[1047] "Means of querying a database based on user search criteria and filtering information that matches the criteria" refers to a technology or method that searches a database according to search criteria specified by the user and extracts information that matches those criteria.

[1048] The "means for providing filtered information to a user" refers to an interface or method for providing information extracted by a query to a user.

[1049] "Means of collecting production data from sensors within the factory" refers to technologies and methods for automatically acquiring production-related data from various sensors installed within the factory.

[1050] "Means for analyzing collected data and calculating production efficiency and error rate" refers to techniques and methods for analyzing data collected from sensors and calculating production efficiency and error rate as a result.

[1051] "Means for providing optimal production line settings based on analysis results" refers to technologies and methods that propose optimal production line settings and improvement measures to users based on the results of data analysis.

[1052] The following describes in detail the mode for carrying out the present invention. This system is designed to optimize production efficiency within a factory and applies the technology of a hospital selection support system. The system has three main components: a server, a terminal, and a user.

[1053] Server Operation

[1054] Data collection

[1055] The server collects production data from various sensors installed in the factory, such as production volume, production speed, error rate, equipment usage, etc. It also collects visual and audio information from cameras and microphones in the factory.

[1056] Data analysis

[1057] The server analyzes the collected data. For example, visual information is analyzed using image analysis software to identify production efficiency and error patterns. This is done using Google Cloud's BigQuery and AWS's Athena, which analyze the data in real time. Natural language processing (NLP) technology is applied to the collected data, and spacy and NLTK are used to extract problems and areas for improvement from communication logs within the factory.

[1058] Providing information

[1059] The server generates optimal production line settings based on the analysis results and provides this information to the user, using Python's Dash and Tableau to visualize the analysis results and present them to managers as reports.

[1060] Device behavior

[1061] User Interface

[1062] The terminal provides an interface where the user can enter search criteria. For example, when the user enters "Please tell me the optimization settings for the production line next week," the terminal sends this information to the server. The information received from the server is formatted into an easy-to-read format (e.g., list or graph) and displayed to the user.

[1063] User Actions

[1064] Search and Select

[1065] Users access the system using a web browser on their device or a dedicated app, input search criteria such as desired production efficiency and error rate, and adjust the production line based on the optimal settings returned by the server.

[1066] Specific examples

[1067] When a factory wants to optimize the production time of a new production line, the system generates the following prompt:

[1068] "What settings can we use to optimize the average production time and reduce the error rate on our new production line? Please suggest optimal settings taking into account the current production volume, machine usage, and worker communication log data."

[1069] Based on the analysis results of these prompts, the system provides the user with optimal production settings, ensuring efficient operation of the factory's production line. This system allows users to quickly obtain reliable information to improve factory production efficiency.

[1070] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1071] Step 1:

[1072] Users access the system via a terminal and input search criteria to find the optimal settings for a specific production line. These criteria include requests for improving production efficiency and reducing error rates. The search criteria are then sent from the terminal to the server.

[1073] Input: Search criteria entered by the user

[1074] Output: Sending search criteria to the server

[1075] Step 2:

[1076] The server collects real-time production data from various sensors in the factory, including production volume, production speed, error rate, equipment usage, etc. It also captures visual and audio information from cameras and microphones in the factory.

[1077] Input: Data from various sensors and cameras in the factory

[1078] Output: Acquired production data and visual and audio information

[1079] Step 3:

[1080] The server stores the collected data in a database and prepares it for analysis: production data is stored in a database, visual information is analyzed using image analysis software, and audio information is converted into text using natural language processing technology.

[1081] Input: Collected production data, and visual and audio information

[1082] Output: A dataset ready for analysis

[1083] Step 4:

[1084] The server uses Google Cloud's BigQuery and AWS's Athena to perform real-time analysis of the collected data, extracting important indicators such as production efficiency and error rates. It also uses spacy and NLTK to analyze communication logs and identify problems and areas for improvement.

[1085] Input: A prepared dataset

[1086] Output: Extracted indicators such as production efficiency, error rate, problems and areas for improvement

[1087] Step 5:

[1088] The server generates optimal production line settings based on the analysis results, including revisions to machine operation settings and work procedures. The generated settings along with the analysis results are converted into a format that can be displayed on the user interface.

[1089] Input: Extracted metrics

[1090] Output: Optimal production line settings and data for user interface display

[1091] Step 6:

[1092] The server sends the optimal production line settings to the terminal. The terminal receives this information and displays it in a format that is easy for the user to view (lists and graphs). For example, the dashboard displays trends in production efficiency and error rates, and provides specific improvement suggestions.

[1093] Input: Data for optimal production line configuration and user interface display

[1094] Output: User-friendly formatted display content

[1095] Step 7:

[1096] The user can then confirm and implement the optimal settings for the production line provided via the terminal. Implementing the settings is expected to improve the production efficiency of the entire factory and reduce the error rate. Furthermore, the user can adjust the search conditions again as needed and obtain the optimal settings again.

[1097] Input: Optimal production line settings (display content)

[1098] Output: Implemented optimal settings and increased production efficiency

[1099] 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.

[1100] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[1101] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[1102] Server Operation

[1103] Data collection

[1104] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries and delivery times in the past, and the experience of each doctor, from medical institution databases.

[1105] Data analysis

[1106] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes medical data to generate medical statistics such as the number of deliveries per doctor, average delivery time, and success rate.

[1107] Emotion recognition by emotion engine

[1108] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, input text, etc. while using the system. Based on this emotional data, it determines whether the user is relaxed, anxious, or impatient.

[1109] Providing information

[1110] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is very anxious, it will prioritize recommending highly rated hospitals that will give them a sense of security. The filtered information is then formatted in JSON format or similar and sent to the device.

[1111] Device behavior

[1112] User Interface

[1113] The device provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The device then formats the information received from the server into an easy-to-read format (e.g., list or graph) and displays it to the user. The device also uses a camera to recognize the user's facial expressions and a microphone to analyze their voice, collecting emotional data about the user.

[1114] User Actions

[1115] Search and Select

[1116] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[1117] Specific examples

[1118] Example 1: Collecting data from a hospital's official website

[1119] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[1120] Example 2: Review analysis

[1121] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[1122] Example 3: Analysis of medical data

[1123] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[1124] Example 4: Emotional awareness and adaptation

[1125] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that are committed to providing a relaxing environment.

[1126] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[1127] The processing flow will be explained below.

[1128] Step 1: Data collection (hospital official website)

[1129] The server accesses the official website of each hospital based on a pre-configured list.

[1130] The server scrapes HTML data from pricing information pages and facility introduction pages.

[1131] The server temporarily stores the scraped raw data.

[1132] Step 2: Data collection (review sites)

[1133] The server obtains review data about specific hospitals and doctors through the APIs of major review sites.

[1134] The server formats the acquired word-of-mouth data and stores it in a database.

[1135] Step 3: Data collection (medical data)

[1136] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[1137] The server converts the collected data into a standard format and stores it in a database.

[1138] Step 4: Analyze review data

[1139] The server analyzes the stored word-of-mouth data using natural language processing technology.

[1140] The server extracts positive or negative ratings from each review and aggregates them to calculate an overall reputation score.

[1141] The server stores the calculated reputation scores in a database.

[1142] Step 5: Analyze pricing data

[1143] The server analyzes the fee information collected from the hospitals.

[1144] The server calculates the median and variance of each fee item and generates basic data for calculating the standard fee.

[1145] The server stores the calculated fee data in a database.

[1146] Step 6: Analyze medical data

[1147] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[1148] The server stores the calculated statistical information in a database.

[1149] Step 7: Collect emotion data

[1150] The device captures the user's facial expression data through a camera.

[1151] The terminal uses a microphone to collect voice data of the user.

[1152] The device analyzes the collected facial and voice data in real time to determine the user's emotional state.

[1153] Step 8: Receiving a search request

[1154] Users access the search screen using their device's web browser or a dedicated app.

[1155] The user enters search criteria such as the desired area, price, reputation score, etc., and presses the search button.

[1156] The terminal transmits the user's emotion data to the server together with the input search conditions.

[1157] Step 9: Filtering data based on conditions and sentiment

[1158] The server queries the database based on the user's search criteria and emotional state.

[1159] The server extracts data on hospitals and doctors that match the conditions and emotions and generates a list.

[1160] For example, if the user is in an anxious state, the server will preferentially filter out hospitals that are highly reliable and have a high rating that can alleviate the user's anxiety.

[1161] Step 10: Generate a response with search results

[1162] The server converts the extracted data into an appropriate format, such as JSON.

[1163] The server transmits the converted data to the terminal.

[1164] Step 11: Viewing search results

[1165] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (e.g., list, graph, table).

[1166] The user selects the most suitable maternity hospital and doctor based on the displayed information.

[1167] The above are the specific processing steps in this system.

[1168] Example 2

[1169] 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."

[1170] Conventional maternity hospital recommendation systems do not take into account the user's emotional state when making recommendations, making it difficult for users to select a maternity hospital with confidence. Childbirth is a particularly stressful event for users, so providing appropriate information based on the user's emotional state is essential. Furthermore, the difficulty of incorporating human opinions and emotions into the analysis of collected data limits the improvement of user satisfaction. Therefore, a system that can filter information based on the user's emotional state and provide highly reliable recommendations is needed.

[1171] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median prices, and medical statistical information, means for analyzing the review data using natural language processing technology in analyzing the collected information, means for recognizing user emotions and filtering based on the emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions, and means for providing the filtered information to the user. This allows for more reliable information to be provided quickly, taking the user's emotional state into consideration, enabling the user to select an appropriate maternity hospital with peace of mind.

[1172] An "official hospital website" is an online information site officially operated by a medical institution, where various information such as medical departments, medical staff, facilities, and fees is made public.

[1173] A "review site" is a website where users can post their own experiences and opinions, which can then be viewed and evaluated by other users. Medical review sites often post reviews and ratings of specific medical institutions and doctors.

[1174] "Medical data" refers to patient medical records, medical procedure histories, and data related to the work of doctors and other medical staff held by medical institutions. Examples include the number of deliveries and delivery times in the past, and the experience of each doctor.

[1175] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze natural language, enabling semantic analysis and emotional assessment of text data.

[1176] An "emotion engine" is software or algorithm for determining a user's emotional state, collecting and analyzing emotional data from the user's facial expressions, voice, and input text.

[1177] A "reputation score" is a number calculated from word-of-mouth data and other evaluation data, and indicates the overall evaluation of a particular medical institution or doctor.

[1178] The "median price" is the median value of the collected price data, and is a statistical value that represents the center of the distribution.

[1179] "Medical statistical information" is statistical information generated based on collected medical data, and includes the number of births per doctor, average birth time, success rate, etc.

[1180] "Filtering" is the process of selecting necessary information from collected and analyzed data based on specific criteria.

[1181] "User search conditions" are conditions such as desired area, price, reputation score, etc. that a user inputs when using the system.

[1182] "User emotional state" refers to the psychological state the user experiences when using the system, including relaxation, anxiety, tension, etc.

[1183] "Querying a database" is the operation of making an inquiry to a database to retrieve specific data.

[1184] "User Device" means a device used by a user to access the system and input and retrieve information, including a personal computer, smartphone, tablet, etc.

[1185] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[1186] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[1187] Server Operation

[1188] Data collection

[1189] The server accesses the official websites of pre-configured hospitals and collects information on fees and facilities by scraping. Examples of software used include Python's "Beautiful Soup" and "Scrapy." It also uses the APIs of major review sites to obtain review data on hospitals and doctors. For example, "TrustPilot API" and "Google Places API" are used. Furthermore, data such as the number of past deliveries, delivery times, and the experience of each doctor is obtained from medical institution databases. "SQLAlchemy" and "Pandas" are used to connect to the database.

[1190] Data analysis

[1191] The server analyzes the collected data. For example, natural language processing technology is used to calculate reputation scores from the collected review data. Specific tools include "NLTK" and "spaCy." Statistical processing tools such as "NumPy" and "Pandas" are also used to calculate median prices from fee information. "SciPy" and "Matplotlib" are used to analyze medical data and generate medical statistics.

[1192] Emotion recognition by emotion engine

[1193] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, and input text while using the system. To achieve this, OpenCV is used for facial expression recognition, Google Cloud Speech-to-Text for voice analysis, and BERT for text analysis.

[1194] Providing information

[1195] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is feeling anxious, SQLAlchemy and Flask are used to recognize this state and prioritize recommendations of highly rated hospitals that will give the user a sense of security. The filtered information is formatted in JSON format or similar and sent to the device as an HTTP response.

[1196] Device behavior

[1197] User Interface

[1198] The terminal provides an interface that allows the user to enter search criteria. This interface is built using the web application frameworks "React.js" and "Vue.js." When the user enters criteria and presses the search button, the information is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format (for example, a list or graph) on the terminal and displayed to the user using "Chart.js" or "D3.js."

[1199] The device also has the ability to recognize the user's facial expressions with a camera and analyze their voice using a microphone, thereby collecting emotional data about the user. These functions allow the server to filter and provide the user with information about the most suitable maternity hospitals based on the user's search criteria and emotional state.

[1200] User Actions

[1201] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[1202] Specific examples

[1203] Example 1: Collecting data from a hospital's official website

[1204] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[1205] Example 2: Review analysis

[1206] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[1207] Example 3: Analysis of medical data

[1208] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[1209] Example 4: Emotional awareness and adaptation

[1210] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that offer a relaxing environment.

[1211] Prompt Sentence Examples

[1212] If the user is unsure, prioritize recommending highly rated hospitals that will give them peace of mind. Additionally, provide information on the best maternity hospitals based on the user's search criteria (area, price, reputation score).

[1213] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[1214] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1215] Step 1: Data collection

[1216] The server accesses the hospital's official website and collects pricing and facility information by scraping. A URL is given as input, and HTML data is retrieved and analyzed based on this. Specifically, pricing information is extracted from the HTML using Python's "Beautiful Soup" and "Scrapy" and temporarily stored in a database. Similarly, review data is collected using the APIs of major review sites, and the JSON data obtained from the review sites is analyzed and stored. Furthermore, the server accesses the medical institution's medical database and obtains medical data such as the number of past deliveries and delivery times. SQLAlchemy and Pandas are used to extract the necessary information from the database and temporarily store it. This completes the server's collection of information from the official website, review sites, and medical database.

[1217] Step 2: Data analysis

[1218] The server analyzes the data collected in step 1. The collected data is used as input, and based on this, a reputation score, median price, and medical statistical information are generated. First, the review data is analyzed using natural language processing technology to calculate a reputation score. Specifically, tools such as "NLTK" and "spaCy" are used to extract sentiment from the review text and score it. In addition, to calculate the median price from the fee information, statistical processing of the fee data is performed using "NumPy" and "Pandas." For the medical treatment data, "SciPy" and "Matplotlib" are used to generate medical statistical information such as the number of deliveries per doctor, average delivery time, and success rate. As a result, the server obtains a reputation score, median price, and medical statistical information as the analysis results.

[1219] Step 3: Emotion Recognition

[1220] The server receives input data (facial expressions, voice, and text) sent from the device to recognize the user's emotional data. Specifically, the device captures facial expressions with a camera and performs facial expression analysis using OpenCV. It also collects audio data with a microphone, converts the audio into text using Google Cloud Speech-to-Text, and analyzes the text using a natural language processing model such as BERT. This process identifies the user's emotional state and sends it to the server as emotional data, such as relaxed, anxious, or nervous. This allows the server to recognize the user's emotional state.

[1221] Step 4: Provide information

[1222] The server provides information based on the analysis results obtained in step 2 and the emotional state recognized in step 3. Specifically, it queries the database for matching maternity hospital information using the user's search criteria and emotional state as input. Filtering is performed based on the criteria, and if the user is feeling anxious, for example, it prioritizes recommendations of highly rated hospitals that offer a relaxing environment. The filtered information is formatted into JSON using "SQLAlchemy" and "Flask" and sent to the terminal as an HTTP response. This allows the server to provide the user with appropriate maternity hospital information.

[1223] Step 5: User Interface

[1224] The device provides an interface for users to enter search criteria and display information received from the server. As input, it receives the search criteria entered by the user (area, price, reputation score) and JSON data from the server. Specifically, a search form is built using "React.js" or "Vue.js," and when the search button is pressed, a request is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format using "Chart.js" or "D3.js" and displayed to the user. In addition, by allowing access to the camera and microphone, appropriate emotional data is sent to the server. This allows the device to provide the maternity hospital information searched for by the user in an easy-to-read format.

[1225] (Application example 2)

[1226] 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."

[1227] Conventional maternity hospital recommendation systems and customer service systems provide uniform information and services without considering the user's emotional state, making it impossible to adequately alleviate the user's anxiety and tension. It is also difficult to recognize the user's emotions in real time and provide appropriate responses based on them. This reduces user satisfaction and poses the risk of inappropriate information and customer service being provided.

[1228] 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.

[1229] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating evaluation indexes, median fees, and medical statistical information, means for recognizing customer emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions and emotions, and means for providing the filtered information to the user, thereby enabling appropriate information provision and customer service that takes the user's emotional state into consideration.

[1230] The "method of collecting information from the official hospital website" refers to the method of collecting fee information and facility information from the official internet page of the health institution by scraping.

[1231] "Means for collecting information from review sites" refers to methods for obtaining evaluation data and comments from online review sites.

[1232] "Means for collecting medical data held by medical institutions" refers to methods for extracting past treatment records and doctor experience from medical databases held by medical facilities.

[1233] "Means for analyzing collected information and generating evaluation indexes, median prices, and medical statistical information" refers to a method for processing acquired data using natural language processing and statistical analysis to generate hospital reputation scores, median prices, and medical statistical information.

[1234] "Means for recognizing customer emotions" refers to technology for determining a user's emotional state from their facial expressions and voice.

[1235] "Means for querying a database based on a user's search criteria and emotional state, and filtering information that matches the criteria and emotions" refers to a method for searching a database based on the user's specified criteria and emotions at the time, and extracting appropriate information.

[1236] A "means for providing filtered information to a user" is a method for organizing and displaying selected information on a user's device.

[1237] "Natural language processing" is a technology that allows computers to analyze and understand human language.

[1238] "Evaluation index" is a numerical representation of reviews and reputations of hospital and medical institution services.

[1239] "Median price" refers to the median of a statistical price for a particular service.

[1240] "Medical statistical information" refers to statistical data such as the number of births and average treatment times generated based on medical data.

[1241] A "search condition" indicates a specific attribute or requirement that a user is looking for.

[1242] "Emotional state" refers to the emotions felt by the user when using the system.

[1243] The following describes in detail the mode for carrying out the present invention. The present invention is a system that utilizes an emotion engine that recognizes user emotions, collects and analyzes medical data held by official hospital websites, review sites, and medical institutions, and provides appropriate information based on the user's search criteria and emotional state. It also includes an application example specifically aimed at improving customer service in brick-and-mortar stores.

[1244] Server Operation

[1245] The server operates using the following hardware and software.

[1246] 1. Data Collection

[1247] The server uses scraping technology to collect pricing and facility information from hospitals' official websites, obtains reviews and comments via APIs of review sites, and extracts medical data held by medical institutions from databases.

[1248] 2. Data Analysis

[1249] The collected data is analyzed using natural language processing techniques, using Python libraries OpenCV and TensorFlow to generate review metrics, median prices, and medical statistics.

[1250] 3. Emotion Recognition by Emotion Engine

[1251] The server analyzes facial expression and voice data sent from the user's device to determine the user's emotional state, using a generative AI model, a machine learning model.

[1252] 4. Information provision

[1253] It queries the database based on the user's search criteria and emotional state, and filters out information that matches the criteria. The filtered information is formatted in JSON format or similar and sent to the user's device.

[1254] Device behavior

[1255] The terminal provides an interface for users to access the system and performs the following functions:

[1256] 1. User Interface

[1257] The terminal has an interface that accepts search criteria input by the user. When the user inputs search criteria and presses the search button, the information is sent to the server.

[1258] 2. Emotion recognition

[1259] The device uses sensors such as smart glasses and a microphone to capture the user's facial expressions and voice to collect emotional data, which is then sent to a server and analyzed by an emotion engine.

[1260] 3. Display results

[1261] The information received from the server is formatted into an easy-to-read format (list, graph, etc.) on the terminal and displayed to the user.

[1262] User Actions

[1263] A user accesses the system using a terminal and operates it in the following manner.

[1264] 1. Search and Select

[1265] Users enter search criteria such as the desired area, price, and reputation score, and then press the search button. By granting permission to access the camera and microphone, the device can collect emotional data.

[1266] Specific examples of hardware and software used

[1267] 1. Hardware

[1268] Smart Glasses

[1269] microphone

[1270] 2. Software

[1271] Python

[1272] OpenCV (face detection)

[1273] TensorFlow (emotion recognition)

[1274] SpeechRecognition

[1275] Examples and prompts

[1276] Specific examples

[1277] Example 1: If a customer's facial expression is recognized as "angry," real-time advice such as "The customer is angry. Please respond calmly" will be displayed on the smart glasses.

[1278] Example 2: When a customer says, "Please tell me more about this product," a template sentence such as "This product is ____ and has ____ features. Please take a look." is returned.

[1279] Prompt Sentence Examples

[1280] "What emotions is the person in this image experiencing?"

[1281] "Tell me what the customer is saying in this audio."

[1282] "Please suggest the best way to serve customers based on the results."

[1283] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1284] Step 1:

[1285] The server collects fee and facility information from the hospital's official website by scraping it. At this stage, it accesses the web, obtains HTML data, and extracts the necessary information from specific DOM elements.

[1286] Input: Hospital's official website URL

[1287] Data processing: Parsing HTML documents and extracting data from the DOM

[1288] Output: List of fee information and facility information

[1289] Step 2:

[1290] The server retrieves review and comment data using the review site's API. At this stage, it makes an API request and receives the response data in JSON format. It then extracts the review content and rating score.

[1291] Input: API endpoint and authentication information of the review site

[1292] Data processing: Parsing JSON responses and extracting data from specific fields

[1293] Output: List of reviews and rating scores

[1294] Step 3:

[1295] The server accesses the medical database held by the medical institution and extracts medical data such as treatment records and doctor experience. At this stage, SQL queries are executed to retrieve the necessary data from the database.

[1296] Input: Connection information to medical institution's medical database

[1297] Data processing: Data extraction and aggregation using SQL queries

[1298] Output: List of treatment records and doctor experience data

[1299] Step 4:

[1300] The server analyzes the collected data using natural language processing technology to generate evaluation indicators, median prices, and medical statistics. At this stage, Python libraries are used to perform sentiment analysis on text data and statistical analysis on numerical data.

[1301] Input: Price information, reviews, treatment records

[1302] Data processing: text analysis, statistical calculations

[1303] Output: Evaluation index, median price, medical statistics

[1304] Step 5:

[1305] The server receives facial expression and voice data sent from the user's device and analyzes it using a generative AI model, thereby determining the user's emotional state.

[1306] Input: facial expression data, voice data

[1307] Data Processing: Sentiment Analysis with Generative AI Models

[1308] Output: User's emotional state (e.g., relaxed, anxious, angry)

[1309] Step 6:

[1310] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. At this stage, SQL queries or NoSQL are used to extract data that matches the criteria.

[1311] Input: User search criteria, user emotional state, medical database

[1312] Data processing: conditional filtering, sentiment matching

[1313] Output: A list of matching medical information

[1314] Step 7:

[1315] The server formats the filtered information in JSON format or similar and sends it to the user's device. At this stage, data format conversion and API response generation are performed.

[1316] Input: Filtered information

[1317] Data processing: format conversion, API response generation

[1318] Output: Medical information in JSON format

[1319] Step 8:

[1320] The terminal provides an interface for users to access the system and input search criteria. At this stage, form input and search button operations are accepted.

[1321] Input: User search criteria

[1322] Data processing: Validating search conditions and converting to JSON format

[1323] Output: Sending search criteria to the server

[1324] Step 9:

[1325] The device formats the information received from the server into an easy-to-read format (list, graph, etc.) and displays it to the user. At this stage, data parsing and UI rendering are performed.

[1326] Input: JSON response from the server

[1327] Data Transformation: Parsing data and mapping it to UI elements

[1328] Output: Display of results on the user interface

[1329] Step 10:

[1330] The device uses smart glasses and a microphone to capture the user's facial expressions and voice data, collecting emotional data, which is then sent to a server.

[1331] Input: Camera video, microphone audio

[1332] Data processing: Real-time capture, data compression, server transmission

[1333] Output: Sending facial expression data and voice data to the server

[1334] 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.

[1335] 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.

[1336] 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.

[1337] [Fourth embodiment]

[1338] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1339] 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.

[1340] 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).

[1341] 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.

[1342] 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.

[1343] 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).

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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.

[1350] 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."

[1351] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, and provides the user with the analysis results to help them select an appropriate maternity hospital.

[1352] The system has three main components: the server, the terminal, and the user. The server has the main computing function of collecting and analyzing information, and the terminal provides the interface through which the user can access the system. The user accesses the system through the terminal and obtains the information they need.

[1353] Server Operation

[1354] Data collection

[1355] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries, delivery times, and doctor experience from medical institution databases.

[1356] Data analysis

[1357] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes the medical data to generate medical statistics such as the number of deliveries, average delivery time, and success rate for each doctor.

[1358] Providing information

[1359] The server queries the database based on the search criteria entered by the user through the device, filters out information that matches the criteria, and formats the filtered information in JSON format or similar before sending it to the device.

[1360] Device behavior

[1361] User Interface

[1362] The terminal provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The information received from the server is formatted into an easy-to-read format (e.g., a list or graph) by the terminal and displayed to the user.

[1363] User Actions

[1364] Search and Select

[1365] Users access the system using a web browser on their device or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. The search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[1366] Specific examples

[1367] Example 1: Collecting data from a hospital's official website

[1368] The server accesses the official website of "Hospital A" and scrapes pricing information from the HTML of the page. The extracted pricing data is temporarily stored and used for analysis.

[1369] Example 2: Review analysis

[1370] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[1371] Example 3: Analysis of medical data

[1372] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[1373] Example 4: Search and display

[1374] Using a device, the user enters the search criteria "maternity hospitals in Shibuya Ward with a good reputation and average fees." The device sends this information to the server, which then queries a database for maternity hospitals that meet the criteria. The device analyzes the results sent from the server and displays them to the user as a list. The user can use this list to check the detailed information of each maternity hospital and select the one that best suits them.

[1375] According to the embodiment of the present invention, the user can quickly obtain reliable information and easily select an appropriate maternity hospital, which is expected to have the effect of significantly reducing the burden on the user.

[1376] The processing flow will be explained below.

[1377] Step 1: Data collection (hospital official website)

[1378] The server accesses the official website of each hospital based on the preset list.

[1379] The server scrapes HTML data from pricing information pages and facility introduction pages.

[1380] The server stores the scraped raw data in a temporary folder for analysis.

[1381] Step 2: Data collection (review sites)

[1382] The server accesses the APIs of major review sites to obtain review data about specific hospitals and doctors.

[1383] The server formats the acquired word-of-mouth data and stores it in a database.

[1384] Step 3: Data collection (medical data)

[1385] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[1386] The server normalizes this data and stores it in a database.

[1387] Step 4: Analyze review data

[1388] The server analyzes the collected word-of-mouth data using natural language processing technology.

[1389] The server extracts the positive or negative ratings from each review and calculates an overall reputation score.

[1390] The server stores the calculated reputation scores in a database.

[1391] Step 5: Analyze pricing data

[1392] The server analyzes the fee information collected from the hospital.

[1393] The server calculates the median and variance of the charges and calculates the standard charge based on this.

[1394] The server stores the calculated fee information in a database.

[1395] Step 6: Analyze medical data

[1396] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[1397] The server stores the generated statistics in a database.

[1398] Step 7: Receiving a search request

[1399] Users access the search screen using their device's web browser or a dedicated app.

[1400] The user enters search criteria such as area, price, reputation score, etc. and presses the search button.

[1401] The terminal transmits the input search conditions to the server.

[1402] Step 8: Filtering data based on conditions

[1403] The server queries the database based on the user's search criteria.

[1404] The server extracts data on hospitals and doctors that meet the criteria and generates a list.

[1405] Step 9: Generate a response with search results

[1406] The server converts the extracted data into an appropriate format, such as JSON.

[1407] The server transmits the converted data to the terminal.

[1408] Step 10: Displaying search results

[1409] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (such as a list or graph).

[1410] The user selects the most suitable maternity hospital based on the displayed information.

[1411] The above are the specific processing steps in this system.

[1412] Example 1

[1413] 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."

[1414] Conventional medical information systems often lack the reliability and comprehensiveness of their information, and are unable to provide sufficient support for users in selecting appropriate medical institutions. Furthermore, they have problems such as difficult-to-use user interfaces and insufficient information analysis, making it difficult for users to interpret the information. As a result, users may end up selecting an inappropriate medical institution, which can lead to anxiety and dissatisfaction.

[1415] 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.

[1416] In this invention, the server includes: means for collecting information from official hospital websites; means for collecting information from review sites; means for collecting medical data held by medical institutions; means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information; means for querying a database based on user search criteria and filtering information that matches the criteria; means for providing the filtered information to the user; and means for the terminal to provide a graphical user interface for inputting search criteria, inputting the user's desired criteria, and displaying search results. This allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution. Furthermore, the easy-to-use user interface allows users to easily input and view the information they need.

[1417] An "official hospital website" is an official internet page provided by a particular medical institution, which is intended to provide information about that medical institution.

[1418] A "word-of-mouth site" is a platform where users can post their own experiences and opinions and share them with other users, and in particular, it collects ratings and reviews of medical institutions and doctors.

[1419] "Medical data held by medical institutions" refers to specific information about medical treatment collected and accumulated by medical institutions, including data such as the number of patients, treatment details, and treatment results.

[1420] "Information collection means" refers to the techniques and methods used to obtain the required information from designated websites and databases.

[1421] "Means for analyzing information" refers to the technology or method for processing collected data and calculating specific indicators (e.g., reputation score, median price, medical statistics).

[1422] A "reputation score" is an evaluation value calculated based on word-of-mouth data, etc., and numerically indicates the quality and reliability of medical institutions and doctors.

[1423] "Median price" is a median calculated based on collected price data and indicates the average cost of the medical service in question.

[1424] "Medical statistical information" refers to statistical indicators calculated based on medical data, and includes specific data such as the number of past births, average birth times, and doctors' success rates.

[1425] "Means of querying a database based on search criteria" refers to the technology or method of searching for information in a database based on criteria specified by the user and extracting the relevant data.

[1426] "Filtering means" refers to the technology or method for sorting out information that matches the user's criteria from search results and extracting only the necessary information.

[1427] "Means for providing to the user" refers to the technology and method for presenting information to the user through a terminal or display device.

[1428] A "graphical user interface" is an interface that provides a screen format that is visually easy for users to operate, and allows them to easily input search conditions and display results.

[1429] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used for sentiment analysis of word-of-mouth data and calculating reputation scores.

[1430] "Statistical analysis tools" refers to software or programs used to analyze numerical data and calculate statistical information such as the mean, median, and distribution.

[1431] A "server" refers to a primary computer that provides services to other computers and terminals on a network, and in this system plays a central role in collecting, analyzing, and providing data.

[1432] "Terminal" refers to a device that a user uses to access and operate the system, including a personal computer or smartphone.

[1433] "User" refers to an individual or organization that uses this system to obtain information and select an appropriate medical institution.

[1434] MODE FOR CARRYING OUT THE INVENTION

[1435] The following describes in detail an embodiment of the present invention. This system collects information from official hospital websites, review sites, and medical data held by medical institutions, analyzes the information, and provides it to users to help them select an appropriate medical institution. The system has three main components: a server, a terminal, and a user.

[1436] Server Operation

[1437] Data collection

[1438] The server accesses the official website of a pre-configured hospital and sends an HTTP request to retrieve the HTML of the page. This is done using a scraping tool such as Python's BeautifulSoup. Specific HTML tags (e.g., " 」、「 "). It also uses the API of the specified review site to send an HTTP request and obtain review data about hospitals and doctors in JSON format. It also executes SQL queries against the medical database held by the medical institution to obtain data such as the number of deliveries, delivery times, and doctor experience.

[1439] Data analysis

[1440] The server analyzes the collected data. First, it uses Python's natural language processing library (e.g., NLTK, spaCy) to analyze the review data and calculate a reputation score. It then performs a sentiment analysis on each review and calculates the frequency of positive and negative words. Next, it uses Pandas to perform statistical analysis on the fee data and calculates the median and average. For the medical treatment data, it uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate.

[1441] Providing information

[1442] The server queries the database based on the search criteria entered by the user through the device, filters the information that matches the criteria, and then formats the filtered information into JSON format and sends it to the device.

[1443] Device behavior

[1444] User Interface

[1445] The terminal provides a graphical user interface for users to enter search criteria. A form is created using HTML and JavaScript on a web browser, allowing users to enter criteria such as the desired area, price, and reputation score. When the user presses the search button, the information is sent to the server as an HTTP request. The terminal formats the information received from the server as a list or graph and displays it to the user. This is done using JavaScript and libraries such as D3.js.

[1446] User Actions

[1447] Search and Select

[1448] Users access the system using a personal computer or smartphone, enter the necessary search criteria through the interface provided on the browser, and click the search button. The search results are displayed, and the user can select the most suitable medical institution based on the results.

[1449] Specific examples

[1450] Example 1: Collecting data from a hospital's official website

[1451] The server accesses the official website of the "specified hospital" and uses BeautifulSoup to scrape the fee information from the HTML. For example, or Text information is extracted from the data, temporarily stored, and used for analysis.

[1452] Example 2: Review analysis

[1453] The server retrieves review data about a "certain obstetrics and gynecology clinic" through the API of a "specific review site." The retrieved JSON data is analyzed using Python's NLTK, and sentiment analysis is performed to calculate a reputation score. For example, each review is tokenized and a score is calculated based on the frequency of positive and negative words.

[1454] Example 3: Analysis of medical data

[1455] The server accesses the medical institution's medical database and retrieves data on deliveries handled by a specific doctor over the past year. The retrieved data on the number of deliveries and delivery times is analyzed using Pandas to calculate average values ​​and success rates.

[1456] Example 4: Search and display

[1457] The user enters the search criteria "maternity hospitals with a good reputation and average fees in a specific area." The device sends this information to the server, which queries a database for medical institutions that meet the criteria. The device analyzes the resulting information and displays it to the user in a list format. The user can then use this list to check the details of each medical institution and select the location that best suits them.

[1458] Prompt Sentence Examples

[1459] "I'm looking for a maternity hospital with a good reputation and average prices in a specific area. Please provide details on pricing, number of deliveries, delivery times, and reputation scores."

[1460] The present invention provides an easy-to-use user interface that allows users to quickly obtain reliable and comprehensive information, making it easier to select an appropriate medical institution.

[1461] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1462] Step 1: Data collection

[1463] The server sends an HTTP request to the official website of a pre-listed hospital and retrieves the HTML content. Specifically, it uses Python's BeautifulSoup to extract specific HTML tags (e.g., " 」、「 "). It also sends an HTTP request to the API of the specified review site to obtain the review data in JSON format. It also executes an SQL query to the medical institution's medical database to obtain data such as the number of deliveries, delivery time, and doctor's experience. This input data is temporarily saved and used for later analysis.

[1464] Step 2: Data analysis

[1465] The server analyzes the data collected in step 1. First, it uses natural language processing tools (e.g., NLTK, spaCy) to perform sentiment analysis on the review data and calculate a reputation score. Specifically, it tokenizes positive and negative words and calculates their occurrence rate. Next, it converts the fee data into a Pandas data frame and calculates the median and mean. For the medical treatment data, it also uses Pandas and NumPy to generate statistical information for each doctor, such as the number of deliveries, average delivery time, and success rate. These analysis results are output as JSON format data.

[1466] Step 3: Enter search criteria

[1467] The terminal provides a graphical user interface for users to input search criteria. Specifically, a form is created using HTML and JavaScript, and users are prompted to input search criteria such as area, price, and reputation score. The search criteria entered by the user are sent to the server as an HTTP request. The input data is in text or numeric format, and is used as search criteria for subsequent query processing.

[1468] Step 4: Execute a database query

[1469] The server executes a query to the database based on the user's search criteria received in step 3. Specifically, it generates and executes an SQL query to filter information on medical institutions that match the criteria. This filtering extracts matching information, formats it in JSON format, and sends it to the terminal. The query input is the search criteria, and the output is information on medical institutions that match the criteria.

[1470] Step 5: Viewing search results

[1471] The device analyzes the filtered information received from the server and displays it in a format that is easy for the user to view. Specifically, it uses JavaScript and D3.js to display search results as lists and graphs. The user can scroll through these results to check detailed information about each medical institution. The displayed information includes the name of the medical institution, reputation score, median fee, and statistical information on the number of deliveries and delivery times, and the user can select the most suitable medical institution based on this information.

[1472] Step 6: Select a medical institution

[1473] The user selects the most suitable medical institution based on the information displayed on the device. Specifically, by clicking on each entry in the list or graph, the user can check detailed information and narrow down the options. The information selected by the user is sent to the server and saved as the final selection result. This selection process allows the user to find the medical institution that is most suitable for them.

[1474] Through the above steps, users can efficiently obtain information on reliable medical institutions and select the location that is best suited to them.

[1475] (Application example 1)

[1476] 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."

[1477] Conventional hospital selection support systems are limited to collecting and analyzing information from limited data sources and are unable to improve the efficiency of large-scale production facilities and factories. The objective of this invention is to apply the usefulness of hospital selection support systems to factories and achieve improved production efficiency. Specifically, the objective is to maximize the operating efficiency of the entire factory by collecting and analyzing production line data and providing users with optimal production settings.

[1478] 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.

[1479] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median fees, and medical statistical information, means for querying a database based on a user's search criteria and filtering information that matches the criteria, means for providing the filtered information to the user, means for collecting production data from sensors in a factory, means for analyzing the collected data and calculating production efficiency and error rates, and means for providing optimal production line settings based on the analysis results. This makes it possible to apply the technology of the hospital selection support system to improving factory production efficiency.

[1480] "Means for collecting information from the hospital's official website" refers to technologies and methods for automatically obtaining fee information and facility information published on the hospital's official website.

[1481] "Means of collecting information from review sites" refers to technologies and methods for automatically obtaining data about hospitals and doctors from review sites that aggregate user experiences and opinions.

[1482] "Means for collecting medical data held by medical institutions" refers to technologies and methods for automatically obtaining information about medical treatment from medical record databases managed by hospitals and clinics.

[1483] "Means for analyzing collected information and generating reputation scores, median fees, and medical statistical information" refers to technologies and methods for analyzing collected data and generating numerical scores for the reputation of hospitals and doctors, median fees, and medical statistical data for each doctor.

[1484] "Means of querying a database based on user search criteria and filtering information that matches the criteria" refers to a technology or method that searches a database according to search criteria specified by the user and extracts information that matches those criteria.

[1485] The "means for providing filtered information to a user" refers to an interface or method for providing information extracted by a query to a user.

[1486] "Means of collecting production data from sensors within the factory" refers to technologies and methods for automatically acquiring production-related data from various sensors installed within the factory.

[1487] "Means for analyzing collected data and calculating production efficiency and error rate" refers to techniques and methods for analyzing data collected from sensors and calculating production efficiency and error rate as a result.

[1488] "Means for providing optimal production line settings based on analysis results" refers to technologies and methods that propose optimal production line settings and improvement measures to users based on the results of data analysis.

[1489] The following describes in detail the mode for carrying out the present invention. This system is designed to optimize production efficiency within a factory and applies the technology of a hospital selection support system. The system has three main components: a server, a terminal, and a user.

[1490] Server Operation

[1491] Data collection

[1492] The server collects production data from various sensors installed in the factory, such as production volume, production speed, error rate, equipment usage, etc. It also collects visual and audio information from cameras and microphones in the factory.

[1493] Data analysis

[1494] The server analyzes the collected data. For example, visual information is analyzed using image analysis software to identify production efficiency and error patterns. This is done using Google Cloud's BigQuery and AWS's Athena, which analyze the data in real time. Natural language processing (NLP) technology is applied to the collected data, and spacy and NLTK are used to extract problems and areas for improvement from communication logs within the factory.

[1495] Providing information

[1496] The server generates optimal production line settings based on the analysis results and provides this information to the user, using Python's Dash and Tableau to visualize the analysis results and present them to managers as reports.

[1497] Device behavior

[1498] User Interface

[1499] The terminal provides an interface where the user can enter search criteria. For example, when the user enters "Please tell me the optimization settings for the production line next week," the terminal sends this information to the server. The information received from the server is formatted into an easy-to-read format (e.g., list or graph) and displayed to the user.

[1500] User Actions

[1501] Search and Select

[1502] Users access the system using a web browser on their device or a dedicated app, input search criteria such as desired production efficiency and error rate, and adjust the production line based on the optimal settings returned by the server.

[1503] Specific examples

[1504] When a factory wants to optimize the production time of a new production line, the system generates the following prompt:

[1505] "What settings can we use to optimize the average production time and reduce the error rate on our new production line? Please suggest optimal settings taking into account the current production volume, machine usage, and worker communication log data."

[1506] Based on the analysis results of these prompts, the system provides the user with optimal production settings, ensuring efficient operation of the factory's production line. This system allows users to quickly obtain reliable information to improve factory production efficiency.

[1507] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1508] Step 1:

[1509] Users access the system via a terminal and input search criteria to find the optimal settings for a specific production line. These criteria include requests for improving production efficiency and reducing error rates. The search criteria are then sent from the terminal to the server.

[1510] Input: Search criteria entered by the user

[1511] Output: Sending search criteria to the server

[1512] Step 2:

[1513] The server collects real-time production data from various sensors in the factory, including production volume, production speed, error rate, equipment usage, etc. It also captures visual and audio information from cameras and microphones in the factory.

[1514] Input: Data from various sensors and cameras in the factory

[1515] Output: Acquired production data and visual and audio information

[1516] Step 3:

[1517] The server stores the collected data in a database and prepares it for analysis: production data is stored in a database, visual information is analyzed using image analysis software, and audio information is converted into text using natural language processing technology.

[1518] Input: Collected production data, and visual and audio information

[1519] Output: A dataset ready for analysis

[1520] Step 4:

[1521] The server uses Google Cloud's BigQuery and AWS's Athena to perform real-time analysis of the collected data, extracting important indicators such as production efficiency and error rates. It also uses spacy and NLTK to analyze communication logs and identify problems and areas for improvement.

[1522] Input: A prepared dataset

[1523] Output: Extracted indicators such as production efficiency, error rate, problems and areas for improvement

[1524] Step 5:

[1525] The server generates optimal production line settings based on the analysis results, including revisions to machine operation settings and work procedures. The generated settings along with the analysis results are converted into a format that can be displayed on the user interface.

[1526] Input: Extracted metrics

[1527] Output: Optimal production line settings and data for user interface display

[1528] Step 6:

[1529] The server sends the optimal production line settings to the terminal. The terminal receives this information and displays it in a format that is easy for the user to view (lists and graphs). For example, the dashboard displays trends in production efficiency and error rates, and provides specific improvement suggestions.

[1530] Input: Data for optimal production line configuration and user interface display

[1531] Output: User-friendly formatted display content

[1532] Step 7:

[1533] The user can then confirm and implement the optimal settings for the production line provided via the terminal. Implementing the settings is expected to improve the production efficiency of the entire factory and reduce the error rate. Furthermore, the user can adjust the search conditions again as needed and obtain the optimal settings again.

[1534] Input: Optimal production line settings (display content)

[1535] Output: Implemented optimal settings and increased production efficiency

[1536] 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.

[1537] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[1538] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[1539] Server Operation

[1540] Data collection

[1541] The server accesses the official websites of pre-defined hospitals and scrapes information on fees and facilities. It also uses the APIs of major review sites to obtain review data on hospitals and doctors. It also obtains data such as the number of deliveries and delivery times in the past, and the experience of each doctor, from medical institution databases.

[1542] Data analysis

[1543] The server analyzes the collected data, using natural language processing technology to calculate a reputation score from word-of-mouth data and a median fee from fee information. It also analyzes medical data to generate medical statistics such as the number of deliveries per doctor, average delivery time, and success rate.

[1544] Emotion recognition by emotion engine

[1545] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, input text, etc. while using the system. Based on this emotional data, it determines whether the user is relaxed, anxious, or impatient.

[1546] Providing information

[1547] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is very anxious, it will prioritize recommending highly rated hospitals that will give them a sense of security. The filtered information is then formatted in JSON format or similar and sent to the device.

[1548] Device behavior

[1549] User Interface

[1550] The device provides an interface where the user can enter search criteria. When the user enters the criteria and presses the search button, the information is sent to the server. The device then formats the information received from the server into an easy-to-read format (e.g., list or graph) and displays it to the user. The device also uses a camera to recognize the user's facial expressions and a microphone to analyze their voice, collecting emotional data about the user.

[1551] User Actions

[1552] Search and Select

[1553] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[1554] Specific examples

[1555] Example 1: Collecting data from a hospital's official website

[1556] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[1557] Example 2: Review analysis

[1558] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[1559] Example 3: Analysis of medical data

[1560] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[1561] Example 4: Emotional awareness and adaptation

[1562] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that are committed to providing a relaxing environment.

[1563] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[1564] The processing flow will be explained below.

[1565] Step 1: Data collection (hospital official website)

[1566] The server accesses the official website of each hospital based on a pre-configured list.

[1567] The server scrapes HTML data from pricing information pages and facility introduction pages.

[1568] The server temporarily stores the scraped raw data.

[1569] Step 2: Data collection (review sites)

[1570] The server obtains review data about specific hospitals and doctors through the APIs of major review sites.

[1571] The server formats the acquired word-of-mouth data and stores it in a database.

[1572] Step 3: Data collection (medical data)

[1573] The server accesses the medical institution's medical database and collects data such as the number of past births, delivery times, and the experience of each doctor.

[1574] The server converts the collected data into a standard format and stores it in a database.

[1575] Step 4: Analyze review data

[1576] The server analyzes the stored word-of-mouth data using natural language processing technology.

[1577] The server extracts positive or negative ratings from each review and aggregates them to calculate an overall reputation score.

[1578] The server stores the calculated reputation scores in a database.

[1579] Step 5: Analyze pricing data

[1580] The server analyzes the fee information collected from the hospitals.

[1581] The server calculates the median and variance of each fee item and generates basic data for calculating the standard fee.

[1582] The server stores the calculated fee data in a database.

[1583] Step 6: Analyze medical data

[1584] The server analyzes the medical data and generates statistical information such as the number of deliveries performed by each doctor, average delivery time, and success rate.

[1585] The server stores the calculated statistical information in a database.

[1586] Step 7: Collect emotion data

[1587] The device captures the user's facial expression data through a camera.

[1588] The terminal uses a microphone to collect voice data of the user.

[1589] The device analyzes the collected facial and voice data in real time to determine the user's emotional state.

[1590] Step 8: Receiving a search request

[1591] Users access the search screen using their device's web browser or a dedicated app.

[1592] The user enters search criteria such as the desired area, price, reputation score, etc., and presses the search button.

[1593] The terminal transmits the user's emotion data to the server together with the input search conditions.

[1594] Step 9: Filtering data based on conditions and sentiment

[1595] The server queries the database based on the user's search criteria and emotional state.

[1596] The server extracts data on hospitals and doctors that match the conditions and emotions and generates a list.

[1597] For example, if the user is in an anxious state, the server will preferentially filter out hospitals that are highly reliable and have a high rating that can alleviate the user's anxiety.

[1598] Step 10: Generate a response with search results

[1599] The server converts the extracted data into an appropriate format, such as JSON.

[1600] The server transmits the converted data to the terminal.

[1601] Step 11: Viewing search results

[1602] The terminal analyzes the received data and displays it in a format that is easy for the user to understand (e.g., list, graph, table).

[1603] The user selects the most suitable maternity hospital and doctor based on the displayed information.

[1604] The above are the specific processing steps in this system.

[1605] Example 2

[1606] 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."

[1607] Conventional maternity hospital recommendation systems do not take into account the user's emotional state when making recommendations, making it difficult for users to select a maternity hospital with confidence. Childbirth is a particularly stressful event for users, so providing appropriate information based on the user's emotional state is essential. Furthermore, the difficulty of incorporating human opinions and emotions into the analysis of collected data limits the improvement of user satisfaction. Therefore, a system that can filter information based on the user's emotional state and provide highly reliable recommendations is needed.

[1608] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating reputation scores, median prices, and medical statistical information, means for analyzing the review data using natural language processing technology in analyzing the collected information, means for recognizing user emotions and filtering based on the emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions, and means for providing the filtered information to the user. This allows for more reliable information to be provided quickly, taking the user's emotional state into consideration, enabling the user to select an appropriate maternity hospital with peace of mind.

[1609] An "official hospital website" is an online information site officially operated by a medical institution, where various information such as medical departments, medical staff, facilities, and fees is made public.

[1610] A "review site" is a website where users can post their own experiences and opinions, which can then be viewed and evaluated by other users. Medical review sites often post reviews and ratings of specific medical institutions and doctors.

[1611] "Medical data" refers to patient medical records, medical procedure histories, and data related to the work of doctors and other medical staff held by medical institutions. Examples include the number of deliveries and delivery times in the past, and the experience of each doctor.

[1612] "Natural language processing technology" is a technology that enables computers to understand, generate, and analyze natural language, enabling semantic analysis and emotional assessment of text data.

[1613] An "emotion engine" is software or algorithm for determining a user's emotional state, collecting and analyzing emotional data from the user's facial expressions, voice, and input text.

[1614] A "reputation score" is a number calculated from word-of-mouth data and other evaluation data, and indicates the overall evaluation of a particular medical institution or doctor.

[1615] The "median price" is the median value of the collected price data, and is a statistical value that represents the center of the distribution.

[1616] "Medical statistical information" is statistical information generated based on collected medical data, and includes the number of births per doctor, average birth time, success rate, etc.

[1617] "Filtering" is the process of selecting necessary information from collected and analyzed data based on specific criteria.

[1618] "User search conditions" are conditions such as desired area, price, reputation score, etc. that a user inputs when using the system.

[1619] "User emotional state" refers to the psychological state the user experiences when using the system, including relaxation, anxiety, tension, etc.

[1620] "Querying a database" is the operation of making an inquiry to a database to retrieve specific data.

[1621] "User Device" means a device used by a user to access the system and input and retrieve information, including a personal computer, smartphone, tablet, etc.

[1622] As an embodiment of the present invention, we will explain in detail a maternity hospital recommendation system that incorporates an emotion engine that recognizes the user's emotions. This system collects and analyzes official hospital websites, review sites, and medical data held by medical institutions, and provides appropriate maternity hospital information based on the user's search criteria and emotional state.

[1623] The system has three main components: a server, a terminal, and a user. The server collects and analyzes information and recognizes emotions using an emotion engine, while the terminal provides an interface for users to access the system. Users access the system through their terminal and obtain the information they need.

[1624] Server Operation

[1625] Data collection

[1626] The server accesses the official websites of pre-configured hospitals and collects information on fees and facilities by scraping. Examples of software used include Python's "Beautiful Soup" and "Scrapy." It also uses the APIs of major review sites to obtain review data on hospitals and doctors. For example, "TrustPilot API" and "Google Places API" are used. Furthermore, data such as the number of past deliveries, delivery times, and the experience of each doctor is obtained from medical institution databases. "SQLAlchemy" and "Pandas" are used to connect to the database.

[1627] Data analysis

[1628] The server analyzes the collected data. For example, natural language processing technology is used to calculate reputation scores from the collected review data. Specific tools include "NLTK" and "spaCy." Statistical processing tools such as "NumPy" and "Pandas" are also used to calculate median prices from fee information. "SciPy" and "Matplotlib" are used to analyze medical data and generate medical statistics.

[1629] Emotion recognition by emotion engine

[1630] The server analyzes the user's emotional data sent from the device. The emotion engine determines the user's emotional state from facial expressions, voice, and input text while using the system. To achieve this, OpenCV is used for facial expression recognition, Google Cloud Speech-to-Text for voice analysis, and BERT for text analysis.

[1631] Providing information

[1632] The server queries the database based on the user's search criteria and emotional state, and filters information that matches the criteria and emotion. For example, if the user is feeling anxious, SQLAlchemy and Flask are used to recognize this state and prioritize recommendations of highly rated hospitals that will give the user a sense of security. The filtered information is formatted in JSON format or similar and sent to the device as an HTTP response.

[1633] Device behavior

[1634] User Interface

[1635] The terminal provides an interface that allows the user to enter search criteria. This interface is built using the web application frameworks "React.js" and "Vue.js." When the user enters criteria and presses the search button, the information is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format (for example, a list or graph) on the terminal and displayed to the user using "Chart.js" or "D3.js."

[1636] The device also has the ability to recognize the user's facial expressions with a camera and analyze their voice using a microphone, thereby collecting emotional data about the user. These functions allow the server to filter and provide the user with information about the most suitable maternity hospitals based on the user's search criteria and emotional state.

[1637] User Actions

[1638] Users access the system using their device's web browser or a dedicated app. They enter search criteria such as their desired area, price, and reputation score. They are also encouraged to grant permission to access the camera and microphone so that the system can collect user emotional data. Search results are displayed clearly on the device, allowing users to select the maternity hospital that best suits them.

[1639] Specific examples

[1640] Example 1: Collecting data from a hospital's official website

[1641] The server accesses the official website of "Hospital A" and scrapes the HTML of the fee information page. The extracted fee data is temporarily stored and used for analysis.

[1642] Example 2: Review analysis

[1643] The server obtains review data about "Obstetrics and Gynecology Clinic B" through the API of "Review Site B." The obtained data is analyzed using natural language processing to calculate a reputation score.

[1644] Example 3: Analysis of medical data

[1645] The server accesses the medical institution's medical database and obtains the number of deliveries and delivery times handled by "Doctor C" over the past year. This data is analyzed to generate statistical information, which is used as the basic data to provide to users.

[1646] Example 4: Emotional awareness and adaptation

[1647] When a user enters search criteria and presses the search button, the device captures the user's facial expression through the camera and recognizes their voice through the microphone. The emotion engine analyzes this, and if it determines that the user is nervous, the server will prioritize filtering and provide highly rated hospitals that offer a relaxing environment.

[1648] Prompt Sentence Examples

[1649] If the user is unsure, prioritize recommending highly rated hospitals that will give them peace of mind. Additionally, provide information on the best maternity hospitals based on the user's search criteria (area, price, reputation score).

[1650] The embodiments of the present invention enable users to quickly obtain reliable information and select a more appropriate maternity hospital based on their emotional state, which is expected to significantly reduce the burden on users and allow them to make an appropriate selection with peace of mind.

[1651] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1652] Step 1: Data collection

[1653] The server accesses the hospital's official website and collects pricing and facility information by scraping. A URL is given as input, and HTML data is retrieved and analyzed based on this. Specifically, pricing information is extracted from the HTML using Python's "Beautiful Soup" and "Scrapy" and temporarily stored in a database. Similarly, review data is collected using the APIs of major review sites, and the JSON data obtained from the review sites is analyzed and stored. Furthermore, the server accesses the medical institution's medical database and obtains medical data such as the number of past deliveries and delivery times. SQLAlchemy and Pandas are used to extract the necessary information from the database and temporarily store it. This completes the server's collection of information from the official website, review sites, and medical database.

[1654] Step 2: Data analysis

[1655] The server analyzes the data collected in step 1. The collected data is used as input, and based on this, a reputation score, median price, and medical statistical information are generated. First, the review data is analyzed using natural language processing technology to calculate a reputation score. Specifically, tools such as "NLTK" and "spaCy" are used to extract sentiment from the review text and score it. In addition, to calculate the median price from the fee information, statistical processing of the fee data is performed using "NumPy" and "Pandas." For the medical treatment data, "SciPy" and "Matplotlib" are used to generate medical statistical information such as the number of deliveries per doctor, average delivery time, and success rate. As a result, the server obtains a reputation score, median price, and medical statistical information as the analysis results.

[1656] Step 3: Emotion Recognition

[1657] The server receives input data (facial expressions, voice, and text) sent from the device to recognize the user's emotional data. Specifically, the device captures facial expressions with a camera and performs facial expression analysis using OpenCV. It also collects audio data with a microphone, converts the audio into text using Google Cloud Speech-to-Text, and analyzes the text using a natural language processing model such as BERT. This process identifies the user's emotional state and sends it to the server as emotional data, such as relaxed, anxious, or nervous. This allows the server to recognize the user's emotional state.

[1658] Step 4: Provide information

[1659] The server provides information based on the analysis results obtained in step 2 and the emotional state recognized in step 3. Specifically, it queries the database for matching maternity hospital information using the user's search criteria and emotional state as input. Filtering is performed based on the criteria, and if the user is feeling anxious, for example, it prioritizes recommendations of highly rated hospitals that offer a relaxing environment. The filtered information is formatted into JSON using "SQLAlchemy" and "Flask" and sent to the terminal as an HTTP response. This allows the server to provide the user with appropriate maternity hospital information.

[1660] Step 5: User Interface

[1661] The device provides an interface for users to enter search criteria and display information received from the server. As input, it receives the search criteria entered by the user (area, price, reputation score) and JSON data from the server. Specifically, a search form is built using "React.js" or "Vue.js," and when the search button is pressed, a request is sent to the server via "AJAX" or "Fetch API." The information received from the server is formatted into an easy-to-read format using "Chart.js" or "D3.js" and displayed to the user. In addition, by allowing access to the camera and microphone, appropriate emotional data is sent to the server. This allows the device to provide the maternity hospital information searched for by the user in an easy-to-read format.

[1662] (Application example 2)

[1663] 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."

[1664] Conventional maternity hospital recommendation systems and customer service systems provide uniform information and services without considering the user's emotional state, making it impossible to adequately alleviate the user's anxiety and tension. It is also difficult to recognize the user's emotions in real time and provide appropriate responses based on them. This reduces user satisfaction and poses the risk of inappropriate information and customer service being provided.

[1665] 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.

[1666] In this invention, the server includes means for collecting information from official hospital websites, means for collecting information from review sites, means for collecting medical data held by medical institutions, means for analyzing the collected information and generating evaluation indexes, median fees, and medical statistical information, means for recognizing customer emotions, means for querying a database based on the user's search conditions and emotional state and filtering information that matches the conditions and emotions, and means for providing the filtered information to the user, thereby enabling appropriate information provision and customer service that takes the user's emotional state into consideration.

[1667] The "method of collecting information from the official hospital website" refers to the method of collecting fee information and facility information from the official internet page of the health institution by scraping.

[1668] "Means for collecting information from review sites" refers to methods for obtaining evaluation data and comments from online review sites.

[1669] "Means for collecting medical data held by medical institutions" refers to methods for extracting past treatment records and doctor experience from medical databases held by medical facilities.

[1670] "Means for analyzing collected information and generating evaluation indexes, median prices, and medical statistical information" refers to a method for processing acquired data using natural language processing and statistical analysis to generate hospital reputation scores, median prices, and medical statistical information.

[1671] "Means for recognizing customer emotions" refers to technology for determining a user's emotional state from their facial expressions and voice.

[1672] "Means for querying a database based on a user's search criteria and emotional state, and filtering information that matches the criteria and emotions" refers to a method for searching a database based on the user's specified criteria and emotions at the time, and extracting appropriate information.

[1673] A "means for providing filtered information to a user" is a method for organizing and displaying selected information on a user's device.

[1674] "Natural language processing" is a technology that allows computers to analyze and understand human language.

[1675] "Evaluation index" is a numerical representation of reviews and reputations of hospital and medical institution services.

[1676] "Median price" refers to the median of a statistical price for a particular service.

[1677] "Medical statistical information" refers to statistical data such as the number of births and average treatment times generated based on medical data.

[1678] A "search condition" indicates a specific attribute or requirement that a user is looking for.

[1679] "Emotional state" refers to the emotions felt by the user when using the system.

[1680] The following describes in detail the mode for carrying out the present invention. The present invention is a system that utilizes an emotion engine that recognizes user emotions, collects and analyzes medical data held by official hospital websites, review sites, and medical institutions, and provides appropriate information based on the user's search criteria and emotional state. It also includes an application example specifically aimed at improving customer service in brick-and-mortar stores.

[1681] Server Operation

[1682] The server operates using the following hardware and software.

[1683] 1. Data Collection

[1684] The server uses scraping technology to collect pricing and facility information from hospitals' official websites, obtains reviews and comments via APIs of review sites, and extracts medica...

Claims

1. and how to gather information from the hospital's official website. How to collect information from review sites, A means of collecting medical data held by medical institutions; a means for analyzing the collected information to generate reputation scores, median prices, and medical statistics; means for querying the database based on user search criteria and filtering information that meets the criteria; means for providing the filtered information to a user; A system including:

2. 10. The system of claim 1, wherein the collected information is analyzed using natural language processing.

3. The system according to claim 1, wherein the system transmits search conditions to a server based on a user's input, and displays data received from the server on the user device.

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