system

Abstract

We provide the system. [Solution] A means of collecting user input information and creating a profile regarding learning aspirations, A means of collecting public and private information about research laboratories and seminars at educational institutions and forming a database, A method using an artificial intelligence engine to recommend suitable research laboratories and seminars based on collected information, A means of presenting users with information on recommended research laboratories and seminars, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, when high school students and their instructors choose a university laboratory or seminar, they often only refer to information based on deviation values, and there may be choices that do not match their essential interests and learning aptitudes. As a result, the satisfaction in university life may decrease, which may also have an adverse impact on future career formation. In addition, there is also a problem that it is difficult for companies to appropriately discover potential excellent talents. Based on these problems, it is required to support high school students to make career choices based on their own interests and aptitudes.

Means for Solving the Problems

[0005] This invention provides a system that collects user input information and creates a profile of the user's learning aspirations. This system collects both public and private information about research labs and seminars at educational institutions and forms a database. Furthermore, using an artificial intelligence engine, it recommends suitable research labs or seminars based on the user's profile and presents this information to the user. This allows users to make choices based on their interests and aptitudes, rather than solely on academic rankings, thereby reducing mismatches between universities. It also enables companies to efficiently identify talented individuals.

[0006] "Users" refer to individuals such as high school students who use this system and teachers who guide them in their career paths.

[0007] "Input information" refers to information that users provide to the system, such as their interests, aspirations, and academic abilities.

[0008] A "profile" is a collection of data related to learning aspirations and aptitudes, created based on the input information provided by the user.

[0009] "Educational institutions" refer to organizations and facilities that provide higher education, such as universities and vocational schools.

[0010] A "research laboratory" is an academic group or organization that operates within an educational institution based on a specific research theme.

[0011] "Zemi" refers to small-group academic instruction activities or seminar-style classes conducted at educational institutions.

[0012] "Public information" refers to information that is made available to the general public, such as on university websites and in brochures.

[0013] "Confidential information" refers to information that is accessible only under specific conditions and is typically provided directly between educational institutions and systems.

[0014] A "database" is an information storage system that systematically organizes and stores the collected information.

[0015] An "artificial intelligence engine" refers to a collection of algorithms and computer programs that process the user's profile and information of educational institutions to make optimal recommendations.

[0016] A "compatibility score" is a quantification of the compatibility between the user's profile and a laboratory or seminar of an educational institution.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is a system that helps high school students and their instructors appropriately select university research labs and seminars based on their interests and learning aspirations. The system consists of a server, terminals, and an artificial intelligence engine. Users access the system using the terminals, input their areas of interest, desired career paths, academic ability, etc., and set up a profile.

[0039] The server aggregates publicly available information about research labs and seminars at various educational institutions, collected from the internet, as well as confidential information provided by partner educational institutions, into a database. This information includes details such as course content, research themes, supervising professors, and past research achievements. The server regularly updates this information to maintain a constantly up-to-date database.

[0040] The artificial intelligence engine uses user profile information and database information on research labs and seminars to calculate a suitability score based on an algorithm. It utilizes natural language processing technology to analyze text data and perform optimal matching to identify candidates that best match the user's interests and academic performance.

[0041] The server then selects a list of recommended research labs and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. Through this information, the user can make the choice that best suits their interests and future goals.

[0042] As a concrete example, consider a user who sets a profile stating, "I am interested in the field of robotics and want to be involved in creative projects." Upon receiving this information, the server retrieves information on relevant educational institutions from its database and analyzes the degree of matching using an artificial intelligence engine. As a result, recommendations such as "Robotics Lab at X University" or "Mechatronics Seminar at Y University" are displayed on the terminal. Based on this information, the user can choose the option that best suits what they want to learn.

[0043] In this way, this system enables users to make more appropriate choices regarding higher education, and also helps companies to efficiently identify promising talent.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user accesses the platform through their device and enters information such as a username, email address, and password to create a new account. The device sends the entered information to the server, and the user's account is registered in the database.

[0047] Step 2:

[0048] Users enter their areas of interest, desired occupations, and current academic information (e.g., grades, mock exam scores) into the terminal to set up a profile. The terminal sends this profile information to the server, which stores it in the database as the user's individual profile.

[0049] Step 3:

[0050] The server collects publicly available information about research labs and seminars at various educational institutions from the internet. Furthermore, it also collects confidential information provided by partner educational institutions to keep the database up-to-date.

[0051] Step 4:

[0052] The server organizes and stores the collected information in a database. This database includes detailed information about research labs and seminars, and serves as the basis for user-profile-based analysis.

[0053] Step 5:

[0054] The AI ​​engine activates, and the server analyzes the database of research labs and seminars based on the user's profile. Here, natural language processing techniques are used to calculate a suitability score for each candidate that reflects their academic interests and career aspirations.

[0055] Step 6:

[0056] The server lists the most suitable research labs and seminars based on the calculated fit score. This list is prepared as a recommendation for the user.

[0057] Step 7:

[0058] The terminal displays a list of recommendations sent from the server. The list includes detailed information about each research lab or seminar (e.g., research topic, professor information, research results), which the user can refer to.

[0059] Step 8:

[0060] Based on the information displayed on the device, users can select the research lab or seminar that best suits their interests and goals, and then proceed with inquiries and enrollment procedures based on that selection.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern higher education, a challenge exists in that it is difficult for individuals to choose educational institutions and research facilities that best suit their interests and academic abilities. Traditional methods make it difficult to find the best option from a vast amount of information, and poor choices can have detrimental effects on career paths and future prospects.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information regarding research facilities and seminars of educational institutions and forming a data set, and means for using artificial intelligence components to recommend suitable research facilities and seminars based on the collected information. This makes it possible for users to easily find the best option for themselves from a vast amount of information.

[0066] A "user" is an individual who attempts to obtain information about educational institutions and research facilities by operating the system.

[0067] "Input information" refers to data that users provide to the system regarding their learning aspirations, interests, and academic ability.

[0068] A "learning aspirations profile" is individual profile information compiled by the system based on the user's interests and desired career path.

[0069] An "educational institution" is an organization that provides higher education, including research facilities and seminars.

[0070] "Information regarding research facilities and seminars" refers to a collection of information that includes detailed data on course content, research themes, supervising professors, and past research results.

[0071] "Publicly available information" refers to information about educational institutions that is widely provided to the public on the internet.

[0072] "Confidential information" refers to data that is provided to a limited extent by educational institutions and is not publicly available.

[0073] A "data set" is a database that compiles publicly available and confidential information about educational institutions and research facilities collected by the server.

[0074] "Artificial intelligence components" refer to the technical engines used to analyze data and identify research facilities and seminars suitable for the user.

[0075] "Suitable research facilities and seminars" refer to research facilities and seminars at educational institutions that have characteristics that best match the user's input information.

[0076] "Comparison and favorites features" are support tools that allow users to evaluate and save recommended options, helping them make the best decision.

[0077] This invention is a system that assists users in selecting the most suitable educational institution according to their individual learning aspirations and interests. Its basic components consist of a server, a terminal, and an artificial intelligence engine.

[0078] First, users access the system using their device and input information such as their areas of interest, career path, and academic ability. Based on this information, the device sets up the user's profile and sends it to the server. The device can be a regular computer or smartphone, and it connects to the system via an internet browser or a dedicated application.

[0079] Next, the server uses publicly available information about research facilities and seminars at each educational institution, collected from the internet, along with confidential information provided by partner educational institutions, to form a comprehensive data set. This data includes information such as course content, research themes, supervisors, and past research achievements. The server regularly updates this data to ensure the reliability of the information.

[0080] The artificial intelligence engine is used to analyze the degree of fit based on the user's profile information and research information stored on the server. This engine utilizes generative AI models and applies natural language processing technology to analyze the data and identify the educational institution that best matches the user's interests and aspirations. For example, by inputting a prompt sentence based on the user's wishes, such as "I am interested in the field of robotics and would like to be involved in creative projects," the engine will perform the optimal matching.

[0081] Finally, the server creates a list of recommended research facilities and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. The user can compare each option, register them as favorites, and make the choice that best suits their preferences. This system enables users to make well-informed decisions when choosing their career path.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users log in to the system using a terminal and enter information about their areas of interest, desired career path, and academic ability. This information is used to create a personalized user profile. The terminal formats the entered information and sends it to the server as profile data.

[0085] Step 2:

[0086] The server aggregates optimized information from the database based on the received user profile. The server references existing data sets to obtain basic information about research facilities and seminars at relevant educational institutions. During this process, the server checks information from the internet and partner institutions, updating the database accordingly.

[0087] Step 3:

[0088] The server passes the user's profile information and acquired educational institution information to an artificial intelligence engine. This engine uses a generative AI model and natural language processing techniques to analyze the data. To match the profile information and research information as input, the engine performs advanced data analysis to identify the most relevant options.

[0089] Step 4:

[0090] Based on the suitability score obtained from the artificial intelligence engine, the server generates a list of recommended research facilities and seminars. Here, the list is sorted in order of priority according to the suitability score. The result is a refined recommendation list.

[0091] Step 5:

[0092] The server sends the created recommendation list to the terminal. The terminal provides an interface that allows the user to freely browse the list. The terminal utilizes detailed information display, favorites registration, and comparison functions to create an environment where the user can make the best choice.

[0093] Step 6:

[0094] Users refer to information provided via their device to learn more about research facilities and seminars that interest them. They make selections based on a list of recommendations and determine their next actions. They can save their selected information and conduct further in-depth inquiries.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] It is difficult for motivated learners to efficiently find research labs or seminars that match their interests within a specific educational institution. Furthermore, the inability to obtain relevant information in real time on campus hinders the selection of the optimal learning environment.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information about research laboratories and seminars at educational institutions and forming a database, and means for acquiring location information and performing image recognition of surrounding objects. This makes it possible for users to visually obtain information about research laboratories and seminars that match their interests in real time on a specific campus.

[0100] "Users" refers to individuals who use this system to find research labs or seminars that match their learning aspirations.

[0101] "Input information" refers to information about the user's interests and learning goals that they provide to the system.

[0102] A "profile" refers to a collection of information about an individual's learning aspirations, created based on their interests and academic abilities.

[0103] "Educational institutions" refer to organizations that provide higher education, such as universities.

[0104] "Public and confidential information" refers to information made publicly available by educational institutions and information that is only available within specific organizations.

[0105] A "database" refers to a system for systematically organizing and recording collected information.

[0106] An "artificial intelligence engine" refers to an automated algorithm that recommends the most suitable research lab or seminar based on the user's profile and information about their educational institution.

[0107] "Location information" refers to data about the current location of the user or device.

[0108] "Methods for image recognition of objects" refers to technologies that use a device's camera or similar equipment to identify surrounding objects and analyze that information.

[0109] "Presenting visually" refers to providing information in a way that users can understand visually.

[0110] This invention provides a system that utilizes information processing terminals such as smart glasses and smartphones to enable users with a desire to learn to obtain information about research labs and seminars at educational institutions in real time.

[0111] The server collects information about the user's interests and learning goals and generates a learning aspiration profile based on this information. This identifies potential research labs and seminars at educational institutions that match the user's interests.

[0112] In this system, the server processes the collected data using an artificial intelligence engine and performs analysis using natural language processing technology. Using deep learning algorithms, it calculates a compatibility score between the user's profile and the educational institution information stored in the database. Based on this analysis, it identifies the most suitable research labs and seminars and generates a recommendation list.

[0113] When a user visits the campus, the device uses GPS and image recognition technologies such as OpenCV to recognize their current location and surrounding objects. Based on the recognized information, it visually displays information about relevant educational institutions in real time.

[0114] For example, if user A sets a profile stating "I am interested in robotics and would like to participate in creative projects" and enters the engineering campus, the smart glasses will recognize the engineering building and display information to user A stating that "the Robotics Laboratory is currently working on a cutting-edge project and is highly suitable."

[0115] An example of a prompt might be: "Create an application that uses AI to recognize relevant buildings on a university campus and provide real-time information to help the user find a research lab of interest. The inputs are the user's areas of interest and past learning history."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server receives input from the user regarding their interests, academic ability, and learning aspirations. Based on this, it generates a profile. The input consists of the user's areas of interest and learning history, and by converting this into structured data, the server obtains the profile as output.

[0119] Step 2:

[0120] The server collects public and private information about educational institutions from various sources and updates the database. New data is entered and compared with existing data to produce the most up-to-date information on educational institutions.

[0121] Step 3:

[0122] The device acquires its current location information, uses its camera to perform image recognition on surrounding objects, and identifies them. The input consists of location data and image data, and based on this, it generates output in the form of location and object identification.

[0123] Step 4:

[0124] The server uses an artificial intelligence engine to calculate a compatibility score with research labs and seminars at educational institutions, based on profile information and a database. The input is profile and database information, and the server generates a compatibility score as output through text analysis using natural language processing techniques and calculations using deep learning algorithms.

[0125] Step 5:

[0126] The server generates a list of recommended research labs and seminars based on the calculated fit score. This list takes the profile and fit score as input and outputs the names of the most suitable research labs and seminars.

[0127] Step 6:

[0128] The terminal visually displays relevant recommended research lab information to the user in real time. The input is recommended information from the server, and the output is the presentation of this information to the user via a visual interface.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system that recommends appropriate university research labs and seminars based on the user's personal interests and academic ability, and also incorporates an emotion engine to recognize the user's emotional state and optimize the recommendations. This system consists of a server, a terminal, an artificial intelligence engine, and an emotion engine.

[0131] Users access the platform through their devices and create new accounts. During this process, they create a profile by entering information about their areas of interest, desired fields of study, and academic abilities. This information is sent to the server and registered in the database.

[0132] The server collects publicly available information about university research labs and seminars via the internet, and also obtains confidential information from partner educational institutions. This information is registered in and maintained in a database.

[0133] The artificial intelligence engine analyzes the user's profile and information in the database to identify suitable research labs and seminars. This utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities, and select the most optimal candidates.

[0134] Furthermore, this system integrates an emotion engine that recognizes the user's emotional state. It analyzes the user's emotional state based on text, voice, or dialogue history. This emotional information is reflected in the recommendations, providing optimal recommendations tailored to the user's mood and motivation.

[0135] For example, if a user has a profile and emotional information indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will select suitable research labs and seminars. Furthermore, taking into account the emotion of "anxiety" recognized by the emotional engine, it will further emphasize and recommend candidates with supportive environments that can help improve motivation.

[0136] This series of processes will enable users to make more deeply personalized career choices, and allow educational institutions and companies to more efficiently find the right talent.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] Users access the platform through their device and go through the account creation process. They set up their profile by entering their areas of interest, desired career paths, and academic performance data. The device then transmits this information to the server.

[0140] Step 2:

[0141] The server receives user profile information and registers it in the database. This information is fundamental data used in subsequent processing.

[0142] Step 3:

[0143] The server uses web scraping techniques to collect publicly available information about research labs and seminars at educational institutions via the internet. It also obtains confidential information through communication with partner educational institutions, forming and updating a database.

[0144] Step 4:

[0145] The artificial intelligence engine compares the user profile received from the server with the educational institution's information database. Here, it utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities.

[0146] Step 5:

[0147] The emotion engine extracts emotional data from user input and past conversation history. It analyzes text and voice input through the device to identify the user's emotional state.

[0148] Step 6:

[0149] The server comprehensively analyzes the suitability score calculated by the artificial intelligence engine and the emotional data recognized by the emotion engine to create a list of optimal research labs and seminars. In particular, it fine-tunes the recommendations to provide encouragement and reassurance based on the user's emotional state.

[0150] Step 7:

[0151] The terminal displays a list of recommendations sent from the server. The list includes information about each research lab and seminar, along with additional comments and messages tailored to the user's emotional state.

[0152] Step 8:

[0153] Users refer to the above recommendation information and make the selection that best suits their interests and emotional state. Based on their selection, they can then make specific laboratory visits or inquiries through their device.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] In today's educational environment, it is a challenging task for users to quickly and accurately select research facilities and seminars that best suit their interests and learning abilities. Furthermore, providing personalized recommendations while considering the user's emotional state is even more complex. Therefore, there is a need to provide a method for suggesting the most suitable educational options for each user.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for collecting user input information and creating information on learning orientation; means for collecting public and private information on research facilities and seminars of educational organizations and forming an information set; means for using an artificial intelligence model to recommend suitable research facilities and seminars based on the collected information; and means for using an emotion analysis device to analyze the user's emotional state and optimize the recommendation content. As a result, users can obtain the optimal educational options according to their interests and emotional state.

[0159] A "user" refers to an individual who uses the system to receive recommendations based on their own interests and learning abilities.

[0160] "Input information" refers to data related to the user's interests, learning content, and learning ability that they provide to the system.

[0161] "Information regarding learning orientation" refers to information about the academic fields and research themes that users are interested in.

[0162] An "educational organization" refers to any group that conducts academic instruction and research activities, including universities and other educational institutions.

[0163] "Research facilities and seminars" refer to organizational units within an educational institution for conducting specific research or academic pursuits.

[0164] "Public information" refers to information about research facilities and seminars that is publicly available.

[0165] "Confidential information" refers to information that is not publicly available, such as information about research facilities and seminars provided by affiliated educational organizations.

[0166] An "information collection" refers to a dataset used to integrate and systematically manage collected public and private information.

[0167] An "artificial intelligence model" refers to the algorithms and methods used to analyze collected data and provide educational recommendations to users.

[0168] A "emotion analysis device" refers to a technology that analyzes a user's emotional state based on their input and history, and uses this analysis to optimize recommendations.

[0169] This system is built to provide users with the best possible educational options and consists of a server, terminals, an artificial intelligence engine, and an emotion engine.

[0170] The server provides an interface that allows users to access the system through their terminals. Users use their terminals to input information about their areas of interest, preferred learning areas, and learning abilities, creating a profile. This input is sent to the server and registered in the database. The server also collects public and private information about research facilities and seminars within educational organizations from the internet and partner institutions, storing it in the database and maintaining it at all times.

[0171] The artificial intelligence engine uses deep learning algorithms to analyze collected information and user profiles. This analysis calculates a suitability score based on the user's interests and academic abilities to select the most suitable research facilities and seminars. Leveraging natural language processing technology, it analyzes user-generated text and other input information to achieve highly accurate recommendations. The sentiment engine identifies emotions from user input and dialogue history to optimize recommendations for greater personalization.

[0172] For example, if a user has a profile indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will use this information to select research facilities suitable for medical research. Furthermore, the emotion engine will consider the user's "anxiety" and recommend facilities with robust mentoring and support systems to help maintain the user's motivation.

[0173] An example of a prompt to be input into the generating AI model would be, "Please recommend the most suitable research facility based on the user's areas of interest and emotional information." This would make it easier for educational institutions to find suitable personnel and enable individual users to make the best career choices.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users access the platform via their device and enter their areas of interest, desired field of study, and academic information on the new account creation screen. This information includes interests such as "Medical Research" or "Computer Science," as well as academic information such as GPA and test scores. After entering this information, the device sends it to the server. This input is used to create the user's profile. The output is the registration of the user profile information to the server.

[0177] Step 2:

[0178] The server receives profile information sent by the user and stores it in the database. The database stores the information structured using the user ID as the key. At this point, the input is profile data from the terminal, and the server registers this neatly in the database. The output is the registered user profile.

[0179] Step 3:

[0180] The server collects information on research facilities and seminars made public by educational organizations via the internet. Furthermore, it obtains confidential information from partner institutions. This information may be collected using web scraping tools or APIs. The collected information is integrated into a database and stored as an information set. The input is data from the internet, and the output is the storage of information in the database.

[0181] Step 4:

[0182] An artificial intelligence engine analyzes data acquired from the server. Using deep learning algorithms, it calculates suitability scores for research facilities and seminars based on the user's profile and the collected data. Natural language processing techniques are used to analyze the relationships between the profile and the data. The input here is all the data registered on the server, and the output is the suitability score for each candidate.

[0183] Step 5:

[0184] The emotion engine analyzes past conversation history and messages entered by the user to identify their emotional state. Text analysis tools are used for emotion analysis; the input is past text and messages, and the output is identified emotion information. This emotion data is then reflected in the recommendations generated by the artificial intelligence engine.

[0185] Step 6:

[0186] The server integrates information from the artificial intelligence engine and the emotion engine to generate recommendations for the most suitable research facilities and seminars for the user. This information is sent to the terminal, where the user is shown the recommendation level and detailed information. The final input is the analyzed suitability score and emotion information, and the output is the recommendation result provided to the user.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] Traditional recommendation systems for research labs and seminars have a problem in that they cannot make recommendations that take into account the individual feelings of users, and it is difficult to deliver appropriate content that takes into account learning motivation and emotions. In particular, there was a need for a system that takes into account the impact of users' emotional states on learning motivation.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations; means for collecting public and private information regarding research laboratories and seminars of educational institutions and forming an information storage device; means for using calculation means to recommend suitable research laboratories and seminars based on the collected information; and means for using a processing structure that includes emotion analysis means for recognizing the user's emotional state and optimizing the recommendation content. This makes it possible to recommend research laboratories and seminars that are tailored to the individual emotional state of each user.

[0192] "Means for collecting user input information and creating a profile regarding learning aspirations" refers to a function that collects information on the user's interests and academic abilities, and generates an individualized profile that summarizes their learning preferences and goals based on that information.

[0193] "Means for collecting public and private information concerning research laboratories and seminars of educational institutions and forming an information storage device" refers to the process of collecting publicly available information and limited access information provided by schools and research facilities, and constructing a database or similar device for organizing and storing this information.

[0194] "Methods for recommending suitable research laboratories and seminars based on collected information" refers to methods for analyzing user profiles and collected research laboratories and seminar information, and performing computational processing to select the most suitable candidates. This may include artificial intelligence algorithms.

[0195] "Means using a processing structure that includes sentiment analysis means for recognizing the user's emotional state and optimizing recommendation content" refers to a processing device or procedure for determining the emotional state from the text or voice expressed by the user and adjusting the recommendation content to provide information in a manner that matches that emotion.

[0196] A description of the embodiment for carrying out the invention will be given.

[0197] In the system that realizes this invention, the terminal first receives input information from the user and builds a profile of the user based on data related to their interests and academic abilities. The server collects this profile information and plays the role of gathering public and private information about research laboratories and seminars at educational institutions via the internet and creating a database. This process utilizes high-performance database technology and network communication technology.

[0198] Based on the collected data, the server uses an artificial intelligence engine built with Python and TENSORFLOW® to select the research lab or seminar that best matches the user profile. Hugging Face Transformers is used for natural language processing, and a deep learning algorithm is employed to calculate the fit score.

[0199] Furthermore, the server analyzes the user's input text or voice data using the Google® Cloud Natural Language API to recognize the user's emotional state. The results of the emotional analysis are used to optimize recommendations, forming a feedback loop to provide information that aligns with the user's emotions.

[0200] For example, if a user has a profile indicating they are "interested in psychology research but feel anxious in new environments," the AI ​​engine will recommend research labs with well-established support systems within the field of psychology. The system's emotion engine takes into account the emotion of "anxiety" and emphasizes and recommends candidates with active communities that can provide a sense of security.

[0201] An example of a prompt sentence to input into the generative AI model is, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me." This allows the user to receive highly personalized information based on their emotions and select the optimal learning environment.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The device receives input information from the user. Specifically, the user inputs data about their interests, academic ability, desired field of study, and emotions into the device. The input data is compiled into a profile and sent to the server.

[0205] Step 2:

[0206] The server receives profile information sent by the user and stores it in a database. The stored profile information is used to measure the user's interests and academic abilities, and serves as the basis for recommendations.

[0207] Step 3:

[0208] The server collects public and private information about research labs and seminars at educational institutions via the internet and stores this information in a new data storage device. Database cleanup is performed periodically to ensure that data is stored reliably and without duplication.

[0209] Step 4:

[0210] The server uses information from the database to launch an artificial intelligence engine built with Python and TensorFlow to analyze potential research labs and seminars that match the user profile. For natural language processing, Hugging Face Transformers are used to calculate the degree of agreement between the input profile information and the database information. Based on these results, a suitability score for research labs and seminars is calculated, and candidates are selected.

[0211] Step 5:

[0212] The server uses the Google Cloud Natural Language API to analyze the emotional state of the user's input text and voice data. The results of the emotional analysis (e.g., anxiety, excitement, relief) are combined with a relevance score, which is the output of the AI ​​engine, to optimize recommendations.

[0213] Step 6:

[0214] The server sends optimized recommendations to the terminal and presents them to the user on the terminal. The user then uses the displayed information to check the details of the recommended research labs and seminars and utilizes it for career choices. An example of a specific prompt message would be a request like, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me."

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] This invention is a system that helps high school students and their instructors appropriately select university research labs and seminars based on their interests and learning aspirations. The system consists of a server, terminals, and an artificial intelligence engine. Users access the system using the terminals, input their areas of interest, desired career paths, academic ability, etc., and set up a profile.

[0232] The server aggregates publicly available information about research labs and seminars at various educational institutions, collected from the internet, as well as confidential information provided by partner educational institutions, into a database. This information includes details such as course content, research themes, supervising professors, and past research achievements. The server regularly updates this information to maintain a constantly up-to-date database.

[0233] The artificial intelligence engine uses user profile information and database information on research labs and seminars to calculate a suitability score based on an algorithm. It utilizes natural language processing technology to analyze text data and perform optimal matching to identify candidates that best match the user's interests and academic performance.

[0234] The server then selects a list of recommended research labs and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. Through this information, the user can make the choice that best suits their interests and future goals.

[0235] As a concrete example, consider a user who sets a profile stating, "I am interested in the field of robotics and want to be involved in creative projects." Upon receiving this information, the server retrieves information on relevant educational institutions from its database and analyzes the degree of matching using an artificial intelligence engine. As a result, recommendations such as "Robotics Lab at X University" or "Mechatronics Seminar at Y University" are displayed on the terminal. Based on this information, the user can choose the option that best suits what they want to learn.

[0236] In this way, this system enables users to make more appropriate choices regarding higher education, and also helps companies to efficiently identify promising talent.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user accesses the platform through their device and enters information such as a username, email address, and password to create a new account. The device sends the entered information to the server, and the user's account is registered in the database.

[0240] Step 2:

[0241] Users enter their areas of interest, desired occupations, and current academic information (e.g., grades, mock exam scores) into the terminal to set up a profile. The terminal sends this profile information to the server, which stores it in the database as the user's individual profile.

[0242] Step 3:

[0243] The server collects publicly available information about research labs and seminars at various educational institutions from the internet. Furthermore, it also collects confidential information provided by partner educational institutions to keep the database up-to-date.

[0244] Step 4:

[0245] The server organizes and stores the collected information in a database. This database includes detailed information about research labs and seminars, and serves as the basis for user-profile-based analysis.

[0246] Step 5:

[0247] The AI ​​engine activates, and the server analyzes the database of research labs and seminars based on the user's profile. Here, natural language processing techniques are used to calculate a suitability score for each candidate that reflects their academic interests and career aspirations.

[0248] Step 6:

[0249] The server lists the most suitable research labs and seminars based on the calculated fit score. This list is prepared as a recommendation for the user.

[0250] Step 7:

[0251] The terminal displays a list of recommendations sent from the server. The list includes detailed information about each research lab or seminar (e.g., research topic, professor information, research results), which the user can refer to.

[0252] Step 8:

[0253] Based on the information displayed on the device, users can select the research lab or seminar that best suits their interests and goals, and then proceed with inquiries and enrollment procedures based on that selection.

[0254] (Example 1)

[0255] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0256] In modern higher education, a challenge exists in that it is difficult for individuals to choose educational institutions and research facilities that best suit their interests and academic abilities. Traditional methods make it difficult to find the best option from a vast amount of information, and poor choices can have detrimental effects on career paths and future prospects.

[0257] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0258] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information regarding research facilities and seminars of educational institutions and forming a data set, and means for using artificial intelligence components to recommend suitable research facilities and seminars based on the collected information. This makes it possible for users to easily find the best option for themselves from a vast amount of information.

[0259] A "user" is an individual who attempts to obtain information about educational institutions and research facilities by operating the system.

[0260] "Input information" refers to data that users provide to the system regarding their learning aspirations, interests, and academic ability.

[0261] A "learning aspirations profile" is individual profile information compiled by the system based on the user's interests and desired career path.

[0262] An "educational institution" is an organization that provides higher education, including research facilities and seminars.

[0263] "Information regarding research facilities and seminars" refers to a collection of information that includes detailed data on course content, research themes, supervising professors, and past research results.

[0264] "Publicly available information" refers to information about educational institutions that is widely provided to the public on the internet.

[0265] "Confidential information" refers to data that is provided to a limited extent by educational institutions and is not publicly available.

[0266] A "data set" is a database that compiles publicly available and confidential information about educational institutions and research facilities collected by the server.

[0267] "Artificial intelligence components" refer to the technical engines used to analyze data and identify research facilities and seminars suitable for the user.

[0268] "Suitable research facilities and seminars" refer to research facilities and seminars at educational institutions that have characteristics that best match the user's input information.

[0269] "Comparison and favorites features" are support tools that allow users to evaluate and save recommended options, helping them make the best decision.

[0270] This invention is a system that assists users in selecting the most suitable educational institution according to their individual learning aspirations and interests. Its basic components consist of a server, a terminal, and an artificial intelligence engine.

[0271] First, users access the system using their device and input information such as their areas of interest, career path, and academic ability. Based on this information, the device sets up the user's profile and sends it to the server. The device can be a regular computer or smartphone, and it connects to the system via an internet browser or a dedicated application.

[0272] Next, the server uses publicly available information about research facilities and seminars at each educational institution, collected from the internet, along with confidential information provided by partner educational institutions, to form a comprehensive data set. This data includes information such as course content, research themes, supervisors, and past research achievements. The server regularly updates this data to ensure the reliability of the information.

[0273] The artificial intelligence engine is used to analyze the degree of fit based on the user's profile information and research information stored on the server. This engine utilizes generative AI models and applies natural language processing technology to analyze the data and identify the educational institution that best matches the user's interests and aspirations. For example, by inputting a prompt sentence based on the user's wishes, such as "I am interested in the field of robotics and would like to be involved in creative projects," the engine will perform the optimal matching.

[0274] Finally, the server creates a list of recommended research facilities and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. The user can compare each option, register them as favorites, and make the choice that best suits their preferences. This system enables users to make well-informed decisions when choosing their career path.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users log in to the system using a terminal and enter information about their areas of interest, desired career path, and academic ability. This information is used to create a personalized user profile. The terminal formats the entered information and sends it to the server as profile data.

[0278] Step 2:

[0279] The server aggregates optimized information from the database based on the received user profile. The server references existing data sets to obtain basic information about research facilities and seminars at relevant educational institutions. During this process, the server checks information from the internet and partner institutions, updating the database accordingly.

[0280] Step 3:

[0281] The server passes the user's profile information and acquired educational institution information to an artificial intelligence engine. This engine uses a generative AI model and natural language processing techniques to analyze the data. To match the profile information and research information as input, the engine performs advanced data analysis to identify the most relevant options.

[0282] Step 4:

[0283] Based on the fitness scores obtained from the artificial intelligence engine, the server generates a list of recommended research institutions and seminars. Here, a process of sorting the list in order of priority according to the fitness scores is performed. As its output, a refined recommendation list is created.

[0284] Step 5:

[0285] The server sends the created recommendation list to the terminal. The terminal provides an interface that allows the user to freely view the list. The terminal utilizes detailed information display, favorite registration, and comparison functions to prepare an environment in which the user can make an optimal choice.

[0286] Step 6:

[0287] The user refers to the information provided via the terminal and checks the details about the research institutions and seminars of interest. The user makes a selection based on the recommendation list and determines the next action. It is possible to save the selected information or make further in-depth inquiries.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] It is difficult for users with a learning desire to efficiently discover research laboratories and seminars that match their interests within a specific educational institution. Also, there is a problem that relevant information on-site within the campus cannot be obtained in real time, hindering the selection of an optimal learning environment. [[ID=二十九]]

[0291] The specific processing by the specific processing unit 290 of the data processing device in Application Example 1 is realized by the following respective means.

[0292] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information about research laboratories and seminars at educational institutions and forming a database, and means for acquiring location information and performing image recognition of surrounding objects. This makes it possible for users to visually obtain information about research laboratories and seminars that match their interests in real time on a specific campus.

[0293] "Users" refers to individuals who use this system to find research labs or seminars that match their learning aspirations.

[0294] "Input information" refers to information about the user's interests and learning goals that they provide to the system.

[0295] A "profile" refers to a collection of information about an individual's learning aspirations, created based on their interests and academic abilities.

[0296] "Educational institutions" refer to organizations that provide higher education, such as universities.

[0297] "Public and confidential information" refers to information made publicly available by educational institutions and information that is only available within specific organizations.

[0298] A "database" refers to a system for systematically organizing and recording collected information.

[0299] An "artificial intelligence engine" refers to an automated algorithm that recommends the most suitable research lab or seminar based on the user's profile and information about their educational institution.

[0300] "Location information" refers to data about the current location of the user or device.

[0301] "Methods for image recognition of objects" refers to technologies that use a device's camera or similar equipment to identify surrounding objects and analyze that information.

[0302] "Visually presenting" means providing information in a form that can be visually understood by the user.

[0303] This invention provides a system for users with a desire to learn to obtain information about research laboratories and seminars in educational institutions in real time by utilizing information processing terminals such as smart glasses and smartphones.

[0304] The server collects information related to the user's interests and learning, and generates a learning desire profile based on it. Thereby, candidates for research laboratories and seminars in educational institutions that match the user's interests are identified.

[0305] In this system, the server processes the collected data with an artificial intelligence engine and performs analysis using natural language processing technology. Using a deep learning algorithm, a compatibility score between the user's profile and the educational institution information stored in the database is calculated. Based on the results of this analysis, research laboratories and seminars showing the highest compatibility are identified, and a recommendation list is generated.

[0306] When the user visits the campus, the terminal uses image recognition technologies such as GPS and OpenCV to recognize the current location and surrounding objects. Based on the recognized information, information on related educational institutions is visually presented in real time.

[0307] As a specific example, when User A sets a profile of "interested in robotics and wants to participate in creative projects" and enters the campus of the Faculty of Engineering, the smart glasses recognize the building of the Faculty of Engineering and display information to User A such as "The Robotics Institute is conducting the latest project and has a high compatibility."

[0308] Examples of prompt sentences may include content such as "Please generate an application that recognizes the corresponding building on the university campus and provides information in real time using AI to find the research laboratory that the user is interested in. The input is the user's field of interest and past learning history."

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] The server receives input from the user regarding their interests, academic ability, and learning aspirations. Based on this, it generates a profile. The input consists of the user's areas of interest and learning history, and by converting this into structured data, the server obtains the profile as output.

[0312] Step 2:

[0313] The server collects public and private information about educational institutions from various sources and updates the database. New data is entered and compared with existing data to produce the most up-to-date information on educational institutions.

[0314] Step 3:

[0315] The device acquires its current location information, uses its camera to perform image recognition on surrounding objects, and identifies them. The input consists of location data and image data, and based on this, it generates output in the form of location and object identification.

[0316] Step 4:

[0317] The server uses an artificial intelligence engine to calculate a compatibility score with research labs and seminars at educational institutions, based on profile information and a database. The input is profile and database information, and the server generates a compatibility score as output through text analysis using natural language processing techniques and calculations using deep learning algorithms.

[0318] Step 5:

[0319] The server generates a list of recommended research labs and seminars based on the calculated fit score. This list takes the profile and fit score as input and outputs the names of the most suitable research labs and seminars.

[0320] Step 6:

[0321] The terminal visually displays relevant recommended research lab information to the user in real time. The input is recommended information from the server, and the output is the presentation of this information to the user via a visual interface.

[0322] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0323] This invention is a system that recommends appropriate university research labs and seminars based on the user's personal interests and academic ability, and also incorporates an emotion engine to recognize the user's emotional state and optimize the recommendations. This system consists of a server, a terminal, an artificial intelligence engine, and an emotion engine.

[0324] Users access the platform through their devices and create new accounts. During this process, they create a profile by entering information about their areas of interest, desired fields of study, and academic abilities. This information is sent to the server and registered in the database.

[0325] The server collects publicly available information about university research labs and seminars via the internet, and also obtains confidential information from partner educational institutions. This information is registered in and maintained in a database.

[0326] The artificial intelligence engine analyzes the user's profile and information in the database to identify suitable research labs and seminars. This utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities, and select the most optimal candidates.

[0327] Furthermore, this system integrates an emotion engine that recognizes the user's emotional state. It analyzes the user's emotional state based on text, voice, or dialogue history. This emotional information is reflected in the recommendations, providing optimal recommendations tailored to the user's mood and motivation.

[0328] For example, if a user has a profile and emotional information indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will select suitable research labs and seminars. Furthermore, taking into account the emotion of "anxiety" recognized by the emotional engine, it will further emphasize and recommend candidates with supportive environments that can help improve motivation.

[0329] This series of processes will enable users to make more deeply personalized career choices, and allow educational institutions and companies to more efficiently find the right talent.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] Users access the platform through their device and go through the account creation process. They set up their profile by entering their areas of interest, desired career paths, and academic performance data. The device then transmits this information to the server.

[0333] Step 2:

[0334] The server receives user profile information and registers it in the database. This information is fundamental data used in subsequent processing.

[0335] Step 3:

[0336] The server uses web scraping techniques to collect publicly available information about research labs and seminars at educational institutions via the internet. It also obtains confidential information through communication with partner educational institutions, forming and updating a database.

[0337] Step 4:

[0338] The artificial intelligence engine compares the user profile received from the server with the educational institution's information database. Here, it utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities.

[0339] Step 5:

[0340] The emotion engine extracts emotional data from user input and past conversation history. It analyzes text and voice input through the device to identify the user's emotional state.

[0341] Step 6:

[0342] The server comprehensively analyzes the suitability score calculated by the artificial intelligence engine and the emotional data recognized by the emotion engine to create a list of optimal research labs and seminars. In particular, it fine-tunes the recommendations to provide encouragement and reassurance based on the user's emotional state.

[0343] Step 7:

[0344] The terminal displays a list of recommendations sent from the server. The list includes information about each research lab and seminar, along with additional comments and messages tailored to the user's emotional state.

[0345] Step 8:

[0346] Users refer to the above recommendation information and make the selection that best suits their interests and emotional state. Based on their selection, they can then make specific laboratory visits or inquiries through their device.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] In today's educational environment, it is a challenging task for users to quickly and accurately select research facilities and seminars that best suit their interests and learning abilities. Furthermore, providing personalized recommendations while considering the user's emotional state is even more complex. Therefore, there is a need to provide a method for suggesting the most suitable educational options for each user.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for collecting user input information and creating information on learning orientation; means for collecting public and private information on research facilities and seminars of educational organizations and forming an information set; means for using an artificial intelligence model to recommend suitable research facilities and seminars based on the collected information; and means for using an emotion analysis device to analyze the user's emotional state and optimize the recommendation content. As a result, users can obtain the optimal educational options according to their interests and emotional state.

[0352] A "user" refers to an individual who uses the system to receive recommendations based on their own interests and learning abilities.

[0353] "Input information" refers to data related to the user's interests, learning content, and learning ability that they provide to the system.

[0354] "Information regarding learning orientation" refers to information about the academic fields and research themes that users are interested in.

[0355] An "educational organization" refers to any group that conducts academic instruction and research activities, including universities and other educational institutions.

[0356] "Research facilities and seminars" refer to organizational units within an educational institution for conducting specific research or academic pursuits.

[0357] "Public information" refers to information about research facilities and seminars that is publicly available.

[0358] "Confidential information" refers to information that is not publicly available, such as information about research facilities and seminars provided by affiliated educational organizations.

[0359] An "information collection" refers to a dataset used to integrate and systematically manage collected public and private information.

[0360] An "artificial intelligence model" refers to the algorithms and methods used to analyze collected data and provide educational recommendations to users.

[0361] A "emotion analysis device" refers to a technology that analyzes a user's emotional state based on their input and history, and uses this analysis to optimize recommendations.

[0362] This system is built to provide users with the best possible educational options and consists of a server, terminals, an artificial intelligence engine, and an emotion engine.

[0363] The server provides an interface that allows users to access the system through their terminals. Users use their terminals to input information about their areas of interest, preferred learning areas, and learning abilities, creating a profile. This input is sent to the server and registered in the database. The server also collects public and private information about research facilities and seminars within educational organizations from the internet and partner institutions, storing it in the database and maintaining it at all times.

[0364] The artificial intelligence engine uses deep learning algorithms to analyze collected information and user profiles. This analysis calculates a suitability score based on the user's interests and academic abilities to select the most suitable research facilities and seminars. Leveraging natural language processing technology, it analyzes user-generated text and other input information to achieve highly accurate recommendations. The sentiment engine identifies emotions from user input and dialogue history to optimize recommendations for greater personalization.

[0365] For example, if a user has a profile indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will use this information to select research facilities suitable for medical research. Furthermore, the emotion engine will consider the user's "anxiety" and recommend facilities with robust mentoring and support systems to help maintain the user's motivation.

[0366] An example of a prompt to be input into the generating AI model would be, "Please recommend the most suitable research facility based on the user's areas of interest and emotional information." This would make it easier for educational institutions to find suitable personnel and enable individual users to make the best career choices.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users access the platform via their device and enter their areas of interest, desired field of study, and academic information on the new account creation screen. This information includes interests such as "Medical Research" or "Computer Science," as well as academic information such as GPA and test scores. After entering this information, the device sends it to the server. This input is used to create the user's profile. The output is the registration of the user profile information to the server.

[0370] Step 2:

[0371] The server receives profile information sent by the user and stores it in the database. The database stores the information structured using the user ID as the key. At this point, the input is profile data from the terminal, and the server registers this neatly in the database. The output is the registered user profile.

[0372] Step 3:

[0373] The server collects information on research facilities and seminars made public by educational organizations via the internet. Furthermore, it obtains confidential information from partner institutions. This information may be collected using web scraping tools or APIs. The collected information is integrated into a database and stored as an information set. The input is data from the internet, and the output is the storage of information in the database.

[0374] Step 4:

[0375] An artificial intelligence engine analyzes data acquired from the server. Using deep learning algorithms, it calculates suitability scores for research facilities and seminars based on the user's profile and the collected data. Natural language processing techniques are used to analyze the relationships between the profile and the data. The input here is all the data registered on the server, and the output is the suitability score for each candidate.

[0376] Step 5:

[0377] The emotion engine analyzes past conversation history and messages entered by the user to identify their emotional state. Text analysis tools are used for emotion analysis; the input is past text and messages, and the output is identified emotion information. This emotion data is then reflected in the recommendations generated by the artificial intelligence engine.

[0378] Step 6:

[0379] The server integrates information from the artificial intelligence engine and the emotion engine to generate recommendations for the most suitable research facilities and seminars for the user. This information is sent to the terminal, where the user is shown the recommendation level and detailed information. The final input is the analyzed suitability score and emotion information, and the output is the recommendation result provided to the user.

[0380] (Application Example 2)

[0381] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] Traditional recommendation systems for research labs and seminars have a problem in that they cannot make recommendations that take into account the individual feelings of users, and it is difficult to deliver appropriate content that takes into account learning motivation and emotions. In particular, there was a need for a system that takes into account the impact of users' emotional states on learning motivation.

[0383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0384] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations; means for collecting public and private information regarding research laboratories and seminars of educational institutions and forming an information storage device; means for using calculation means to recommend suitable research laboratories and seminars based on the collected information; and means for using a processing structure that includes emotion analysis means for recognizing the user's emotional state and optimizing the recommendation content. This makes it possible to recommend research laboratories and seminars that are tailored to the individual emotional state of each user.

[0385] "Means for collecting user input information and creating a profile regarding learning aspirations" refers to a function that collects information on the user's interests and academic abilities, and generates an individualized profile that summarizes their learning preferences and goals based on that information.

[0386] "Means for collecting public and private information concerning research laboratories and seminars of educational institutions and forming an information storage device" refers to the process of collecting publicly available information and limited access information provided by schools and research facilities, and constructing a database or similar device for organizing and storing this information.

[0387] "Methods for recommending suitable research laboratories and seminars based on collected information" refers to methods for analyzing user profiles and collected research laboratories and seminar information, and performing computational processing to select the most suitable candidates. This may include artificial intelligence algorithms.

[0388] "Means using a processing structure that includes sentiment analysis means for recognizing the user's emotional state and optimizing recommendation content" refers to a processing device or procedure for determining the emotional state from the text or voice expressed by the user and adjusting the recommendation content to provide information in a manner that matches that emotion.

[0389] A description of the embodiment for carrying out the invention will be given.

[0390] In the system that realizes this invention, the terminal first receives input information from the user and builds a profile of the user based on data related to their interests and academic abilities. The server collects this profile information and plays the role of gathering public and private information about research laboratories and seminars at educational institutions via the internet and creating a database. This process utilizes high-performance database technology and network communication technology.

[0391] Based on the collected data, the server uses an artificial intelligence engine built with Python and TensorFlow to select the research lab or seminar that best matches the user profile. Hugging Face Transformers is used for natural language processing, and a deep learning algorithm is employed to calculate the fit score.

[0392] Furthermore, the server analyzes the user's input text or voice data using the Google Cloud Natural Language API to recognize the user's emotional state. The results of the sentiment analysis are used to optimize recommendations, forming a feedback loop to provide information that aligns with the user's emotions.

[0393] For example, if a user has a profile indicating they are "interested in psychology research but feel anxious in new environments," the AI ​​engine will recommend research labs with well-established support systems within the field of psychology. The system's emotion engine takes into account the emotion of "anxiety" and emphasizes and recommends candidates with active communities that can provide a sense of security.

[0394] An example of a prompt sentence to input into the generative AI model is, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me." This allows the user to receive highly personalized information based on their emotions and select the optimal learning environment.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The device receives input information from the user. Specifically, the user inputs data about their interests, academic ability, desired field of study, and emotions into the device. The input data is compiled into a profile and sent to the server.

[0398] Step 2:

[0399] The server receives profile information sent by the user and stores it in a database. The stored profile information is used to measure the user's interests and academic abilities, and serves as the basis for recommendations.

[0400] Step 3:

[0401] The server collects public and private information about research labs and seminars at educational institutions via the internet and stores this information in a new data storage device. Database cleanup is performed periodically to ensure that data is stored reliably and without duplication.

[0402] Step 4:

[0403] The server uses information from the database to launch an artificial intelligence engine built with Python and TensorFlow to analyze potential research labs and seminars that match the user profile. For natural language processing, Hugging Face Transformers are used to calculate the degree of agreement between the input profile information and the database information. Based on these results, a suitability score for research labs and seminars is calculated, and candidates are selected.

[0404] Step 5:

[0405] The server uses the Google Cloud Natural Language API to analyze the emotional state of the user's input text and voice data. The results of the emotional analysis (e.g., anxiety, excitement, relief) are combined with a relevance score, which is the output of the AI ​​engine, to optimize recommendations.

[0406] Step 6:

[0407] The server sends optimized recommendations to the terminal and presents them to the user on the terminal. The user then uses the displayed information to check the details of the recommended research labs and seminars and utilizes it for career choices. An example of a specific prompt message would be a request like, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me."

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0414] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0416] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0418] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0419] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0420] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0423] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0424] This invention is a system that helps high school students and their instructors appropriately select university research labs and seminars based on their interests and learning aspirations. The system consists of a server, terminals, and an artificial intelligence engine. Users access the system using the terminals, input their areas of interest, desired career paths, academic ability, etc., and set up a profile.

[0425] The server aggregates publicly available information about research labs and seminars at various educational institutions, collected from the internet, as well as confidential information provided by partner educational institutions, into a database. This information includes details such as course content, research themes, supervising professors, and past research achievements. The server regularly updates this information to maintain a constantly up-to-date database.

[0426] The artificial intelligence engine uses user profile information and database information on research labs and seminars to calculate a suitability score based on an algorithm. It utilizes natural language processing technology to analyze text data and perform optimal matching to identify candidates that best match the user's interests and academic performance.

[0427] The server then selects a list of recommended research labs and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. Through this information, the user can make the choice that best suits their interests and future goals.

[0428] As a concrete example, consider a user who sets a profile stating, "I am interested in the field of robotics and want to be involved in creative projects." Upon receiving this information, the server retrieves information on relevant educational institutions from its database and analyzes the degree of matching using an artificial intelligence engine. As a result, recommendations such as "Robotics Lab at X University" or "Mechatronics Seminar at Y University" are displayed on the terminal. Based on this information, the user can choose the option that best suits what they want to learn.

[0429] In this way, this system enables users to make more appropriate choices regarding higher education, and also helps companies to efficiently identify promising talent.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] The user accesses the platform through their device and enters information such as a username, email address, and password to create a new account. The device sends the entered information to the server, and the user's account is registered in the database.

[0433] Step 2:

[0434] Users enter their areas of interest, desired occupations, and current academic information (e.g., grades, mock exam scores) into the terminal to set up a profile. The terminal sends this profile information to the server, which stores it in the database as the user's individual profile.

[0435] Step 3:

[0436] The server collects publicly available information about research labs and seminars at various educational institutions from the internet. Furthermore, it also collects confidential information provided by partner educational institutions to keep the database up-to-date.

[0437] Step 4:

[0438] The server organizes and stores the collected information in a database. This database includes detailed information about research labs and seminars, and serves as the basis for user-profile-based analysis.

[0439] Step 5:

[0440] The AI ​​engine activates, and the server analyzes the database of research labs and seminars based on the user's profile. Here, natural language processing techniques are used to calculate a suitability score for each candidate that reflects their academic interests and career aspirations.

[0441] Step 6:

[0442] The server lists the most suitable research labs and seminars based on the calculated fit score. This list is prepared as a recommendation for the user.

[0443] Step 7:

[0444] The terminal displays a list of recommendations sent from the server. The list includes detailed information about each research lab or seminar (e.g., research topic, professor information, research results), which the user can refer to.

[0445] Step 8:

[0446] Based on the information displayed on the device, users can select the research lab or seminar that best suits their interests and goals, and then proceed with inquiries and enrollment procedures based on that selection.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] In modern higher education, a challenge exists in that it is difficult for individuals to choose educational institutions and research facilities that best suit their interests and academic abilities. Traditional methods make it difficult to find the best option from a vast amount of information, and poor choices can have detrimental effects on career paths and future prospects.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information regarding research facilities and seminars of educational institutions and forming a data set, and means for using artificial intelligence components to recommend suitable research facilities and seminars based on the collected information. This makes it possible for users to easily find the best option for themselves from a vast amount of information.

[0452] A "user" is an individual who attempts to obtain information about educational institutions and research facilities by operating the system.

[0453] "Input information" refers to data that users provide to the system regarding their learning aspirations, interests, and academic ability.

[0454] A "learning aspirations profile" is individual profile information compiled by the system based on the user's interests and desired career path.

[0455] An "educational institution" is an organization that provides higher education, including research facilities and seminars.

[0456] "Information regarding research facilities and seminars" refers to a collection of information that includes detailed data on course content, research themes, supervising professors, and past research results.

[0457] "Publicly available information" refers to information about educational institutions that is widely provided to the public on the internet.

[0458] "Confidential information" refers to data that is provided to a limited extent by educational institutions and is not publicly available.

[0459] A "data set" is a database that compiles publicly available and confidential information about educational institutions and research facilities collected by the server.

[0460] "Artificial intelligence components" refer to the technical engines used to analyze data and identify research facilities and seminars suitable for the user.

[0461] "Suitable research facilities and seminars" refer to research facilities and seminars at educational institutions that have characteristics that best match the user's input information.

[0462] "Comparison and favorites features" are support tools that allow users to evaluate and save recommended options, helping them make the best decision.

[0463] This invention is a system that assists users in selecting the most suitable educational institution according to their individual learning aspirations and interests. Its basic components consist of a server, a terminal, and an artificial intelligence engine.

[0464] First, users access the system using their device and input information such as their areas of interest, career path, and academic ability. Based on this information, the device sets up the user's profile and sends it to the server. The device can be a regular computer or smartphone, and it connects to the system via an internet browser or a dedicated application.

[0465] Next, the server uses publicly available information about research facilities and seminars at each educational institution, collected from the internet, along with confidential information provided by partner educational institutions, to form a comprehensive data set. This data includes information such as course content, research themes, supervisors, and past research achievements. The server regularly updates this data to ensure the reliability of the information.

[0466] The artificial intelligence engine is used to analyze the degree of fit based on the user's profile information and research information stored on the server. This engine utilizes generative AI models and applies natural language processing technology to analyze the data and identify the educational institution that best matches the user's interests and aspirations. For example, by inputting a prompt sentence based on the user's wishes, such as "I am interested in the field of robotics and would like to be involved in creative projects," the engine will perform the optimal matching.

[0467] Finally, the server creates a list of recommended research facilities and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. The user can compare each option, register them as favorites, and make the choice that best suits their preferences. This system enables users to make well-informed decisions when choosing their career path.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] Users log in to the system using a terminal and enter information about their areas of interest, desired career path, and academic ability. This information is used to create a personalized user profile. The terminal formats the entered information and sends it to the server as profile data.

[0471] Step 2:

[0472] The server aggregates optimized information from the database based on the received user profile. The server references existing data sets to obtain basic information about research facilities and seminars at relevant educational institutions. During this process, the server checks information from the internet and partner institutions, updating the database accordingly.

[0473] Step 3:

[0474] The server passes the user's profile information and acquired educational institution information to an artificial intelligence engine. This engine uses a generative AI model and natural language processing techniques to analyze the data. To match the profile information and research information as input, the engine performs advanced data analysis to identify the most relevant options.

[0475] Step 4:

[0476] Based on the suitability score obtained from the artificial intelligence engine, the server generates a list of recommended research facilities and seminars. Here, the list is sorted in order of priority according to the suitability score. The result is a refined recommendation list.

[0477] Step 5:

[0478] The server sends the created recommendation list to the terminal. The terminal provides an interface that allows the user to freely browse the list. The terminal utilizes detailed information display, favorites registration, and comparison functions to create an environment where the user can make the best choice.

[0479] Step 6:

[0480] Users refer to information provided via their device to learn more about research facilities and seminars that interest them. They make selections based on a list of recommendations and determine their next actions. They can save their selected information and conduct further in-depth inquiries.

[0481] (Application Example 1)

[0482] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0483] It is difficult for motivated learners to efficiently find research labs or seminars that match their interests within a specific educational institution. Furthermore, the inability to obtain relevant information in real time on campus hinders the selection of the optimal learning environment.

[0484] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0485] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information about research laboratories and seminars at educational institutions and forming a database, and means for acquiring location information and performing image recognition of surrounding objects. This makes it possible for users to visually obtain information about research laboratories and seminars that match their interests in real time on a specific campus.

[0486] "Users" refers to individuals who use this system to find research labs or seminars that match their learning aspirations.

[0487] "Input information" refers to information about the user's interests and learning goals that they provide to the system.

[0488] A "profile" refers to a collection of information about an individual's learning aspirations, created based on their interests and academic abilities.

[0489] "Educational institutions" refer to organizations that provide higher education, such as universities.

[0490] "Public and confidential information" refers to information made publicly available by educational institutions and information that is only available within specific organizations.

[0491] A "database" refers to a system for systematically organizing and recording collected information.

[0492] An "artificial intelligence engine" refers to an automated algorithm that recommends the most suitable research lab or seminar based on the user's profile and information about their educational institution.

[0493] "Location information" refers to data about the current location of the user or device.

[0494] "Methods for image recognition of objects" refers to technologies that use a device's camera or similar equipment to identify surrounding objects and analyze that information.

[0495] "Presenting visually" refers to providing information in a way that users can understand visually.

[0496] This invention provides a system that utilizes information processing terminals such as smart glasses and smartphones to enable users with a desire to learn to obtain information about research labs and seminars at educational institutions in real time.

[0497] The server collects information about the user's interests and learning goals and generates a learning aspiration profile based on this information. This identifies potential research labs and seminars at educational institutions that match the user's interests.

[0498] In this system, the server processes the collected data using an artificial intelligence engine and performs analysis using natural language processing technology. Using deep learning algorithms, it calculates a compatibility score between the user's profile and the educational institution information stored in the database. Based on this analysis, it identifies the most suitable research labs and seminars and generates a recommendation list.

[0499] When a user visits the campus, the device uses GPS and image recognition technologies such as OpenCV to recognize their current location and surrounding objects. Based on the recognized information, it visually displays information about relevant educational institutions in real time.

[0500] For example, if user A sets a profile stating "I am interested in robotics and would like to participate in creative projects" and enters the engineering campus, the smart glasses will recognize the engineering building and display information to user A stating that "the Robotics Laboratory is currently working on a cutting-edge project and is highly suitable."

[0501] An example of a prompt might be: "Create an application that uses AI to recognize relevant buildings on a university campus and provide real-time information to help the user find a research lab of interest. The inputs are the user's areas of interest and past learning history."

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] The server receives input from the user regarding their interests, academic ability, and learning aspirations. Based on this, it generates a profile. The input consists of the user's areas of interest and learning history, and by converting this into structured data, the server obtains the profile as output.

[0505] Step 2:

[0506] The server collects public and private information about educational institutions from various sources and updates the database. New data is entered and compared with existing data to produce the most up-to-date information on educational institutions.

[0507] Step 3:

[0508] The device acquires its current location information, uses its camera to perform image recognition on surrounding objects, and identifies them. The input consists of location data and image data, and based on this, it generates output in the form of location and object identification.

[0509] Step 4:

[0510] The server uses an artificial intelligence engine to calculate a compatibility score with research labs and seminars at educational institutions, based on profile information and a database. The input is profile and database information, and the server generates a compatibility score as output through text analysis using natural language processing techniques and calculations using deep learning algorithms.

[0511] Step 5:

[0512] The server generates a list of recommended research labs and seminars based on the calculated fit score. This list takes the profile and fit score as input and outputs the names of the most suitable research labs and seminars.

[0513] Step 6:

[0514] The terminal visually displays relevant recommended research lab information to the user in real time. The input is recommended information from the server, and the output is the presentation of this information to the user via a visual interface.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] This invention is a system that recommends appropriate university research labs and seminars based on the user's personal interests and academic ability, and also incorporates an emotion engine to recognize the user's emotional state and optimize the recommendations. This system consists of a server, a terminal, an artificial intelligence engine, and an emotion engine.

[0517] Users access the platform through their devices and create new accounts. During this process, they create a profile by entering information about their areas of interest, desired fields of study, and academic abilities. This information is sent to the server and registered in the database.

[0518] The server collects publicly available information about university research labs and seminars via the internet, and also obtains confidential information from partner educational institutions. This information is registered in and maintained in a database.

[0519] The artificial intelligence engine analyzes the user's profile and information in the database to identify suitable research labs and seminars. This utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities, and select the most optimal candidates.

[0520] Furthermore, this system integrates an emotion engine that recognizes the user's emotional state. It analyzes the user's emotional state based on text, voice, or dialogue history. This emotional information is reflected in the recommendations, providing optimal recommendations tailored to the user's mood and motivation.

[0521] For example, if a user has a profile and emotional information indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will select suitable research labs and seminars. Furthermore, taking into account the emotion of "anxiety" recognized by the emotional engine, it will further emphasize and recommend candidates with supportive environments that can help improve motivation.

[0522] This series of processes will enable users to make more deeply personalized career choices, and allow educational institutions and companies to more efficiently find the right talent.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] Users access the platform through their device and go through the account creation process. They set up their profile by entering their areas of interest, desired career paths, and academic performance data. The device then transmits this information to the server.

[0526] Step 2:

[0527] The server receives user profile information and registers it in the database. This information is fundamental data used in subsequent processing.

[0528] Step 3:

[0529] The server uses web scraping techniques to collect publicly available information about research labs and seminars at educational institutions via the internet. It also obtains confidential information through communication with partner educational institutions, forming and updating a database.

[0530] Step 4:

[0531] The artificial intelligence engine compares the user profile received from the server with the educational institution's information database. Here, it utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities.

[0532] Step 5:

[0533] The emotion engine extracts emotional data from user input and past conversation history. It analyzes text and voice input through the device to identify the user's emotional state.

[0534] Step 6:

[0535] The server comprehensively analyzes the suitability score calculated by the artificial intelligence engine and the emotional data recognized by the emotion engine to create a list of optimal research labs and seminars. In particular, it fine-tunes the recommendations to provide encouragement and reassurance based on the user's emotional state.

[0536] Step 7:

[0537] The terminal displays a list of recommendations sent from the server. The list includes information about each research lab and seminar, along with additional comments and messages tailored to the user's emotional state.

[0538] Step 8:

[0539] Users refer to the above recommendation information and make the selection that best suits their interests and emotional state. Based on their selection, they can then make specific laboratory visits or inquiries through their device.

[0540] (Example 2)

[0541] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In today's educational environment, it is a challenging task for users to quickly and accurately select research facilities and seminars that best suit their interests and learning abilities. Furthermore, providing personalized recommendations while considering the user's emotional state is even more complex. Therefore, there is a need to provide a method for suggesting the most suitable educational options for each user.

[0543] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0544] In this invention, the server includes means for collecting user input information and creating information on learning orientation; means for collecting public and private information on research facilities and seminars of educational organizations and forming an information set; means for using an artificial intelligence model to recommend suitable research facilities and seminars based on the collected information; and means for using an emotion analysis device to analyze the user's emotional state and optimize the recommendation content. As a result, users can obtain the optimal educational options according to their interests and emotional state.

[0545] A "user" refers to an individual who uses the system to receive recommendations based on their own interests and learning abilities.

[0546] "Input information" refers to data related to the user's interests, learning content, and learning ability that they provide to the system.

[0547] "Information regarding learning orientation" refers to information about the academic fields and research themes that users are interested in.

[0548] An "educational organization" refers to any group that conducts academic instruction and research activities, including universities and other educational institutions.

[0549] "Research facilities and seminars" refer to organizational units within an educational institution for conducting specific research or academic pursuits.

[0550] "Public information" refers to information about research facilities and seminars that is publicly available.

[0551] "Confidential information" refers to information that is not publicly available, such as information about research facilities and seminars provided by affiliated educational organizations.

[0552] An "information collection" refers to a dataset used to integrate and systematically manage collected public and private information.

[0553] An "artificial intelligence model" refers to the algorithms and methods used to analyze collected data and provide educational recommendations to users.

[0554] A "emotion analysis device" refers to a technology that analyzes a user's emotional state based on their input and history, and uses this analysis to optimize recommendations.

[0555] This system is built to provide users with the best possible educational options and consists of a server, terminals, an artificial intelligence engine, and an emotion engine.

[0556] The server provides an interface that allows users to access the system through their terminals. Users use their terminals to input information about their areas of interest, preferred learning areas, and learning abilities, creating a profile. This input is sent to the server and registered in the database. The server also collects public and private information about research facilities and seminars within educational organizations from the internet and partner institutions, storing it in the database and maintaining it at all times.

[0557] The artificial intelligence engine uses deep learning algorithms to analyze collected information and user profiles. This analysis calculates a suitability score based on the user's interests and academic abilities to select the most suitable research facilities and seminars. Leveraging natural language processing technology, it analyzes user-generated text and other input information to achieve highly accurate recommendations. The sentiment engine identifies emotions from user input and dialogue history to optimize recommendations for greater personalization.

[0558] For example, if a user has a profile indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will use this information to select research facilities suitable for medical research. Furthermore, the emotion engine will consider the user's "anxiety" and recommend facilities with robust mentoring and support systems to help maintain the user's motivation.

[0559] An example of a prompt to be input into the generating AI model would be, "Please recommend the most suitable research facility based on the user's areas of interest and emotional information." This would make it easier for educational institutions to find suitable personnel and enable individual users to make the best career choices.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] Users access the platform via their device and enter their areas of interest, desired field of study, and academic information on the new account creation screen. This information includes interests such as "Medical Research" or "Computer Science," as well as academic information such as GPA and test scores. After entering this information, the device sends it to the server. This input is used to create the user's profile. The output is the registration of the user profile information to the server.

[0563] Step 2:

[0564] The server receives profile information sent by the user and stores it in the database. The database stores the information structured using the user ID as the key. At this point, the input is profile data from the terminal, and the server registers this neatly in the database. The output is the registered user profile.

[0565] Step 3:

[0566] The server collects information on research facilities and seminars made public by educational organizations via the internet. Furthermore, it obtains confidential information from partner institutions. This information may be collected using web scraping tools or APIs. The collected information is integrated into a database and stored as an information set. The input is data from the internet, and the output is the storage of information in the database.

[0567] Step 4:

[0568] An artificial intelligence engine analyzes data acquired from the server. Using deep learning algorithms, it calculates suitability scores for research facilities and seminars based on the user's profile and the collected data. Natural language processing techniques are used to analyze the relationships between the profile and the data. The input here is all the data registered on the server, and the output is the suitability score for each candidate.

[0569] Step 5:

[0570] The emotion engine analyzes past conversation history and messages entered by the user to identify their emotional state. Text analysis tools are used for emotion analysis; the input is past text and messages, and the output is identified emotion information. This emotion data is then reflected in the recommendations generated by the artificial intelligence engine.

[0571] Step 6:

[0572] The server integrates information from the artificial intelligence engine and the emotion engine to generate recommendations for the most suitable research facilities and seminars for the user. This information is sent to the terminal, where the user is shown the recommendation level and detailed information. The final input is the analyzed suitability score and emotion information, and the output is the recommendation result provided to the user.

[0573] (Application Example 2)

[0574] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] Traditional recommendation systems for research labs and seminars have a problem in that they cannot make recommendations that take into account the individual feelings of users, and it is difficult to deliver appropriate content that takes into account learning motivation and emotions. In particular, there was a need for a system that takes into account the impact of users' emotional states on learning motivation.

[0576] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0577] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations; means for collecting public and private information regarding research laboratories and seminars of educational institutions and forming an information storage device; means for using calculation means to recommend suitable research laboratories and seminars based on the collected information; and means for using a processing structure that includes emotion analysis means for recognizing the user's emotional state and optimizing the recommendation content. This makes it possible to recommend research laboratories and seminars that are tailored to the individual emotional state of each user.

[0578] "Means for collecting user input information and creating a profile regarding learning aspirations" refers to a function that collects information on the user's interests and academic abilities, and generates an individualized profile that summarizes their learning preferences and goals based on that information.

[0579] "Means for collecting public and private information concerning research laboratories and seminars of educational institutions and forming an information storage device" refers to the process of collecting publicly available information and limited access information provided by schools and research facilities, and constructing a database or similar device for organizing and storing this information.

[0580] "Methods for recommending suitable research laboratories and seminars based on collected information" refers to methods for analyzing user profiles and collected research laboratories and seminar information, and performing computational processing to select the most suitable candidates. This may include artificial intelligence algorithms.

[0581] "Means using a processing structure that includes sentiment analysis means for recognizing the user's emotional state and optimizing recommendation content" refers to a processing device or procedure for determining the emotional state from the text or voice expressed by the user and adjusting the recommendation content to provide information in a manner that matches that emotion.

[0582] A description of the embodiment for carrying out the invention will be given.

[0583] In the system that realizes this invention, the terminal first receives input information from the user and builds a profile of the user based on data related to their interests and academic abilities. The server collects this profile information and plays the role of gathering public and private information about research laboratories and seminars at educational institutions via the internet and creating a database. This process utilizes high-performance database technology and network communication technology.

[0584] Based on the collected data, the server uses an artificial intelligence engine built with Python and TensorFlow to select the research lab or seminar that best matches the user profile. Hugging Face Transformers is used for natural language processing, and a deep learning algorithm is employed to calculate the fit score.

[0585] Furthermore, the server analyzes the user's input text or voice data using the Google Cloud Natural Language API to recognize the user's emotional state. The results of the sentiment analysis are used to optimize recommendations, forming a feedback loop to provide information that aligns with the user's emotions.

[0586] For example, if a user has a profile indicating they are "interested in psychology research but feel anxious in new environments," the AI ​​engine will recommend research labs with well-established support systems within the field of psychology. The system's emotion engine takes into account the emotion of "anxiety" and emphasizes and recommends candidates with active communities that can provide a sense of security.

[0587] An example of a prompt sentence to input into the generative AI model is, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me." This allows the user to receive highly personalized information based on their emotions and select the optimal learning environment.

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The device receives input information from the user. Specifically, the user inputs data about their interests, academic ability, desired field of study, and emotions into the device. The input data is compiled into a profile and sent to the server.

[0591] Step 2:

[0592] The server receives profile information sent by the user and stores it in a database. The stored profile information is used to measure the user's interests and academic abilities, and serves as the basis for recommendations.

[0593] Step 3:

[0594] The server collects public and private information about research labs and seminars at educational institutions via the internet and stores this information in a new data storage device. Database cleanup is performed periodically to ensure that data is stored reliably and without duplication.

[0595] Step 4:

[0596] The server uses information from the database to launch an artificial intelligence engine built with Python and TensorFlow to analyze potential research labs and seminars that match the user profile. For natural language processing, Hugging Face Transformers are used to calculate the degree of agreement between the input profile information and the database information. Based on these results, a suitability score for research labs and seminars is calculated, and candidates are selected.

[0597] Step 5:

[0598] The server uses the Google Cloud Natural Language API to analyze the emotional state of the user's input text and voice data. The results of the emotional analysis (e.g., anxiety, excitement, relief) are combined with a relevance score, which is the output of the AI ​​engine, to optimize recommendations.

[0599] Step 6:

[0600] The server sends optimized recommendations to the terminal and presents them to the user on the terminal. The user then uses the displayed information to check the details of the recommended research labs and seminars and utilizes it for career choices. An example of a specific prompt message would be a request like, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me."

[0601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0614] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention is a system that helps high school students and their instructors appropriately select university research labs and seminars based on their interests and learning aspirations. The system consists of a server, terminals, and an artificial intelligence engine. Users access the system using the terminals, input their areas of interest, desired career paths, academic ability, etc., and set up a profile.

[0619] The server aggregates publicly available information about research labs and seminars at various educational institutions, collected from the internet, as well as confidential information provided by partner educational institutions, into a database. This information includes details such as course content, research themes, supervising professors, and past research achievements. The server regularly updates this information to maintain a constantly up-to-date database.

[0620] The artificial intelligence engine uses user profile information and database information on research labs and seminars to calculate a suitability score based on an algorithm. It utilizes natural language processing technology to analyze text data and perform optimal matching to identify candidates that best match the user's interests and academic performance.

[0621] The server then selects a list of recommended research labs and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. Through this information, the user can make the choice that best suits their interests and future goals.

[0622] As a concrete example, consider a user who sets a profile stating, "I am interested in the field of robotics and want to be involved in creative projects." Upon receiving this information, the server retrieves information on relevant educational institutions from its database and analyzes the degree of matching using an artificial intelligence engine. As a result, recommendations such as "Robotics Lab at X University" or "Mechatronics Seminar at Y University" are displayed on the terminal. Based on this information, the user can choose the option that best suits what they want to learn.

[0623] In this way, this system enables users to make more appropriate choices regarding higher education, and also helps companies to efficiently identify promising talent.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The user accesses the platform through their device and enters information such as a username, email address, and password to create a new account. The device sends the entered information to the server, and the user's account is registered in the database.

[0627] Step 2:

[0628] Users enter their areas of interest, desired occupations, and current academic information (e.g., grades, mock exam scores) into the terminal to set up a profile. The terminal sends this profile information to the server, which stores it in the database as the user's individual profile.

[0629] Step 3:

[0630] The server collects publicly available information about research labs and seminars at various educational institutions from the internet. Furthermore, it also collects confidential information provided by partner educational institutions to keep the database up-to-date.

[0631] Step 4:

[0632] The server organizes and stores the collected information in a database. This database includes detailed information about research labs and seminars, and serves as the basis for user-profile-based analysis.

[0633] Step 5:

[0634] The AI ​​engine activates, and the server analyzes the database of research labs and seminars based on the user's profile. Here, natural language processing techniques are used to calculate a suitability score for each candidate that reflects their academic interests and career aspirations.

[0635] Step 6:

[0636] The server lists the most suitable research labs and seminars based on the calculated fit score. This list is prepared as a recommendation for the user.

[0637] Step 7:

[0638] The terminal displays a list of recommendations sent from the server. The list includes detailed information about each research lab or seminar (e.g., research topic, professor information, research results), which the user can refer to.

[0639] Step 8:

[0640] Based on the information displayed on the device, users can select the research lab or seminar that best suits their interests and goals, and then proceed with inquiries and enrollment procedures based on that selection.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] In modern higher education, a challenge exists in that it is difficult for individuals to choose educational institutions and research facilities that best suit their interests and academic abilities. Traditional methods make it difficult to find the best option from a vast amount of information, and poor choices can have detrimental effects on career paths and future prospects.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information regarding research facilities and seminars of educational institutions and forming a data set, and means for using artificial intelligence components to recommend suitable research facilities and seminars based on the collected information. This makes it possible for users to easily find the best option for themselves from a vast amount of information.

[0646] A "user" is an individual who attempts to obtain information about educational institutions and research facilities by operating the system.

[0647] "Input information" refers to data that users provide to the system regarding their learning aspirations, interests, and academic ability.

[0648] A "learning aspirations profile" is individual profile information compiled by the system based on the user's interests and desired career path.

[0649] An "educational institution" is an organization that provides higher education, including research facilities and seminars.

[0650] "Information regarding research facilities and seminars" refers to a collection of information that includes detailed data on course content, research themes, supervising professors, and past research results.

[0651] "Publicly available information" refers to information about educational institutions that is widely provided to the public on the internet.

[0652] "Confidential information" refers to data that is provided to a limited extent by educational institutions and is not publicly available.

[0653] A "data set" is a database that compiles publicly available and confidential information about educational institutions and research facilities collected by the server.

[0654] "Artificial intelligence components" refer to the technical engines used to analyze data and identify research facilities and seminars suitable for the user.

[0655] "Suitable research facilities and seminars" refer to research facilities and seminars at educational institutions that have characteristics that best match the user's input information.

[0656] "Comparison and favorites features" are support tools that allow users to evaluate and save recommended options, helping them make the best decision.

[0657] This invention is a system that assists users in selecting the most suitable educational institution according to their individual learning aspirations and interests. Its basic components consist of a server, a terminal, and an artificial intelligence engine.

[0658] First, users access the system using their device and input information such as their areas of interest, career path, and academic ability. Based on this information, the device sets up the user's profile and sends it to the server. The device can be a regular computer or smartphone, and it connects to the system via an internet browser or a dedicated application.

[0659] Next, the server uses publicly available information about research facilities and seminars at each educational institution, collected from the internet, along with confidential information provided by partner educational institutions, to form a comprehensive data set. This data includes information such as course content, research themes, supervisors, and past research achievements. The server regularly updates this data to ensure the reliability of the information.

[0660] The artificial intelligence engine is used to analyze the degree of fit based on the user's profile information and research information stored on the server. This engine utilizes generative AI models and applies natural language processing technology to analyze the data and identify the educational institution that best matches the user's interests and aspirations. For example, by inputting a prompt sentence based on the user's wishes, such as "I am interested in the field of robotics and would like to be involved in creative projects," the engine will perform the optimal matching.

[0661] Finally, the server creates a list of recommended research facilities and seminars based on the calculated suitability score and sends it to the terminal. The terminal provides this information to the user, allowing them to view detailed information. The user can compare each option, register them as favorites, and make the choice that best suits their preferences. This system enables users to make well-informed decisions when choosing their career path.

[0662] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0663] Step 1:

[0664] Users log in to the system using a terminal and enter information about their areas of interest, desired career path, and academic ability. This information is used to create a personalized user profile. The terminal formats the entered information and sends it to the server as profile data.

[0665] Step 2:

[0666] The server aggregates optimized information from the database based on the received user profile. The server references existing data sets to obtain basic information about research facilities and seminars at relevant educational institutions. During this process, the server checks information from the internet and partner institutions, updating the database accordingly.

[0667] Step 3:

[0668] The server passes the user's profile information and acquired educational institution information to an artificial intelligence engine. This engine uses a generative AI model and natural language processing techniques to analyze the data. To match the profile information and research information as input, the engine performs advanced data analysis to identify the most relevant options.

[0669] Step 4:

[0670] Based on the suitability score obtained from the artificial intelligence engine, the server generates a list of recommended research facilities and seminars. Here, the list is sorted in order of priority according to the suitability score. The result is a refined recommendation list.

[0671] Step 5:

[0672] The server sends the created recommendation list to the terminal. The terminal provides an interface that allows the user to freely browse the list. The terminal utilizes detailed information display, favorites registration, and comparison functions to create an environment where the user can make the best choice.

[0673] Step 6:

[0674] Users refer to information provided via their device to learn more about research facilities and seminars that interest them. They make selections based on a list of recommendations and determine their next actions. They can save their selected information and conduct further in-depth inquiries.

[0675] (Application Example 1)

[0676] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] It is difficult for motivated learners to efficiently find research labs or seminars that match their interests within a specific educational institution. Furthermore, the inability to obtain relevant information in real time on campus hinders the selection of the optimal learning environment.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0679] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations, means for collecting public and private information about research laboratories and seminars at educational institutions and forming a database, and means for acquiring location information and performing image recognition of surrounding objects. This makes it possible for users to visually obtain information about research laboratories and seminars that match their interests in real time on a specific campus.

[0680] "Users" refers to individuals who use this system to find research labs or seminars that match their learning aspirations.

[0681] "Input information" refers to information about the user's interests and learning goals that they provide to the system.

[0682] A "profile" refers to a collection of information about an individual's learning aspirations, created based on their interests and academic abilities.

[0683] "Educational institutions" refer to organizations that provide higher education, such as universities.

[0684] "Public and confidential information" refers to information made publicly available by educational institutions and information that is only available within specific organizations.

[0685] A "database" refers to a system for systematically organizing and recording collected information.

[0686] An "artificial intelligence engine" refers to an automated algorithm that recommends the most suitable research lab or seminar based on the user's profile and information about their educational institution.

[0687] "Location information" refers to data about the current location of the user or device.

[0688] "Methods for image recognition of objects" refers to technologies that use a device's camera or similar equipment to identify surrounding objects and analyze that information.

[0689] "Presenting visually" refers to providing information in a way that users can understand visually.

[0690] This invention provides a system that utilizes information processing terminals such as smart glasses and smartphones to enable users with a desire to learn to obtain information about research labs and seminars at educational institutions in real time.

[0691] The server collects information about the user's interests and learning goals and generates a learning aspiration profile based on this information. This identifies potential research labs and seminars at educational institutions that match the user's interests.

[0692] In this system, the server processes the collected data using an artificial intelligence engine and performs analysis using natural language processing technology. Using deep learning algorithms, it calculates a compatibility score between the user's profile and the educational institution information stored in the database. Based on this analysis, it identifies the most suitable research labs and seminars and generates a recommendation list.

[0693] When a user visits the campus, the device uses GPS and image recognition technologies such as OpenCV to recognize their current location and surrounding objects. Based on the recognized information, it visually displays information about relevant educational institutions in real time.

[0694] For example, if user A sets a profile stating "I am interested in robotics and would like to participate in creative projects" and enters the engineering campus, the smart glasses will recognize the engineering building and display information to user A stating that "the Robotics Laboratory is currently working on a cutting-edge project and is highly suitable."

[0695] An example of a prompt might be: "Create an application that uses AI to recognize relevant buildings on a university campus and provide real-time information to help the user find a research lab of interest. The inputs are the user's areas of interest and past learning history."

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The server receives input from the user regarding their interests, academic ability, and learning aspirations. Based on this, it generates a profile. The input consists of the user's areas of interest and learning history, and by converting this into structured data, the server obtains the profile as output.

[0699] Step 2:

[0700] The server collects public and private information about educational institutions from various sources and updates the database. New data is entered and compared with existing data to produce the most up-to-date information on educational institutions.

[0701] Step 3:

[0702] The device acquires its current location information, uses its camera to perform image recognition on surrounding objects, and identifies them. The input consists of location data and image data, and based on this, it generates output in the form of location and object identification.

[0703] Step 4:

[0704] The server uses an artificial intelligence engine to calculate a compatibility score with research labs and seminars at educational institutions, based on profile information and a database. The input is profile and database information, and the server generates a compatibility score as output through text analysis using natural language processing techniques and calculations using deep learning algorithms.

[0705] Step 5:

[0706] The server generates a list of recommended research labs and seminars based on the calculated fit score. This list takes the profile and fit score as input and outputs the names of the most suitable research labs and seminars.

[0707] Step 6:

[0708] The terminal visually displays relevant recommended research lab information to the user in real time. The input is recommended information from the server, and the output is the presentation of this information to the user via a visual interface.

[0709] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0710] This invention is a system that recommends appropriate university research labs and seminars based on the user's personal interests and academic ability, and also incorporates an emotion engine to recognize the user's emotional state and optimize the recommendations. This system consists of a server, a terminal, an artificial intelligence engine, and an emotion engine.

[0711] Users access the platform through their devices and create new accounts. During this process, they create a profile by entering information about their areas of interest, desired fields of study, and academic abilities. This information is sent to the server and registered in the database.

[0712] The server collects publicly available information about university research labs and seminars via the internet, and also obtains confidential information from partner educational institutions. This information is registered in and maintained in a database.

[0713] The artificial intelligence engine analyzes the user's profile and information in the database to identify suitable research labs and seminars. This utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities, and select the most optimal candidates.

[0714] Furthermore, this system integrates an emotion engine that recognizes the user's emotional state. It analyzes the user's emotional state based on text, voice, or dialogue history. This emotional information is reflected in the recommendations, providing optimal recommendations tailored to the user's mood and motivation.

[0715] For example, if a user has a profile and emotional information indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will select suitable research labs and seminars. Furthermore, taking into account the emotion of "anxiety" recognized by the emotional engine, it will further emphasize and recommend candidates with supportive environments that can help improve motivation.

[0716] This series of processes will enable users to make more deeply personalized career choices, and allow educational institutions and companies to more efficiently find the right talent.

[0717] The following describes the processing flow.

[0718] Step 1:

[0719] Users access the platform through their device and go through the account creation process. They set up their profile by entering their areas of interest, desired career paths, and academic performance data. The device then transmits this information to the server.

[0720] Step 2:

[0721] The server receives user profile information and registers it in the database. This information is fundamental data used in subsequent processing.

[0722] Step 3:

[0723] The server uses web scraping techniques to collect publicly available information about research labs and seminars at educational institutions via the internet. It also obtains confidential information through communication with partner educational institutions, forming and updating a database.

[0724] Step 4:

[0725] The artificial intelligence engine compares the user profile received from the server with the educational institution's information database. Here, it utilizes deep learning algorithms with natural language processing to calculate a suitability score based on the user's interests and academic abilities.

[0726] Step 5:

[0727] The emotion engine extracts emotional data from user input and past conversation history. It analyzes text and voice input through the device to identify the user's emotional state.

[0728] Step 6:

[0729] The server comprehensively analyzes the suitability score calculated by the artificial intelligence engine and the emotional data recognized by the emotion engine to create a list of optimal research labs and seminars. In particular, it fine-tunes the recommendations to provide encouragement and reassurance based on the user's emotional state.

[0730] Step 7:

[0731] The terminal displays a list of recommendations sent from the server. The list includes information about each research lab and seminar, along with additional comments and messages tailored to the user's emotional state.

[0732] Step 8:

[0733] Users refer to the above recommendation information and make the selection that best suits their interests and emotional state. Based on their selection, they can then make specific laboratory visits or inquiries through their device.

[0734] (Example 2)

[0735] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In today's educational environment, it is a challenging task for users to quickly and accurately select research facilities and seminars that best suit their interests and learning abilities. Furthermore, providing personalized recommendations while considering the user's emotional state is even more complex. Therefore, there is a need to provide a method for suggesting the most suitable educational options for each user.

[0737] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0738] In this invention, the server includes means for collecting user input information and creating information on learning orientation; means for collecting public and private information on research facilities and seminars of educational organizations and forming an information set; means for using an artificial intelligence model to recommend suitable research facilities and seminars based on the collected information; and means for using an emotion analysis device to analyze the user's emotional state and optimize the recommendation content. As a result, users can obtain the optimal educational options according to their interests and emotional state.

[0739] A "user" refers to an individual who uses the system to receive recommendations based on their own interests and learning abilities.

[0740] "Input information" refers to data related to the user's interests, learning content, and learning ability that they provide to the system.

[0741] "Information regarding learning orientation" refers to information about the academic fields and research themes that users are interested in.

[0742] An "educational organization" refers to any group that conducts academic instruction and research activities, including universities and other educational institutions.

[0743] "Research facilities and seminars" refer to organizational units within an educational institution for conducting specific research or academic pursuits.

[0744] "Public information" refers to information about research facilities and seminars that is publicly available.

[0745] "Confidential information" refers to information that is not publicly available, such as information about research facilities and seminars provided by affiliated educational organizations.

[0746] An "information collection" refers to a dataset used to integrate and systematically manage collected public and private information.

[0747] An "artificial intelligence model" refers to the algorithms and methods used to analyze collected data and provide educational recommendations to users.

[0748] A "emotion analysis device" refers to a technology that analyzes a user's emotional state based on their input and history, and uses this analysis to optimize recommendations.

[0749] This system is built to provide users with the best possible educational options and consists of a server, terminals, an artificial intelligence engine, and an emotion engine.

[0750] The server provides an interface that allows users to access the system through their terminals. Users use their terminals to input information about their areas of interest, preferred learning areas, and learning abilities, creating a profile. This input is sent to the server and registered in the database. The server also collects public and private information about research facilities and seminars within educational organizations from the internet and partner institutions, storing it in the database and maintaining it at all times.

[0751] The artificial intelligence engine uses deep learning algorithms to analyze collected information and user profiles. This analysis calculates a suitability score based on the user's interests and academic abilities to select the most suitable research facilities and seminars. Leveraging natural language processing technology, it analyzes user-generated text and other input information to achieve highly accurate recommendations. The sentiment engine identifies emotions from user input and dialogue history to optimize recommendations for greater personalization.

[0752] For example, if a user has a profile indicating they are "interested in medical research but feel anxious about their career path," the AI ​​engine will use this information to select research facilities suitable for medical research. Furthermore, the emotion engine will consider the user's "anxiety" and recommend facilities with robust mentoring and support systems to help maintain the user's motivation.

[0753] An example of a prompt to be input into the generating AI model would be, "Please recommend the most suitable research facility based on the user's areas of interest and emotional information." This would make it easier for educational institutions to find suitable personnel and enable individual users to make the best career choices.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] Users access the platform via their device and enter their areas of interest, desired field of study, and academic information on the new account creation screen. This information includes interests such as "Medical Research" or "Computer Science," as well as academic information such as GPA and test scores. After entering this information, the device sends it to the server. This input is used to create the user's profile. The output is the registration of the user profile information to the server.

[0757] Step 2:

[0758] The server receives profile information sent by the user and stores it in the database. The database stores the information structured using the user ID as the key. At this point, the input is profile data from the terminal, and the server registers this neatly in the database. The output is the registered user profile.

[0759] Step 3:

[0760] The server collects information on research facilities and seminars made public by educational organizations via the internet. Furthermore, it obtains confidential information from partner institutions. This information may be collected using web scraping tools or APIs. The collected information is integrated into a database and stored as an information set. The input is data from the internet, and the output is the storage of information in the database.

[0761] Step 4:

[0762] An artificial intelligence engine analyzes data acquired from the server. Using deep learning algorithms, it calculates suitability scores for research facilities and seminars based on the user's profile and the collected data. Natural language processing techniques are used to analyze the relationships between the profile and the data. The input here is all the data registered on the server, and the output is the suitability score for each candidate.

[0763] Step 5:

[0764] The emotion engine analyzes past conversation history and messages entered by the user to identify their emotional state. Text analysis tools are used for emotion analysis; the input is past text and messages, and the output is identified emotion information. This emotion data is then reflected in the recommendations generated by the artificial intelligence engine.

[0765] Step 6:

[0766] The server integrates information from the artificial intelligence engine and the emotion engine to generate recommendations for the most suitable research facilities and seminars for the user. This information is sent to the terminal, where the user is shown the recommendation level and detailed information. The final input is the analyzed suitability score and emotion information, and the output is the recommendation result provided to the user.

[0767] (Application Example 2)

[0768] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0769] Traditional recommendation systems for research labs and seminars have a problem in that they cannot make recommendations that take into account the individual feelings of users, and it is difficult to deliver appropriate content that takes into account learning motivation and emotions. In particular, there was a need for a system that takes into account the impact of users' emotional states on learning motivation.

[0770] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0771] In this invention, the server includes means for collecting user input information and creating a profile of learning aspirations; means for collecting public and private information regarding research laboratories and seminars of educational institutions and forming an information storage device; means for using calculation means to recommend suitable research laboratories and seminars based on the collected information; and means for using a processing structure that includes emotion analysis means for recognizing the user's emotional state and optimizing the recommendation content. This makes it possible to recommend research laboratories and seminars that are tailored to the individual emotional state of each user.

[0772] "Means for collecting user input information and creating a profile regarding learning aspirations" refers to a function that collects information on the user's interests and academic abilities, and generates an individualized profile that summarizes their learning preferences and goals based on that information.

[0773] "Means for collecting public and private information concerning research laboratories and seminars of educational institutions and forming an information storage device" refers to the process of collecting publicly available information and limited access information provided by schools and research facilities, and constructing a database or similar device for organizing and storing this information.

[0774] "Methods for recommending suitable research laboratories and seminars based on collected information" refers to methods for analyzing user profiles and collected research laboratories and seminar information, and performing computational processing to select the most suitable candidates. This may include artificial intelligence algorithms.

[0775] "Means using a processing structure that includes sentiment analysis means for recognizing the user's emotional state and optimizing recommendation content" refers to a processing device or procedure for determining the emotional state from the text or voice expressed by the user and adjusting the recommendation content to provide information in a manner that matches that emotion.

[0776] A description of the embodiment for carrying out the invention will be given.

[0777] In the system that realizes this invention, the terminal first receives input information from the user and builds a profile of the user based on data related to their interests and academic abilities. The server collects this profile information and plays the role of gathering public and private information about research laboratories and seminars at educational institutions via the internet and creating a database. This process utilizes high-performance database technology and network communication technology.

[0778] Based on the collected data, the server uses an artificial intelligence engine built with Python and TensorFlow to select the research lab or seminar that best matches the user profile. Hugging Face Transformers is used for natural language processing, and a deep learning algorithm is employed to calculate the fit score.

[0779] Furthermore, the server analyzes the user's input text or voice data using the Google Cloud Natural Language API to recognize the user's emotional state. The results of the sentiment analysis are used to optimize recommendations, forming a feedback loop to provide information that aligns with the user's emotions.

[0780] For example, if a user has a profile indicating they are "interested in psychology research but feel anxious in new environments," the AI ​​engine will recommend research labs with well-established support systems within the field of psychology. The system's emotion engine takes into account the emotion of "anxiety" and emphasizes and recommends candidates with active communities that can provide a sense of security.

[0781] An example of a prompt sentence to input into the generative AI model is, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me." This allows the user to receive highly personalized information based on their emotions and select the optimal learning environment.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The device receives input information from the user. Specifically, the user inputs data about their interests, academic ability, desired field of study, and emotions into the device. The input data is compiled into a profile and sent to the server.

[0785] Step 2:

[0786] The server receives profile information sent by the user and stores it in a database. The stored profile information is used to measure the user's interests and academic abilities, and serves as the basis for recommendations.

[0787] Step 3:

[0788] The server collects public and private information about research labs and seminars at educational institutions via the internet and stores this information in a new data storage device. Database cleanup is performed periodically to ensure that data is stored reliably and without duplication.

[0789] Step 4:

[0790] The server uses information from the database to launch an artificial intelligence engine built with Python and TensorFlow to analyze potential research labs and seminars that match the user profile. For natural language processing, Hugging Face Transformers are used to calculate the degree of agreement between the input profile information and the database information. Based on these results, a suitability score for research labs and seminars is calculated, and candidates are selected.

[0791] Step 5:

[0792] The server uses the Google Cloud Natural Language API to analyze the emotional state of the user's input text and voice data. The results of the emotional analysis (e.g., anxiety, excitement, relief) are combined with a relevance score, which is the output of the AI ​​engine, to optimize recommendations.

[0793] Step 6:

[0794] The server sends optimized recommendations to the terminal and presents them to the user on the terminal. The user then uses the displayed information to check the details of the recommended research labs and seminars and utilizes it for career choices. An example of a specific prompt message would be a request like, "I'm interested in psychology, but I feel anxious. Please recommend the best research lab for me."

[0795] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0798] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0799] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0800] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0801] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0802] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0803] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0804] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0805] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0806] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0807] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0808] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0809] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0810] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0811] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0812] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0813] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0814] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0815] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A means of collecting user input information and creating a profile regarding learning aspirations,

[0819] A means of collecting public and private information about research laboratories and seminars at educational institutions and forming a database,

[0820] A method using an artificial intelligence engine to recommend suitable research laboratories and seminars based on collected information,

[0821] A means of presenting users with information on recommended research laboratories and seminars,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising a deep learning algorithm using natural language processing for calculating a suitability score based on the user's interests and academic ability information.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including non-public information provided by educational institutions.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means of collecting user input information and creating a profile regarding learning aspirations,

[0830] A means of collecting public and private information concerning research facilities and seminars of educational institutions to form a data set,

[0831] A means of using artificial intelligence components to recommend suitable research facilities and seminars based on the collected information,

[0832] A means of presenting users with information on recommended research facilities and seminars,

[0833] To assist users in making choices, we provide means to offer comparison and favorites registration functions,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising deep learning processing using natural language processing to calculate a suitable value based on the user's interests and academic ability information.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including non-public information provided by educational institutions.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A means of collecting user input information and creating a profile regarding learning aspirations,

[0842] A means of collecting public and private information about research laboratories and seminars at educational institutions and forming a database,

[0843] A method using an artificial intelligence engine to recommend suitable research laboratories and seminars based on collected information,

[0844] A means of acquiring location information and performing image recognition on surrounding objects,

[0845] A means of presenting relevant information about the recognized object in real time,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising a deep learning algorithm using natural language processing to calculate a suitability score based on the user's interests and academic ability information, and which analyzes visual information to present relevant academic information.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including non-public information provided by educational institutions, and for identifying objects using image recognition technology.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means of collecting user input information and creating information about learning preferences,

[0854] A means of collecting public and private information concerning research facilities and seminars of educational organizations to form an information collection,

[0855] A method using an artificial intelligence model to recommend suitable research facilities and seminars based on collected information,

[0856] A means of using an emotion analysis device to analyze the emotional state of users and optimize recommendation content,

[0857] A means of presenting users with information on recommended research facilities and seminars,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, comprising a deep learning algorithm using natural language processing for calculating a fit score based on user interests and learning ability information.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including confidential information provided by educational organizations.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] A means of collecting user input information and creating a profile regarding learning aspirations,

[0866] A means for collecting public and private information concerning research laboratories and seminars of educational institutions and forming an information storage device,

[0867] A means of using computational means to recommend suitable research laboratories and seminars based on the collected information,

[0868] A means of using a processing structure that includes emotion analysis means for recognizing the emotional state of the user and optimizing the recommendation content,

[0869] A means of presenting users with information on recommended research laboratories and seminars,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising a deep learning algorithm that uses document processing to calculate a suitability score based on the user's interests and academic ability information.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including non-public information provided by educational institutions. [Explanation of Symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user input information and creating a profile regarding learning aspirations, A means of collecting public and private information about research laboratories and seminars at educational institutions and forming a database, A method using an artificial intelligence engine to recommend suitable research laboratories and seminars based on collected information, A means of presenting users with information on recommended research laboratories and seminars, A system that includes this.

2. The system according to claim 1, comprising a deep learning algorithm that utilizes natural language processing to calculate a suitability score based on the user's interests and academic ability information.

3. The system according to claim 1, comprising means for periodically collecting and updating data from various sources, including non-public information provided by educational institutions.

Images