System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting the latest technical information, a means for analyzing an input related to an idea or a project from a user, a means for retrieving a related past project or idea and generating an advice or a solution, a means for analyzing a hypothesis or an experimental idea and proposing an appropriate experimental design or parameter setting, and a means for providing specific learning materials or resources.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Problems faced by researchers and developers include a lack of the latest technical information, a lack of new ideas, difficulty in hypothesis verification and experimental design, and difficulty in obtaining learning resources. To solve these problems and improve the efficiency of research and development work, there is a need for comprehensive support tools. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing the following means: a system including means for collecting the latest technical information, means for analyzing inputs from users regarding ideas and projects, means for searching related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experiment ideas and proposing appropriate experiment designs and parameter settings, and means for providing specific learning materials and resources. This system functions as follows.

[0006] 1. A means of scraping data from external, reliable sources to gather the latest technical information.

[0007] 2. A means of analyzing user input using a natural language processing module and converting it into structured data.

[0008] This will enable researchers and developers to easily obtain the latest information, discover ideas and advance projects, verify hypotheses and design experiments, and obtain learning resources, thereby improving the efficiency and quality of research and development.

[0009] "Latest technical information" refers to the latest knowledge regarding cutting-edge technological trends and research results.

[0010] "Means of collection" refers to methods and mechanisms for efficiently obtaining and storing specific information.

[0011] "User input" refers to information or requests provided by system users using their terminals.

[0012] "Means of analysis" refers to the methods and mechanisms for analyzing, understanding, and semantically interpreting input data.

[0013] "Relevant past projects and ideas" refers to past research and development that is related to current themes and issues.

[0014] "Search means" refers to a method or mechanism for finding required information based on specific conditions.

[0015] "Advice and solutions" refers to advice and methods for solving problems on a given issue or topic.

[0016] A "hypothesis or experimental idea" is a proposed experimental design or method to test a particular scientific question or hypothesis.

[0017] "Appropriate experimental design and parameter settings" refers to the design and setting of variables necessary to effectively conduct an experiment.

[0018] "Means of provision" refers to the method or mechanism for providing the required information or resources to users.

[0019] "System" means a set of components that integrate multiple elements or functions and function as a whole to achieve a specific purpose.

[0020] A "natural language processing module" is a program or algorithm that enables a computer to understand and process human language.

[0021] "Means of transforming into structured data" refers to methods or techniques that transform unstructured data (e.g., text-based input) into a specific format or form. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention is a system for solving problems faced by researchers and developers and improving the efficiency of research and development work. This system provides the following main functions:

[0044] 1. Providing the latest technical information

[0045] The server periodically collects the latest technical information from reliable technical information databases and external information sources. The collected information is stored in the database and provided to users as needed. This allows users to always have access to the latest knowledge and apply it to their own research and development.

[0046] Examples:

[0047] When a user searches for "latest machine learning algorithms" through a terminal, the server collects relevant technical information and displays it to the user.

[0048] 2. Discovering ideas and promoting projects

[0049] Users input information about their ideas and projects through their devices. The input information is analyzed by a natural language processing module and sent to the server as structured data. The server then searches past projects and ideas and generates relevant advice and solutions. This allows users to gain new perspectives and ideas, helping to advance their projects.

[0050] Examples:

[0051] When a user types into their device, "I'd like some ideas for new image recognition methods," the server searches for past related projects and displays suggestions to the user.

[0052] 3. Support for hypothesis verification and experimental design

[0053] When a user inputs a hypothesis or experimental idea into a terminal, the server receives and analyzes it. Based on the input hypothesis or experimental idea, the server proposes appropriate experimental design and parameter settings. It also evaluates the validity of the hypothesis and provides feedback. This allows users to design effective experiments and improve the accuracy of verification.

[0054] Examples:

[0055] When a user inputs that they would like to validate a new model on a specific dataset, the server suggests appropriate validation methods and parameter settings and displays them to the user.

[0056] 4. Providing learning materials and resources

[0057] Users can request specific learning materials and resources through their devices. In response to this request, the server searches for the necessary information from databases and external repositories and provides it to the user. This allows users to easily obtain the materials and resources they need and use them to improve their skills and knowledge.

[0058] Examples:

[0059] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0060] The system works by relying on communication between the server and the terminal, providing appropriate information and support in response to user input. It also combines natural language processing, database search, analysis, and feedback functions to provide comprehensive research and development support. This allows researchers and developers to quickly obtain the information and resources they need and work efficiently.

[0061] The processing flow will be explained below.

[0062] 1. Providing the latest technical information

[0063] Processing Steps:

[0064] Step 1:

[0065] A user types "latest tech info" into the search bar on their device.

[0066] Step 2:

[0067] The terminal sends this input to the server as an HTTP request.

[0068] Step 3:

[0069] The server queries a database of technical information to obtain the latest information.

[0070] Step 4:

[0071] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[0072] Step 5:

[0073] The server updates the database with the latest information (if necessary).

[0074] Step 6:

[0075] The server formats the latest acquired technical information and returns it to the terminal.

[0076] Step 7:

[0077] The terminal receives the data from the server and displays it to the user.

[0078] 2. Discovering ideas and promoting projects

[0079] Processing Steps:

[0080] Step 1:

[0081] Users input their ideas and project-related issues into the terminal.

[0082] Step 2:

[0083] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[0084] Step 3:

[0085] The server searches a database for similar past projects and ideas.

[0086] Step 4:

[0087] The server uses an internal analytics engine to generate relevant solutions and advice.

[0088] Step 5:

[0089] The server returns the search results and generated advice to the terminal.

[0090] Step 6:

[0091] The terminal displays the response from the server to the user.

[0092] 3. Support for hypothesis verification and experimental design

[0093] Processing Steps:

[0094] Step 1:

[0095] The user inputs hypotheses and experiment ideas into the terminal.

[0096] Step 2:

[0097] The terminal sends this input as structured data to the server.

[0098] Step 3:

[0099] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[0100] Step 4:

[0101] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[0102] Step 5:

[0103] The server performs hypothesis validation and generates feedback.

[0104] Step 6:

[0105] The server returns the analysis results and suggestions to the device.

[0106] Step 7:

[0107] The terminal displays feedback and suggestions from the server to the user.

[0108] 4. Providing learning materials and resources

[0109] Processing Steps:

[0110] Step 1:

[0111] A user inputs a request for a particular learning material or resource into a terminal.

[0112] Step 2:

[0113] The terminal sends this request to the server.

[0114] Step 3:

[0115] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[0116] Step 4:

[0117] The server also searches external repositories and databases to retrieve the required resources.

[0118] Step 5:

[0119] The server organizes the acquired information and returns it to the terminal.

[0120] Step 6:

[0121] The terminal displays the learning materials and resources provided by the server to the user.

[0122] Example 1

[0123] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0124] In modern research and development, researchers and developers are required to efficiently gather the latest information from a vast amount of technical information and to quickly and effectively verify their own ideas and hypotheses. However, processes such as information gathering, idea generation, and experimental design require a great deal of time and effort, which creates a bottleneck in research and development. The purpose of this invention is to solve these problems and dramatically improve the efficiency of research and development.

[0125] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0126] In this invention, the server includes means for collecting the latest technical information and storing it in a database, means for analyzing input from users about ideas and projects and converting them into structured data, means for searching past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for searching for and providing specific learning materials and resources, means for receiving requests from users via their terminals and providing information and data in response, and means for generating information based on prompt statements using a generative AI model. This allows users to quickly collect the latest technical information and efficiently discover ideas and verify hypotheses.

[0127] "Latest technical information" refers to the latest research results and technical reports that are novel and inventive, and is information that is regularly collected from external reliable sources.

[0128] A "database" is an information management system that can efficiently store, manage, and search collected technical information, project data, and resources.

[0129] "Users" refer to researchers and developers who use this system to gather information, generate ideas, and test hypotheses.

[0130] "Input related to ideas and projects" refers to text data that users input into a terminal in natural language about their thoughts and plans regarding new research themes or development projects.

[0131] "Structured Data" means data that has been analyzed by a natural language processing module and converted into a specific format that allows the server to effectively process, search, and create suggestions.

[0132] "Past projects and ideas" is data containing information about previous research and development that can be used to generate new ideas and solutions.

[0133] The "means for generating advice and solutions" is a module that allows the server to generate relevant suggestions and problem-solving methods based on past data and provide them to the user.

[0134] "Hypothesis and experimental ideas" refers to theoretical claims that the user wants to verify and plans for actual experiments.

[0135] "Experimental design" refers to a plan for the methods, procedures, parameter settings, etc. required to conduct scientific verification.

[0136] "Learning materials and resources" are the educational materials and reference materials that users need to improve their skills and acquire knowledge.

[0137] A "terminal" is a device, such as a computer or smartphone, that a user uses to input and receive information.

[0138] A "request" refers to input sent by a user to a server via a terminal requesting specific information or assistance.

[0139] A "generative AI model" is an artificial intelligence model for natural language processing and data generation that is used to generate information based on user prompts.

[0140] A "prompt sentence" is an instruction sentence entered into a generative AI model to prompt it to generate specific information.

[0141] MODE FOR CARRYING OUT THE INVENTION

[0142] This invention is a comprehensive support system that allows researchers and developers to efficiently gather information, develop ideas, and verify hypotheses. This system provides optimal information and support in response to user requests through collaboration between the server and terminals.

[0143] 1. Configuring the environment

[0144] The server collects the latest technical information and stores it in a database. It also uses a natural language processing module, a data search module, and a generative AI model to analyze user input and provide relevant information. The terminal is a device through which users input and retrieve information, and communicates with the server via a browser or dedicated application.

[0145] The servers use cloud infrastructure with high-performance processors and large storage capacity for hardware, while the terminals are ordinary computers or smartphones.

[0146] The software uses various open source libraries and tools, such as "spaCy" for natural language processing, "BeautifulSoup" for data collection, and "GPT-4 (registered trademark)" for generative AI models.

[0147] 2. Collecting technical information

[0148] The server periodically collects the latest technical information from external reliable sources (e.g., academic paper databases, specialized websites), and organizes and stores the collected information in a database.

[0149] Examples:

[0150] If a user searches for "latest machine learning algorithms," the server collects relevant information from internal databases and external sources and provides it to the user.

[0151] 3. Promoting ideas and projects

[0152] When a user inputs information about an idea or project through a device, the server uses a natural language processing module to analyze it and convert it into structured data, which is then used to search a database of past projects and generate related information and proposals.

[0153] Examples:

[0154] When a user types in "I'd like some ideas for new image recognition methods," the server searches for past related projects and presents suggestions to the user.

[0155] 4. Hypothesis Verification and Experimental Design

[0156] When a user inputs a hypothesis or experimental idea, the server analyzes it, suggests appropriate experimental designs and parameter settings, evaluates the validity of the hypothesis, and provides feedback.

[0157] Examples:

[0158] When a user inputs, "I would like to validate a new model on a specific dataset," the server suggests appropriate validation methods and parameter settings and displays feedback.

[0159] 5. Providing learning materials and resources

[0160] Upon user request, the server retrieves the necessary learning materials and resources from internal databases and external repositories and provides them to the user.

[0161] Examples:

[0162] When a user requests "I want the latest papers on deep learning," the server searches for relevant papers and provides them to the user.

[0163] Prompt Sentence Examples

[0164] "Tell me about the latest machine learning algorithms."

[0165] "I'd like some ideas for new image recognition methods."

[0166] "I want to validate my new model on a specific dataset."

[0167] "I want the latest paper on deep learning."

[0168] In this way, the present invention realizes a system that provides efficient information and support to researchers and developers.

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1:

[0171] The user inputs information into the terminal. For example, they type, "Please tell me about the latest machine learning algorithms." The user's input is sent to the server by pressing the execute button.

[0172] Input: The text entered by the user.

[0173] Output: Request from the device to the server.

[0174] Specific behavior:

[0175] The terminal retrieves the text entered by the user into the form and sends an HTTP POST request to the server.

[0176] Step 2:

[0177] The server parses the received request, which contains the text entered by the user, and uses a natural language processing module to parse it.

[0178] Input: The request the user sent to the server.

[0179] Output: Structured data parsed by the natural language processing module.

[0180] Specific behavior:

[0181] The server uses natural language processing libraries such as spaCy to analyze the user's input text and extract keywords and contextual information, which is then converted into structured data.

[0182] Step 3:

[0183] The server uses the structured data to generate appropriate database queries to retrieve relevant information from internal databases or external sources.

[0184] Input: Structured data.

[0185] Output: The retrieved technical information.

[0186] Specific behavior:

[0187] The server generates SQL queries or API requests to retrieve relevant technical information from databases or external sources (e.g., academic paper databases).

[0188] Step 4:

[0189] The server organizes the search results, converts them into a format useful to the user, and uses generative AI models to generate information based on prompts (if needed).

[0190] Input: The retrieved technical information and the prompt statement.

[0191] Output: The organized technical information or information generated.

[0192] Specific behavior:

[0193] The server organizes the information it receives and, if necessary, uses a generative AI model (e.g., GPT-4) to generate information based on the user's prompt.

[0194] Step 5:

[0195] The server transmits the data to the terminal to provide the organized or generated information to the user.

[0196] Input: Organized technical information or generated information.

[0197] Output: Information provided to the user.

[0198] Specific behavior:

[0199] The server sends the organized information to the terminal in an appropriate format, such as JSON, and the terminal parses it and displays it to the user.

[0200] In this way, this system realizes a mechanism for efficiently collecting, analyzing, and providing information in response to user requests through a series of processing steps.

[0201] (Application example 1)

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

[0203] Researchers and developers are required to collect the latest technological information, discover and promote their own ideas, and effectively verify hypotheses and design experiments. However, it is difficult to efficiently obtain the necessary data from vast amounts of information and to optimally design experiments and set parameters. Furthermore, accessing learning materials and resources is time-consuming, and the latest information is constantly being updated, making it difficult to obtain the necessary information.

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

[0205] In this invention, the server includes means for collecting the latest technical information, means for analyzing input from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for generating and displaying advice related to projects based on user input, means for proposing ideas and experimental designs based on input information using a generative AI model, and means for automating access to the latest technologies and learning materials using prompt sentences. This enables researchers and developers to efficiently and quickly obtain the information and resources they need, improving the efficiency of research and development work.

[0206] "Means for collecting the latest technological information" refers to means for regularly collecting information on the latest technologies from reliable databases and external information sources and storing it in a database.

[0207] The "means for analyzing inputs from users regarding ideas and projects" refers to a means for analyzing information regarding ideas and projects input by users via their terminals and transmitting the information to a server as structured data.

[0208] "Means for searching relevant past projects and ideas and generating advice and solutions" means means for searching a database of past projects and generating relevant advice and solutions based on user input.

[0209] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to means for analyzing hypotheses and experimental ideas submitted by users and proposing optimal experimental designs and parameter settings based on those.

[0210] "Means for providing specific learning materials and resources" refers to means for searching for and providing the necessary learning materials and resources from databases and external repositories in response to a user's request.

[0211] The "means for generating and displaying advice related to a project based on user input" refers to means for analyzing project-related information input by a user and generating and displaying related advice.

[0212] "Means for using a generative AI model to propose ideas and experimental designs based on input information" refers to means for proposing new ideas and experimental designs using an artificial intelligence model generated based on input information.

[0213] "Means for automating access to the latest technology and learning materials using prompt sentences" means means for automatically accessing the latest technology and necessary learning materials using specific prompt sentences and providing the results to the user.

[0214] This invention is a system that helps researchers and developers efficiently collect the latest technical information, discover and promote ideas, verify hypotheses, and provide learning materials and resources. This system is composed of a server, terminals, and user interactions.

[0215] System configuration

[0216] The system consists of the following main modules:

[0217] A module that collects the latest technical information

[0218] A module that analyzes idea and project inputs

[0219] A module that searches past projects and ideas and generates advice

[0220] A module that analyzes hypotheses and experimental ideas and proposes experimental designs and parameter settings

[0221] Modules that provide learning materials and resources

[0222] A module that displays project-related advice based on user input.

[0223] A module that proposes ideas and experimental designs using generative AI models

[0224] A module that automates access to the latest technology and learning materials using prompts

[0225] Program processing overview

[0226] The server periodically obtains the latest technical information from reliable external sources and stores the information in a database. In response to a user's request, the server searches this database and provides the latest technical information.

[0227] When a user inputs a new project or idea through their device, the information is analyzed by a natural language processing module and sent as structured data to the server, which then searches for relevant information in a database of past projects and provides the user with the best advice and solutions.

[0228] Similarly, for hypotheses and experimental ideas, the system receives user input and suggests appropriate experimental designs and parameter settings, and uses generative AI models to suggest new ideas and experimental designs based on the information entered by the user.

[0229] Regarding learning materials and resources, upon user request, the system searches for the necessary materials from databases and external repositories and provides them to the user, improving user efficiency by automating access to specific technical information and papers, especially using prompts.

[0230] Hardware and software used

[0231] Hardware: Servers, devices (PCs, smartphones, etc.)

[0232] Software: Flask (web framework), natural language processing modules (e.g., spaCy, NLTK), database management systems (e.g., PostgreSQL)

[0233] Specific examples

[0234] For example, if a user searches for "latest machine learning algorithms," they might use the following prompt:

[0235] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[0236] This is a procedure by which the server retrieves relevant technical information from a database and provides it to the user.

[0237] Also, if the user wants ideas for new image recognition techniques, they can use the following prompt:

[0238] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[0239] This prompt will cause the server to use a generative AI model to search a database of relevant projects and provide appropriate advice to the user.

[0240] As described above, the present invention is a multi-functional system for efficiently collecting the latest technical information and supporting research and development.

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

[0242] Step 1:

[0243] The user sends a request through the terminal, for example, by entering a prompt to get information about the latest machine learning algorithms:

[0244] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[0245] This request is sent to the server.

[0246] Step 2:

[0247] The server receives the request and analyzes the specified query. It analyzes the input query (e.g., "latest machine learning algorithms") and prepares to perform a database search based on the query. Specifically, it searches for related technical information using the query as a key.

[0248] Step 3:

[0249] The server searches the database for relevant technical information. It uses a database management system (e.g., PostgreSQL) to find the latest technical information that corresponds to the query. It retrieves the search results and formats the data as needed.

[0250] Step 4:

[0251] The server returns the search results to the user. The searched technical information is sent to the user as structured data in JSON format, allowing the user to view the requested technical information on their device.

[0252] Step 5:

[0253] A user submits input about a new idea or project. For example, a user might type "I'm looking for ideas for new image recognition methods":

[0254] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[0255] This request is sent to the server.

[0256] Step 6:

[0257] The server receives the user's input and parses it using a natural language processing module. The input text ("I'd like some ideas for new image recognition methods") is parsed and converted into structured data.

[0258] Step 7:

[0259] The server uses a generative AI model to generate ideas and advice based on the user's input. Using the analyzed data, the generative AI model suggests appropriate ideas and experimental designs.

[0260] Step 8:

[0261] The server returns the generated ideas and advice to the user. The advice generated by the generative AI model is sent to the user as structured data in JSON format, allowing the user to check the requested ideas and advice on their device.

[0262] Step 9:

[0263] A user inputs a hypothesis or experiment idea and sends it to the server. For example, the user might input, "I want to test a new model on a specific dataset." The server receives this request.

[0264] Step 10:

[0265] The server analyzes the user's hypotheses and experiment ideas, proposes appropriate experimental designs and parameter settings, and uses a natural language processing module to analyze the input and search for appropriate experimental designs and parameter settings from past projects and databases.

[0266] Step 11:

[0267] The server returns the proposed experimental design and parameter settings to the user. The analysis results are sent to the user as structured data in JSON format, allowing the user to check the proposed experimental design and parameter settings on their device.

[0268] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0269] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0270] Function 1: Providing the latest technical information

[0271] The server periodically collects the latest technical information from a technical information database and external reliable information sources, and stores the collected information in the database.

[0272] When a user requests the "latest technical information," the device sends an HTTP request to the server.

[0273] The server retrieves the collected technical information and provides it to the user.

[0274] Examples:

[0275] When a user searches for "latest machine learning algorithms" through their device, the server provides relevant technical information and displays it on the device.

[0276] Function 2: Idea generation and project promotion

[0277] Users input ideas and project-related issues into the device.

[0278] The terminal analyzes the input information using a natural language processing (NLP) module and sends it to the server as structured data.

[0279] The server searches past projects and ideas and generates relevant advice and solutions.

[0280] Examples:

[0281] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[0282] Function 3: Support for hypothesis testing and experimental design

[0283] Users input hypotheses and experiment ideas into the terminal.

[0284] The terminal transmits the input hypotheses and experimental ideas to the server as structured data.

[0285] The server analyzes the hypothesis and suggests appropriate experimental designs and parameter settings.

[0286] Examples:

[0287] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[0288] Function 4: Providing learning materials and resources

[0289] The user types a request for specific learning materials or resources into the device.

[0290] The terminal sends a request to the server.

[0291] The server retrieves the required information from databases and external repositories and provides it to the user.

[0292] Examples:

[0293] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0294] Feature 5: Adding an emotion engine

[0295] When analyzing user input with the natural language processing module, an emotion engine is also used.

[0296] The emotion engine recognizes emotions from user input and reflects that information in the analysis results.

[0297] The server tailors advice and solutions based on emotions, providing more personalized support.

[0298] Examples:

[0299] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[0300] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[0301] The processing flow will be explained below.

[0302] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0303] Function 1: Providing the latest technical information

[0304] Processing Steps:

[0305] Step 1:

[0306] A user types "latest tech info" into the search bar on their device.

[0307] Step 2:

[0308] The terminal sends this input to the server as an HTTP request.

[0309] Step 3:

[0310] The server queries a database of technical information to obtain the latest information.

[0311] Step 4:

[0312] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[0313] Step 5:

[0314] The server updates the database with the latest information (if necessary).

[0315] Step 6:

[0316] The server formats the latest acquired technical information and returns it to the terminal.

[0317] Step 7:

[0318] The terminal receives the data from the server and displays it to the user.

[0319] Function 2: Idea generation and project promotion

[0320] Processing Steps:

[0321] Step 1:

[0322] Users input their ideas and project-related issues into the terminal.

[0323] Step 2:

[0324] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[0325] Step 3:

[0326] The server searches a database for similar past projects and ideas.

[0327] Step 4:

[0328] The server uses an internal analytics engine to generate relevant solutions and advice.

[0329] Step 5:

[0330] The server returns the search results and generated advice to the terminal.

[0331] Step 6:

[0332] The terminal displays the response from the server to the user.

[0333] Function 3: Support for hypothesis testing and experimental design

[0334] Processing Steps:

[0335] Step 1:

[0336] The user inputs hypotheses and experiment ideas into the terminal.

[0337] Step 2:

[0338] The terminal sends this input as structured data to the server.

[0339] Step 3:

[0340] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[0341] Step 4:

[0342] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[0343] Step 5:

[0344] The server performs hypothesis validation and generates feedback.

[0345] Step 6:

[0346] The server returns the analysis results and suggestions to the device.

[0347] Step 7:

[0348] The terminal displays feedback and suggestions from the server to the user.

[0349] Function 4: Providing learning materials and resources

[0350] Processing Steps:

[0351] Step 1:

[0352] A user inputs a request for a particular learning material or resource into a terminal.

[0353] Step 2:

[0354] The terminal sends this request to the server.

[0355] Step 3:

[0356] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[0357] Step 4:

[0358] The server also searches external repositories and databases to retrieve the required resources.

[0359] Step 5:

[0360] The server organizes the acquired information and returns it to the terminal.

[0361] Step 6:

[0362] The terminal displays the learning materials and resources provided by the server to the user.

[0363] Feature 5: Adding an emotion engine

[0364] Processing Steps:

[0365] Step 1:

[0366] The user expresses emotions through text or voice data entered into the terminal.

[0367] Step 2:

[0368] The device passes the user's input to a natural language processing module and an emotion engine to analyze emotions.

[0369] Step 3:

[0370] The emotion engine recognizes the user's emotions and sends that information to the server as structured data.

[0371] Step 4:

[0372] The server receives the user's input, including the emotion data, and performs the necessary analysis.

[0373] Step 5:

[0374] The server tailors the advice and solutions it provides based on the perceived sentiment.

[0375] Step 6:

[0376] The server generates suggestions and feedback based on the emotion and sends them to the device.

[0377] Step 7:

[0378] The terminal displays feedback to the user according to the emotion from the server.

[0379] Examples:

[0380] When a user types, "I'm feeling anxious about this project," the emotion engine recognizes the user's anxiety and the server suggests advice and resources to provide reassurance.

[0381] Example 2

[0382] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] Conventional R&D support systems have limitations in collecting technical information and proposing ideas, and lack personalized support based on the user's emotions. In particular, it is difficult to provide advice and solutions that take the user's emotions into consideration, and there has been a demand for a means to reduce the psychological burden on users in R&D work.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0385] In this invention, the server includes means for collecting the latest technical information, means for analyzing inputs from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experiment ideas and proposing appropriate experiment designs and parameter settings, means for providing specific learning materials and resources, means for recognizing the user's emotions and reflecting that information in the analysis results, and means for adjusting advice and solutions based on the emotions and providing personalized support. This makes it possible to provide personalized support that takes the user's emotions into consideration, thereby improving the efficiency of research and development work and reducing psychological burden.

[0386] "Means for collecting the latest technical information" refers to the technology for regularly collecting the latest technical information from reliable external information sources and databases, and storing and managing it.

[0387] The "means for analyzing inputs regarding ideas and projects from users" refers to a technology that uses natural language processing technology to analyze ideas and project-related information input by users and extract necessary information.

[0388] "Means for searching for relevant past projects and ideas and generating advice and solutions" refers to technology for searching for past projects and ideas in a database based on analyzed user input and generating appropriate advice and solutions.

[0389] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to technology for analyzing hypotheses and experimental ideas proposed by users and setting appropriate experimental designs and parameters based on them.

[0390] "Means for providing specific learning materials and resources" refers to the technology used to search for and provide the learning materials and resources requested by the user from databases or external repositories.

[0391] "Means for recognizing user emotions and reflecting that information in the analysis results" refers to technology that analyzes emotions from user input and reflects that emotional information in the analysis results and suggestions.

[0392] "Means for tailoring advice and solutions based on emotions and providing personalized support" refers to technology for tailoring advice and solutions based on the recognized emotions of a user and providing personalized support.

[0393] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0394] Hardware and software used

[0395] The following hardware and software are used to implement this system: A general cloud server (e.g., Amazon Web Services or Google (registered trademark) Cloud Platform) is used as the server, and MongoDB is used to store data. Computer devices such as PCs, smartphones, and tablets are used as terminals. Software such as Python, Django, SpaCy, TextBlob, SciPy, Pandas, and ElasticSearch (registered trademark) are used.

[0396] Function 1: Providing the latest technical information

[0397] The server periodically collects the latest technical information from technical information databases and external reliable sources (such as ArXiv and IEEE) and stores it in a database such as MongoDB. When a user requests the "latest technical information" through their device, the device sends an HTTP request to the server. The server searches the collected technical information and returns the results to the device. The device then displays the search results to the user.

[0398] Examples:

[0399] When a user searches for "latest machine learning algorithms," the server provides relevant information and displays it on the device.

[0400] Example prompt sentence:

[0401] "Please provide information on the latest machine learning algorithms."

[0402] Function 2: Idea generation and project promotion

[0403] When a user inputs an idea or project-related issue into the device, the device analyzes it using a natural language processing (NLP) module (e.g., SpaCy) and sends it to the server as structured data. The server then searches past projects and ideas using ElasticSearch, generates relevant advice and solutions, and returns them to the device. The device then displays the advice to the user.

[0404] Examples:

[0405] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[0406] Example prompt sentence:

[0407] "Please tell me your ideas for new image recognition methods."

[0408] Function 3: Support for hypothesis testing and experimental design

[0409] When a user inputs a hypothesis or experiment idea into the terminal, the terminal sends it as structured data to the server, which uses libraries such as SciPy and Pandas to analyze the hypothesis and propose appropriate experimental designs and parameter settings. The proposals are then returned to the terminal and displayed to the user.

[0410] Examples:

[0411] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[0412] Example prompt sentence:

[0413] "How do I validate a new model on a specific dataset?"

[0414] Function 4: Providing learning materials and resources

[0415] When a user requests a specific learning material or resource (e.g., a recent paper or tutorial), the device sends it to the server, which retrieves the required information from a database or external repository and returns it to the device, which then displays the search results to the user.

[0416] Examples:

[0417] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0418] Example prompt sentence:

[0419] "Please provide the latest papers on deep learning."

[0420] Feature 5: Adding an emotion engine

[0421] When analyzing user input using the natural language processing module, an emotion engine (e.g., TextBlob) is also used to obtain an emotion score. The device then sends the analysis data, including the emotion score, to the server. The server then adjusts advice and solutions based on the emotion, providing personalized support. The adjusted advice is then returned to the device and displayed to the user.

[0422] Examples:

[0423] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[0424] Example prompt sentence:

[0425] "I'm feeling anxious about this project. Can you give me some advice?"

[0426] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[0427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0428] Function 1: Providing the latest technical information

[0429] Step 1:

[0430] The server periodically collects the latest technical information from technical information databases and external reliable sources. Specifically, the server runs scraping scripts to retrieve data from, for example, academic paper repositories. The collected data is then stored in MongoDB.

[0431] Input: URL or API key of external source

[0432] Output: Latest technical information data

[0433] Step 2:

[0434] When a user searches for the latest technological information, they enter the search keyword into their device. Specifically, the user enters "latest machine learning algorithms" into the browser search form.

[0435] Input: Search keyword

[0436] Output: Search request

[0437] Step 3:

[0438] The device sends the user's request to the server. Specifically, the device sends an HTTP GET request to the server.

[0439] Input: Search request

[0440] Output: HTTP request

[0441] Step 4:

[0442] The server searches for information based on the search keywords from the technical information database and returns the results to the terminal. Specifically, the server searches for information from MongoDB using Django view functions and returns the search results to the terminal in JSON format.

[0443] Input: Search request

[0444] Output: Search result data

[0445] Step 5:

[0446] The device displays the search results to the user. Specifically, the device uses JavaScript (registered trademark) to display the search results in HTML.

[0447] Input: Search result data

[0448] Output: Displayed technical information

[0449] Function 2: Idea generation and project promotion

[0450] Step 1:

[0451] The user inputs ideas and project-related issues into the device. Specifically, the user enters "I'm looking for ideas for new image recognition methods" into the browser input form.

[0452] Input: Text about your idea or assignment

[0453] Output: User input data

[0454] Step 2:

[0455] The device analyzes the input information using a natural language processing (NLP) module. Specifically, the device uses SpaCy to analyze user input and extract keywords and key phrases.

[0456] Input: User-entered data

[0457] Output: Parsed data

[0458] Step 3:

[0459] The device converts the parsed data into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[0460] Input: Parsed data

[0461] Output: Structured data (JSON)

[0462] Step 4:

[0463] The server searches past projects and ideas, generates relevant advice and solutions, and uses ElasticSearch to search the database, collects relevant information, and returns the generated advice in JSON format to the device.

[0464] Input: Structured data (JSON)

[0465] Output: Advice and solution data

[0466] Step 5:

[0467] The device displays advice and solutions to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[0468] Input: Advice and solution data

[0469] Output: The displayed advice or solution

[0470] Function 3: Support for hypothesis testing and experimental design

[0471] Step 1:

[0472] The user inputs hypotheses and experimental ideas into the device, and specifically, the user writes in the notebook-style UI that they want to validate a new model on a specific dataset.

[0473] Input: Hypothesis or experimental idea text

[0474] Output: User input data

[0475] Step 2:

[0476] The device converts the input information into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[0477] Input: User-entered data

[0478] Output: Structured data (JSON)

[0479] Step 3:

[0480] The server analyzes the hypotheses and proposes appropriate experimental designs and parameter settings. Specifically, the server analyzes the hypotheses using SciPy and Pandas, proposes experimental designs, and returns the proposed designs to the terminal in JSON format.

[0481] Input: Structured data (JSON)

[0482] Output: Experimental design proposal data

[0483] Step 4:

[0484] The device displays the suggestions to the user. Specifically, the device uses JavaScript to display the suggestions in HTML.

[0485] Input: Experimental design proposal data

[0486] Output: Experimental design proposal displayed

[0487] Function 4: Providing learning materials and resources

[0488] Step 1:

[0489] The user inputs specific learning materials and resources into the device. Specifically, the user types "I want the latest papers on deep learning" into a browser form.

[0490] Input: Text of specific learning materials or resources

[0491] Output: User input data

[0492] Step 2:

[0493] The device sends a request to the server. Specifically, the device sends data to the server using an HTTP GET request.

[0494] Input: User-entered data

[0495] Output: HTTP request

[0496] Step 3:

[0497] The server searches for relevant learning materials and resources from databases and external repositories and returns the results to the device. Specifically, the server searches for materials from GitHub, ArXiv, etc. based on the search keywords and returns them in JSON format to the device.

[0498] Input: HTTP request

[0499] Output: Learning materials and resource data

[0500] Step 4:

[0501] The device displays the search results to the user. Specifically, the device uses JavaScript to display the results in HTML.

[0502] Input: Study materials and resource data

[0503] Output: Displayed learning materials and resources

[0504] Feature 5: Adding an emotion engine

[0505] Step 1:

[0506] The user types input into the terminal. Specifically, the user types "I'm worried about this project" into a browser form.

[0507] Input: Sentiment text

[0508] Output: User input data

[0509] Step 2:

[0510] The device analyzes the input information using a natural language processing module (TextBlob) and obtains an emotion score. Specifically, the device calculates the emotion score using the TextBlob library.

[0511] Input: User-entered data

[0512] Output: Sentiment score

[0513] Step 3:

[0514] The device converts the data containing emotion information into JSON format and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[0515] Input: Sentiment score

[0516] Output: JSON data containing emotion information

[0517] Step 4:

[0518] The server then adjusts advice and solutions based on the emotion, providing personalized support. Specifically, the server analyzes the emotion score, generates personalized advice and resources, and returns them to the device in JSON format.

[0519] Input: JSON data containing emotion information

[0520] Output: personalized advice and solution data

[0521] Step 5:

[0522] The device then displays the adjusted advice to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[0523] Input: personalized advice and solution data

[0524] Output: The displayed advice or solution

[0525] (Application example 2)

[0526] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0527] In modern manufacturing environments, there is a need to quickly detect problems and troubles that manufacturing robots face and provide optimal solutions. Furthermore, there is a lack of means to understand the emotions of operators involved in the manufacturing process and provide personalized support. This will improve manufacturing efficiency while reducing operator stress and creating an environment where operators can perform their work with peace of mind.

[0528] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the latest technical information, means for analyzing input related to a user's ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for analyzing and recognizing user emotion data, means for a manufacturing robot to detect problems during the manufacturing process and provide related technical information, and means for providing personalized support based on the user's emotions. This enables the manufacturing robot to quickly detect problems during the manufacturing process and provide optimal solutions, as well as provide personalized support based on the operator's emotions.

[0529] "Latest technological information" refers to information on the latest research results, technological trends, methodologies and tools related to manufacturing and technological development.

[0530] "User idea and project input" refers to new technology ideas and detailed information about ongoing projects that researchers and developers provide to the system.

[0531] "Past projects and ideas" refers to previously conducted research projects and development ideas, as well as data containing their results and findings.

[0532] "Advice and solutions" is information that provides users with specific guidelines or solutions to their problems or challenges.

[0533] "Hypotheses and experimental ideas" are proposals based on experimental plans and thought processes to predict or solve phenomena or problems.

[0534] "Appropriate experimental design and parameter settings" refers to the specific experimental methods and conditions used to verify a hypothesis.

[0535] "Specific learning materials and resources" are digital content such as specific information, tools, or guidelines that users need to learn.

[0536] "User emotional data" refers to emotional information extracted from text or voice input by a user to the system.

[0537] A "manufacturing robot" is a mechanical system that automates manufacturing processes such as product assembly and processing.

[0538] A "problem during the manufacturing process" is an anomaly, error, or factor that reduces efficiency that occurs during the manufacturing process.

[0539] "Means for providing technical information" refers to a method or system for presenting collected technical information to users.

[0540] "Personalized support" is support that aims to provide assistance and advice tailored to each individual situation, based on the user's emotions and specific needs.

[0541] This invention provides an integrated system for increasing the efficiency of manufacturing robots and operators in manufacturing operations. The system consists of the following major components:

[0542] System configuration

[0543] Hardware

[0544] Manufacturing robots: Assemble and process products

[0545] Server: Processes and manages the core of the system

[0546] User device: computer or tablet used by the operator

[0547] software

[0548] Sentiment Engine: Analyze user emotions

[0549] Natural Language Processing (NLP) module: Parsing user input

[0550] Technical information collection module: Collect the latest technical information through web scraping

[0551] Project Advisor module: Search past projects and generate advice

[0552] Experimental Design Module: Propose an experimental design for hypothesis testing

[0553] Data processing and calculation

[0554] Providing the latest technical information

[0555] The server periodically collects the latest technical information from reliable sources using a technical information collection module and stores it in a database. When a user requests the latest technical information through their device, the server receives the HTTP request, searches the database for relevant technical information, and provides it to the user.

[0556] Discovering ideas and promoting projects

[0557] Users input ideas and project-related issues into their devices. The devices use an NLP module to parse the input and send it to the server as structured data. The server then uses a project advisor module to search past projects and ideas, generate relevant advice and solutions, and provide them to the user.

[0558] Support for hypothesis testing and experimental design

[0559] When a user inputs a hypothesis or experiment idea into the device, the device sends the information as structured data to the server, which then analyzes the hypothesis using an experiment design module and proposes an appropriate experimental design and parameter settings.

[0560] Providing learning materials and resources

[0561] When a user requests specific learning materials or resources, the server retrieves the required information from databases or external repositories and provides it to the user.

[0562] Using the Emotion Engine

[0563] The device analyzes the user's input using an NLP module and emotion engine, and sends the structured data and emotion data to the server, which analyzes the emotion data and tailors the provision of advice, solutions, and personalized support based on the user's emotions.

[0564] Specific examples

[0565] For example, if a manufacturing robot detects a defect during a process and the operator inputs, "I'm concerned about this new manufacturing process," the system will use its emotion engine to recognize the emotion "concern." At the same time, the server will launch a technical information collection module, searching for and displaying relevant technical information and past projects.

[0566] Prompt Sentence Examples

[0567] Analyze the sentiment of the following sentence and generate a corresponding assistance message:

[0568] "I'm worried about problems with the new manufacturing process."

[0569] Emotion: Worry

[0570] Assistance message: Don't worry, the following solutions have worked for similar processes in the past:

[0571] The system enables rapid resolution of problems faced by manufacturing robots and provides personalized support to operators, improving the efficiency and quality of manufacturing operations.

[0572] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0573] Step 1:

[0574] The user inputs information about an idea or project into the terminal, using natural language such as "I'm concerned about the new manufacturing process." This input becomes the starting point for the system's processing.

[0575] Step 2:

[0576] The device analyzes the input text using a natural language processing (NLP) module. The NLP module converts the text into structured data and sentiment data. This analysis clarifies the user's intent and sentiment. The input is text data, and the output is structured data and sentiment data.

[0577] Step 3:

[0578] The device sends the analyzed structured data and emotional data to the server. The server analyzes the emotional data and recognizes the user's emotions. Based on this recognition, the system prepares to provide personalized support. The input is the structured data and emotional data, and the output is the emotion recognition result.

[0579] Step 4:

[0580] The server launches a technical information collection module based on the structured data. The technical information collection module scrapes the latest technical information from reliable external sources and searches the information stored in the database. The input is the structured data, and the output is related technical information.

[0581] Step 5:

[0582] The server uses the Project Advisor module to search past projects and ideas, extract relevant advice and solutions from the past project database, and generate them. The input is structured data, and the output is advice and solutions.

[0583] Step 6:

[0584] The server analyzes hypotheses and experimental ideas, and proposes appropriate experimental designs and parameter settings using the experimental design module. The input is structured data of hypotheses and experimental ideas, and the output is experimental designs and parameter settings.

[0585] Step 7:

[0586] The server generates personalized support messages based on the user's emotions. The generated support messages reflect the user's emotional data and are designed to reassure the user. The input is emotional data, and the output is personalized support messages.

[0587] Step 8:

[0588] The server compiles the generated technical information, advice, experimental design, and personalized support messages and sends them to the user's device, which displays this information and makes it easily accessible to the user. The input is multiple generated data, and the output is the integrated support information.

[0589] Step 9:

[0590] The user's device displays technical information, advice, experimental designs, and personalized support messages received from the server. The user reviews this information and decides the next steps in the manufacturing process or project. The input is the integrated support information, and the output is the user's action to confirm the displayed content.

[0591] In this way, the system quickly resolves issues on the shop floor and provides personalized assistance to operators.

[0592] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0593] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0595] [Second embodiment]

[0596] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0597] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0598] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0599] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0600] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0601] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0602] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0603] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0604] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0605] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0606] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0607] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0608] The present invention is a system for solving problems faced by researchers and developers and improving the efficiency of research and development work. This system provides the following main functions:

[0609] 1. Providing the latest technical information

[0610] The server periodically collects the latest technical information from reliable technical information databases and external information sources. The collected information is stored in the database and provided to users as needed. This allows users to always have access to the latest knowledge and apply it to their own research and development.

[0611] Examples:

[0612] When a user searches for "latest machine learning algorithms" through a terminal, the server collects relevant technical information and displays it to the user.

[0613] 2. Discovering ideas and promoting projects

[0614] Users input information about their ideas and projects through their devices. The input information is analyzed by a natural language processing module and sent to the server as structured data. The server then searches past projects and ideas and generates relevant advice and solutions. This allows users to gain new perspectives and ideas, helping to advance their projects.

[0615] Examples:

[0616] When a user types into their device, "I'd like some ideas for new image recognition methods," the server searches for past related projects and displays suggestions to the user.

[0617] 3. Support for hypothesis verification and experimental design

[0618] When a user inputs a hypothesis or experimental idea into a terminal, the server receives and analyzes it. Based on the input hypothesis or experimental idea, the server proposes appropriate experimental design and parameter settings. It also evaluates the validity of the hypothesis and provides feedback. This allows users to design effective experiments and improve the accuracy of verification.

[0619] Examples:

[0620] When a user inputs that they would like to validate a new model on a specific dataset, the server suggests appropriate validation methods and parameter settings and displays them to the user.

[0621] 4. Providing learning materials and resources

[0622] Users can request specific learning materials and resources through their devices. In response to this request, the server searches for the necessary information from databases and external repositories and provides it to the user. This allows users to easily obtain the materials and resources they need and use them to improve their skills and knowledge.

[0623] Examples:

[0624] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0625] The system works by relying on communication between the server and the terminal, providing appropriate information and support in response to user input. It also combines natural language processing, database search, analysis, and feedback functions to provide comprehensive research and development support. This allows researchers and developers to quickly obtain the information and resources they need and work efficiently.

[0626] The processing flow will be explained below.

[0627] 1. Providing the latest technical information

[0628] Processing Steps:

[0629] Step 1:

[0630] A user types "latest tech info" into the search bar on their device.

[0631] Step 2:

[0632] The terminal sends this input to the server as an HTTP request.

[0633] Step 3:

[0634] The server queries a database of technical information to obtain the latest information.

[0635] Step 4:

[0636] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[0637] Step 5:

[0638] The server updates the database with the latest information (if necessary).

[0639] Step 6:

[0640] The server formats the latest acquired technical information and returns it to the terminal.

[0641] Step 7:

[0642] The terminal receives the data from the server and displays it to the user.

[0643] 2. Discovering ideas and promoting projects

[0644] Processing Steps:

[0645] Step 1:

[0646] Users input their ideas and project-related issues into the terminal.

[0647] Step 2:

[0648] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[0649] Step 3:

[0650] The server searches a database for similar past projects and ideas.

[0651] Step 4:

[0652] The server uses an internal analytics engine to generate relevant solutions and advice.

[0653] Step 5:

[0654] The server returns the search results and generated advice to the terminal.

[0655] Step 6:

[0656] The terminal displays the response from the server to the user.

[0657] 3. Support for hypothesis verification and experimental design

[0658] Processing Steps:

[0659] Step 1:

[0660] The user inputs hypotheses and experiment ideas into the terminal.

[0661] Step 2:

[0662] The terminal sends this input as structured data to the server.

[0663] Step 3:

[0664] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[0665] Step 4:

[0666] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[0667] Step 5:

[0668] The server performs hypothesis validation and generates feedback.

[0669] Step 6:

[0670] The server returns the analysis results and suggestions to the device.

[0671] Step 7:

[0672] The terminal displays feedback and suggestions from the server to the user.

[0673] 4. Providing learning materials and resources

[0674] Processing Steps:

[0675] Step 1:

[0676] A user inputs a request for a particular learning material or resource into a terminal.

[0677] Step 2:

[0678] The terminal sends this request to the server.

[0679] Step 3:

[0680] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[0681] Step 4:

[0682] The server also searches external repositories and databases to retrieve the required resources.

[0683] Step 5:

[0684] The server organizes the acquired information and returns it to the terminal.

[0685] Step 6:

[0686] The terminal displays the learning materials and resources provided by the server to the user.

[0687] Example 1

[0688] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] In modern research and development, researchers and developers are required to efficiently gather the latest information from a vast amount of technical information and to quickly and effectively verify their own ideas and hypotheses. However, processes such as information gathering, idea generation, and experimental design require a great deal of time and effort, which creates a bottleneck in research and development. The purpose of this invention is to solve these problems and dramatically improve the efficiency of research and development.

[0690] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0691] In this invention, the server includes means for collecting the latest technical information and storing it in a database, means for analyzing input from users about ideas and projects and converting them into structured data, means for searching past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for searching for and providing specific learning materials and resources, means for receiving requests from users via their terminals and providing information and data in response, and means for generating information based on prompt statements using a generative AI model. This allows users to quickly collect the latest technical information and efficiently discover ideas and verify hypotheses.

[0692] "Latest technical information" refers to the latest research results and technical reports that are novel and inventive, and is information that is regularly collected from external reliable sources.

[0693] A "database" is an information management system that can efficiently store, manage, and search collected technical information, project data, and resources.

[0694] "Users" refer to researchers and developers who use this system to gather information, generate ideas, and test hypotheses.

[0695] "Input related to ideas and projects" refers to text data that users input into a terminal in natural language about their thoughts and plans regarding new research themes or development projects.

[0696] "Structured Data" means data that has been analyzed by a natural language processing module and converted into a specific format that allows the server to effectively process, search, and create suggestions.

[0697] "Past projects and ideas" is data containing information about previous research and development that can be used to generate new ideas and solutions.

[0698] The "means for generating advice and solutions" is a module that allows the server to generate relevant suggestions and problem-solving methods based on past data and provide them to the user.

[0699] "Hypothesis and experimental ideas" refers to theoretical claims that the user wants to verify and plans for actual experiments.

[0700] "Experimental design" refers to a plan for the methods, procedures, parameter settings, etc. required to conduct scientific verification.

[0701] "Learning materials and resources" are the educational materials and reference materials that users need to improve their skills and acquire knowledge.

[0702] A "terminal" is a device, such as a computer or smartphone, that a user uses to input and receive information.

[0703] A "request" refers to input sent by a user to a server via a terminal requesting specific information or assistance.

[0704] A "generative AI model" is an artificial intelligence model for natural language processing and data generation that is used to generate information based on user prompts.

[0705] A "prompt sentence" is an instruction sentence entered into a generative AI model to prompt it to generate specific information.

[0706] MODE FOR CARRYING OUT THE INVENTION

[0707] This invention is a comprehensive support system that allows researchers and developers to efficiently gather information, develop ideas, and verify hypotheses. This system provides optimal information and support in response to user requests through collaboration between the server and terminals.

[0708] 1. Configuring the environment

[0709] The server collects the latest technical information and stores it in a database. It also uses a natural language processing module, a data search module, and a generative AI model to analyze user input and provide relevant information. The terminal is a device through which users input and retrieve information, and communicates with the server via a browser or dedicated application.

[0710] The servers use cloud infrastructure with high-performance processors and large storage capacity for hardware, while the terminals are ordinary computers or smartphones.

[0711] The software uses various open source libraries and tools, such as "spaCy" for natural language processing, "BeautifulSoup" for data collection, and "GPT-4" for generative AI models.

[0712] 2. Collecting technical information

[0713] The server periodically collects the latest technical information from external reliable sources (e.g., academic paper databases, specialized websites), and organizes and stores the collected information in a database.

[0714] Examples:

[0715] If a user searches for "latest machine learning algorithms," the server collects relevant information from internal databases and external sources and provides it to the user.

[0716] 3. Promoting ideas and projects

[0717] When a user inputs information about an idea or project through a device, the server uses a natural language processing module to analyze it and convert it into structured data, which is then used to search a database of past projects and generate related information and proposals.

[0718] Examples:

[0719] When a user types in "I'd like some ideas for new image recognition methods," the server searches for past related projects and presents suggestions to the user.

[0720] 4. Hypothesis Verification and Experimental Design

[0721] When a user inputs a hypothesis or experimental idea, the server analyzes it, suggests appropriate experimental designs and parameter settings, evaluates the validity of the hypothesis, and provides feedback.

[0722] Examples:

[0723] When a user inputs, "I would like to validate a new model on a specific dataset," the server suggests appropriate validation methods and parameter settings and displays feedback.

[0724] 5. Providing learning materials and resources

[0725] Upon user request, the server retrieves the necessary learning materials and resources from internal databases and external repositories and provides them to the user.

[0726] Examples:

[0727] When a user requests "I want the latest papers on deep learning," the server searches for relevant papers and provides them to the user.

[0728] Prompt Sentence Examples

[0729] "Tell me about the latest machine learning algorithms."

[0730] "I'd like some ideas for new image recognition methods."

[0731] "I want to validate my new model on a specific dataset."

[0732] "I want the latest paper on deep learning."

[0733] In this way, the present invention realizes a system that provides efficient information and support to researchers and developers.

[0734] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0735] Step 1:

[0736] The user inputs information into the terminal. For example, they type, "Please tell me about the latest machine learning algorithms." The user's input is sent to the server by pressing the execute button.

[0737] Input: The text entered by the user.

[0738] Output: Request from the device to the server.

[0739] Specific behavior:

[0740] The terminal retrieves the text entered by the user into the form and sends an HTTP POST request to the server.

[0741] Step 2:

[0742] The server parses the received request, which contains the text entered by the user, and uses a natural language processing module to parse it.

[0743] Input: The request the user sent to the server.

[0744] Output: Structured data parsed by the natural language processing module.

[0745] Specific behavior:

[0746] The server uses natural language processing libraries such as spaCy to analyze the user's input text and extract keywords and contextual information, which is then converted into structured data.

[0747] Step 3:

[0748] The server uses the structured data to generate appropriate database queries to retrieve relevant information from internal databases or external sources.

[0749] Input: Structured data.

[0750] Output: The retrieved technical information.

[0751] Specific behavior:

[0752] The server generates SQL queries or API requests to retrieve relevant technical information from databases or external sources (e.g., academic paper databases).

[0753] Step 4:

[0754] The server organizes the search results, converts them into a format useful to the user, and uses generative AI models to generate information based on prompts (if needed).

[0755] Input: The retrieved technical information and the prompt statement.

[0756] Output: The organized technical information or information generated.

[0757] Specific behavior:

[0758] The server organizes the information it receives and, if necessary, uses a generative AI model (e.g., GPT-4) to generate information based on the user's prompt.

[0759] Step 5:

[0760] The server transmits the data to the terminal to provide the organized or generated information to the user.

[0761] Input: Organized technical information or generated information.

[0762] Output: Information provided to the user.

[0763] Specific behavior:

[0764] The server sends the organized information to the terminal in an appropriate format, such as JSON, and the terminal parses it and displays it to the user.

[0765] In this way, this system realizes a mechanism for efficiently collecting, analyzing, and providing information in response to user requests through a series of processing steps.

[0766] (Application example 1)

[0767] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0768] Researchers and developers are required to collect the latest technological information, discover and promote their own ideas, and effectively verify hypotheses and design experiments. However, it is difficult to efficiently obtain the necessary data from vast amounts of information and to optimally design experiments and set parameters. Furthermore, accessing learning materials and resources is time-consuming, and the latest information is constantly being updated, making it difficult to obtain the necessary information.

[0769] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0770] In this invention, the server includes means for collecting the latest technical information, means for analyzing input from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for generating and displaying advice related to projects based on user input, means for proposing ideas and experimental designs based on input information using a generative AI model, and means for automating access to the latest technologies and learning materials using prompt sentences. This enables researchers and developers to efficiently and quickly obtain the information and resources they need, improving the efficiency of research and development work.

[0771] "Means for collecting the latest technological information" refers to means for regularly collecting information on the latest technologies from reliable databases and external information sources and storing it in a database.

[0772] The "means for analyzing inputs from users regarding ideas and projects" refers to a means for analyzing information regarding ideas and projects input by users via their terminals and transmitting the information to a server as structured data.

[0773] "Means for searching relevant past projects and ideas and generating advice and solutions" means means for searching a database of past projects and generating relevant advice and solutions based on user input.

[0774] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to means for analyzing hypotheses and experimental ideas submitted by users and proposing optimal experimental designs and parameter settings based on those.

[0775] "Means for providing specific learning materials and resources" refers to means for searching for and providing the necessary learning materials and resources from databases and external repositories in response to a user's request.

[0776] The "means for generating and displaying advice related to a project based on user input" refers to means for analyzing project-related information input by a user and generating and displaying related advice.

[0777] "Means for using a generative AI model to propose ideas and experimental designs based on input information" refers to means for proposing new ideas and experimental designs using an artificial intelligence model generated based on input information.

[0778] "Means for automating access to the latest technology and learning materials using prompt sentences" means means for automatically accessing the latest technology and necessary learning materials using specific prompt sentences and providing the results to the user.

[0779] This invention is a system that helps researchers and developers efficiently collect the latest technical information, discover and promote ideas, verify hypotheses, and provide learning materials and resources. This system is composed of a server, terminals, and user interactions.

[0780] System configuration

[0781] The system consists of the following main modules:

[0782] A module that collects the latest technical information

[0783] A module that analyzes idea and project inputs

[0784] A module that searches past projects and ideas and generates advice

[0785] A module that analyzes hypotheses and experimental ideas and proposes experimental designs and parameter settings

[0786] Modules that provide learning materials and resources

[0787] A module that displays project-related advice based on user input.

[0788] A module that proposes ideas and experimental designs using generative AI models

[0789] A module that automates access to the latest technology and learning materials using prompts

[0790] Program processing overview

[0791] The server periodically obtains the latest technical information from reliable external sources and stores the information in a database. In response to a user's request, the server searches this database and provides the latest technical information.

[0792] When a user inputs a new project or idea through their device, the information is analyzed by a natural language processing module and sent as structured data to the server, which then searches for relevant information in a database of past projects and provides the user with the best advice and solutions.

[0793] Similarly, for hypotheses and experimental ideas, the system receives user input and suggests appropriate experimental designs and parameter settings, and uses generative AI models to suggest new ideas and experimental designs based on the information entered by the user.

[0794] Regarding learning materials and resources, upon user request, the system searches for the necessary materials from databases and external repositories and provides them to the user, improving user efficiency by automating access to specific technical information and papers, especially using prompts.

[0795] Hardware and software used

[0796] Hardware: Servers, devices (PCs, smartphones, etc.)

[0797] Software: Flask (web framework), natural language processing modules (e.g., spaCy, NLTK), database management systems (e.g., PostgreSQL)

[0798] Specific examples

[0799] For example, if a user searches for "latest machine learning algorithms," they might use the following prompt:

[0800] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[0801] This is a procedure by which the server retrieves relevant technical information from a database and provides it to the user.

[0802] Also, if the user wants ideas for new image recognition techniques, they can use the following prompt:

[0803] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[0804] This prompt will cause the server to use a generative AI model to search a database of relevant projects and provide appropriate advice to the user.

[0805] As described above, the present invention is a multi-functional system for efficiently collecting the latest technical information and supporting research and development.

[0806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0807] Step 1:

[0808] The user sends a request through the terminal, for example, by entering a prompt to get information about the latest machine learning algorithms:

[0809] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[0810] This request is sent to the server.

[0811] Step 2:

[0812] The server receives the request and analyzes the specified query. It analyzes the input query (e.g., "latest machine learning algorithms") and prepares to perform a database search based on the query. Specifically, it searches for related technical information using the query as a key.

[0813] Step 3:

[0814] The server searches the database for relevant technical information. It uses a database management system (e.g., PostgreSQL) to find the latest technical information that corresponds to the query. It retrieves the search results and formats the data as needed.

[0815] Step 4:

[0816] The server returns the search results to the user. The searched technical information is sent to the user as structured data in JSON format, allowing the user to view the requested technical information on their device.

[0817] Step 5:

[0818] A user submits input about a new idea or project. For example, a user might type "I'm looking for ideas for new image recognition methods":

[0819] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[0820] This request is sent to the server.

[0821] Step 6:

[0822] The server receives the user's input and parses it using a natural language processing module. The input text ("I'd like some ideas for new image recognition methods") is parsed and converted into structured data.

[0823] Step 7:

[0824] The server uses a generative AI model to generate ideas and advice based on the user's input. Using the analyzed data, the generative AI model suggests appropriate ideas and experimental designs.

[0825] Step 8:

[0826] The server returns the generated ideas and advice to the user. The advice generated by the generative AI model is sent to the user as structured data in JSON format, allowing the user to check the requested ideas and advice on their device.

[0827] Step 9:

[0828] A user inputs a hypothesis or experiment idea and sends it to the server. For example, the user might input, "I want to test a new model on a specific dataset." The server receives this request.

[0829] Step 10:

[0830] The server analyzes the user's hypotheses and experiment ideas, proposes appropriate experimental designs and parameter settings, and uses a natural language processing module to analyze the input and search for appropriate experimental designs and parameter settings from past projects and databases.

[0831] Step 11:

[0832] The server returns the proposed experimental design and parameter settings to the user. The analysis results are sent to the user as structured data in JSON format, allowing the user to check the proposed experimental design and parameter settings on their device.

[0833] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0834] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0835] Function 1: Providing the latest technical information

[0836] The server periodically collects the latest technical information from a technical information database and external reliable information sources, and stores the collected information in the database.

[0837] When a user requests the "latest technical information," the device sends an HTTP request to the server.

[0838] The server retrieves the collected technical information and provides it to the user.

[0839] Examples:

[0840] When a user searches for "latest machine learning algorithms" through their device, the server provides relevant technical information and displays it on the device.

[0841] Function 2: Idea generation and project promotion

[0842] Users input ideas and project-related issues into the device.

[0843] The terminal analyzes the input information using a natural language processing (NLP) module and sends it to the server as structured data.

[0844] The server searches past projects and ideas and generates relevant advice and solutions.

[0845] Examples:

[0846] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[0847] Function 3: Support for hypothesis testing and experimental design

[0848] Users input hypotheses and experiment ideas into the terminal.

[0849] The terminal transmits the input hypotheses and experimental ideas to the server as structured data.

[0850] The server analyzes the hypothesis and suggests appropriate experimental designs and parameter settings.

[0851] Examples:

[0852] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[0853] Function 4: Providing learning materials and resources

[0854] The user types a request for specific learning materials or resources into the device.

[0855] The terminal sends a request to the server.

[0856] The server retrieves the required information from databases and external repositories and provides it to the user.

[0857] Examples:

[0858] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0859] Feature 5: Adding an emotion engine

[0860] When analyzing user input with the natural language processing module, an emotion engine is also used.

[0861] The emotion engine recognizes emotions from user input and reflects that information in the analysis results.

[0862] The server tailors advice and solutions based on emotions, providing more personalized support.

[0863] Examples:

[0864] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[0865] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[0866] The processing flow will be explained below.

[0867] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0868] Function 1: Providing the latest technical information

[0869] Processing Steps:

[0870] Step 1:

[0871] A user types "latest tech info" into the search bar on their device.

[0872] Step 2:

[0873] The terminal sends this input to the server as an HTTP request.

[0874] Step 3:

[0875] The server queries a database of technical information to obtain the latest information.

[0876] Step 4:

[0877] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[0878] Step 5:

[0879] The server updates the database with the latest information (if necessary).

[0880] Step 6:

[0881] The server formats the latest acquired technical information and returns it to the terminal.

[0882] Step 7:

[0883] The terminal receives the data from the server and displays it to the user.

[0884] Function 2: Idea generation and project promotion

[0885] Processing Steps:

[0886] Step 1:

[0887] Users input their ideas and project-related issues into the terminal.

[0888] Step 2:

[0889] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[0890] Step 3:

[0891] The server searches a database for similar past projects and ideas.

[0892] Step 4:

[0893] The server uses an internal analytics engine to generate relevant solutions and advice.

[0894] Step 5:

[0895] The server returns the search results and generated advice to the terminal.

[0896] Step 6:

[0897] The terminal displays the response from the server to the user.

[0898] Function 3: Support for hypothesis testing and experimental design

[0899] Processing Steps:

[0900] Step 1:

[0901] The user inputs hypotheses and experiment ideas into the terminal.

[0902] Step 2:

[0903] The terminal sends this input as structured data to the server.

[0904] Step 3:

[0905] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[0906] Step 4:

[0907] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[0908] Step 5:

[0909] The server performs hypothesis validation and generates feedback.

[0910] Step 6:

[0911] The server returns the analysis results and suggestions to the device.

[0912] Step 7:

[0913] The terminal displays feedback and suggestions from the server to the user.

[0914] Function 4: Providing learning materials and resources

[0915] Processing Steps:

[0916] Step 1:

[0917] A user inputs a request for a particular learning material or resource into a terminal.

[0918] Step 2:

[0919] The terminal sends this request to the server.

[0920] Step 3:

[0921] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[0922] Step 4:

[0923] The server also searches external repositories and databases to retrieve the required resources.

[0924] Step 5:

[0925] The server organizes the acquired information and returns it to the terminal.

[0926] Step 6:

[0927] The terminal displays the learning materials and resources provided by the server to the user.

[0928] Feature 5: Adding an emotion engine

[0929] Processing Steps:

[0930] Step 1:

[0931] The user expresses emotions through text or voice data entered into the terminal.

[0932] Step 2:

[0933] The device passes the user's input to a natural language processing module and an emotion engine to analyze emotions.

[0934] Step 3:

[0935] The emotion engine recognizes the user's emotions and sends that information to the server as structured data.

[0936] Step 4:

[0937] The server receives the user's input, including the emotion data, and performs the necessary analysis.

[0938] Step 5:

[0939] The server tailors the advice and solutions it provides based on the perceived sentiment.

[0940] Step 6:

[0941] The server generates suggestions and feedback based on the emotion and sends them to the device.

[0942] Step 7:

[0943] The terminal displays feedback to the user according to the emotion from the server.

[0944] Examples:

[0945] When a user types, "I'm feeling anxious about this project," the emotion engine recognizes the user's anxiety and the server suggests advice and resources to provide reassurance.

[0946] Example 2

[0947] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0948] Conventional R&D support systems have limitations in collecting technical information and proposing ideas, and lack personalized support based on the user's emotions. In particular, it is difficult to provide advice and solutions that take the user's emotions into consideration, and there has been a demand for a means to reduce the psychological burden on users in R&D work.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0950] In this invention, the server includes means for collecting the latest technical information, means for analyzing inputs from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experiment ideas and proposing appropriate experiment designs and parameter settings, means for providing specific learning materials and resources, means for recognizing the user's emotions and reflecting that information in the analysis results, and means for adjusting advice and solutions based on the emotions and providing personalized support. This makes it possible to provide personalized support that takes the user's emotions into consideration, thereby improving the efficiency of research and development work and reducing psychological burden.

[0951] "Means for collecting the latest technical information" refers to the technology for regularly collecting the latest technical information from reliable external information sources and databases, and storing and managing it.

[0952] The "means for analyzing inputs regarding ideas and projects from users" refers to a technology that uses natural language processing technology to analyze ideas and project-related information input by users and extract necessary information.

[0953] "Means for searching for relevant past projects and ideas and generating advice and solutions" refers to technology for searching for past projects and ideas in a database based on analyzed user input and generating appropriate advice and solutions.

[0954] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to technology for analyzing hypotheses and experimental ideas proposed by users and setting appropriate experimental designs and parameters based on them.

[0955] "Means for providing specific learning materials and resources" refers to the technology used to search for and provide the learning materials and resources requested by the user from databases or external repositories.

[0956] "Means for recognizing user emotions and reflecting that information in the analysis results" refers to technology that analyzes emotions from user input and reflects that emotional information in the analysis results and suggestions.

[0957] "Means for tailoring advice and solutions based on emotions and providing personalized support" refers to technology for tailoring advice and solutions based on the recognized emotions of a user and providing personalized support.

[0958] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[0959] Hardware and software used

[0960] The following hardware and software are used to implement this system: A general cloud server (e.g., Amazon Web Services or Google Cloud Platform) is used as the server, and MongoDB is used to store data. Computer devices such as PCs, smartphones, and tablets are used as terminals. Software such as Python, Django, SpaCy, TextBlob, SciPy, Pandas, and ElasticSearch are used.

[0961] Function 1: Providing the latest technical information

[0962] The server periodically collects the latest technical information from technical information databases and external reliable sources (such as ArXiv and IEEE) and stores it in a database such as MongoDB. When a user requests the "latest technical information" through their device, the device sends an HTTP request to the server. The server searches the collected technical information and returns the results to the device. The device then displays the search results to the user.

[0963] Examples:

[0964] When a user searches for "latest machine learning algorithms," the server provides relevant information and displays it on the device.

[0965] Example prompt sentence:

[0966] "Please provide information on the latest machine learning algorithms."

[0967] Function 2: Idea generation and project promotion

[0968] When a user inputs an idea or project-related issue into the device, the device analyzes it using a natural language processing (NLP) module (e.g., SpaCy) and sends it to the server as structured data. The server then searches past projects and ideas using ElasticSearch, generates relevant advice and solutions, and returns them to the device. The device then displays the advice to the user.

[0969] Examples:

[0970] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[0971] Example prompt sentence:

[0972] "Please tell me your ideas for new image recognition methods."

[0973] Function 3: Support for hypothesis testing and experimental design

[0974] When a user inputs a hypothesis or experiment idea into the terminal, the terminal sends it as structured data to the server, which uses libraries such as SciPy and Pandas to analyze the hypothesis and propose appropriate experimental designs and parameter settings. The proposals are then returned to the terminal and displayed to the user.

[0975] Examples:

[0976] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[0977] Example prompt sentence:

[0978] "How do I validate a new model on a specific dataset?"

[0979] Function 4: Providing learning materials and resources

[0980] When a user requests a specific learning material or resource (e.g., a recent paper or tutorial), the device sends it to the server, which retrieves the required information from a database or external repository and returns it to the device, which then displays the search results to the user.

[0981] Examples:

[0982] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[0983] Example prompt sentence:

[0984] "Please provide the latest papers on deep learning."

[0985] Feature 5: Adding an emotion engine

[0986] When analyzing user input using the natural language processing module, an emotion engine (e.g., TextBlob) is also used to obtain an emotion score. The device then sends the analysis data, including the emotion score, to the server. The server then adjusts advice and solutions based on the emotion, providing personalized support. The adjusted advice is then returned to the device and displayed to the user.

[0987] Examples:

[0988] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[0989] Example prompt sentence:

[0990] "I'm feeling anxious about this project. Can you give me some advice?"

[0991] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[0992] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0993] Function 1: Providing the latest technical information

[0994] Step 1:

[0995] The server periodically collects the latest technical information from technical information databases and external reliable sources. Specifically, the server runs scraping scripts to retrieve data from, for example, academic paper repositories. The collected data is then stored in MongoDB.

[0996] Input: URL or API key of external source

[0997] Output: Latest technical information data

[0998] Step 2:

[0999] When a user searches for the latest technological information, they enter the search keyword into their device. Specifically, the user enters "latest machine learning algorithms" into the browser search form.

[1000] Input: Search keyword

[1001] Output: Search request

[1002] Step 3:

[1003] The device sends the user's request to the server. Specifically, the device sends an HTTP GET request to the server.

[1004] Input: Search request

[1005] Output: HTTP request

[1006] Step 4:

[1007] The server searches for information based on the search keywords from the technical information database and returns the results to the terminal. Specifically, the server searches for information from MongoDB using Django view functions and returns the search results to the terminal in JSON format.

[1008] Input: Search request

[1009] Output: Search result data

[1010] Step 5:

[1011] The device displays the search results to the user. Specifically, the device uses JavaScript to display the search results in HTML.

[1012] Input: Search result data

[1013] Output: Displayed technical information

[1014] Function 2: Idea generation and project promotion

[1015] Step 1:

[1016] The user inputs ideas and project-related issues into the device. Specifically, the user enters "I'm looking for ideas for new image recognition methods" into the browser input form.

[1017] Input: Text about your idea or assignment

[1018] Output: User input data

[1019] Step 2:

[1020] The device analyzes the input information using a natural language processing (NLP) module. Specifically, the device uses SpaCy to analyze user input and extract keywords and key phrases.

[1021] Input: User-entered data

[1022] Output: Parsed data

[1023] Step 3:

[1024] The device converts the parsed data into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1025] Input: Parsed data

[1026] Output: Structured data (JSON)

[1027] Step 4:

[1028] The server searches past projects and ideas, generates relevant advice and solutions, and uses ElasticSearch to search the database, collects relevant information, and returns the generated advice in JSON format to the device.

[1029] Input: Structured data (JSON)

[1030] Output: Advice and solution data

[1031] Step 5:

[1032] The device displays advice and solutions to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[1033] Input: Advice and solution data

[1034] Output: The displayed advice or solution

[1035] Function 3: Support for hypothesis testing and experimental design

[1036] Step 1:

[1037] The user inputs hypotheses and experimental ideas into the device, and specifically, the user writes in the notebook-style UI that they want to validate a new model on a specific dataset.

[1038] Input: Hypothesis or experimental idea text

[1039] Output: User input data

[1040] Step 2:

[1041] The device converts the input information into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1042] Input: User-entered data

[1043] Output: Structured data (JSON)

[1044] Step 3:

[1045] The server analyzes the hypotheses and proposes appropriate experimental designs and parameter settings. Specifically, the server analyzes the hypotheses using SciPy and Pandas, proposes experimental designs, and returns the proposed designs to the terminal in JSON format.

[1046] Input: Structured data (JSON)

[1047] Output: Experimental design proposal data

[1048] Step 4:

[1049] The device displays the suggestions to the user. Specifically, the device uses JavaScript to display the suggestions in HTML.

[1050] Input: Experimental design proposal data

[1051] Output: Experimental design proposal displayed

[1052] Function 4: Providing learning materials and resources

[1053] Step 1:

[1054] The user inputs specific learning materials and resources into the device. Specifically, the user types "I want the latest papers on deep learning" into a browser form.

[1055] Input: Text of specific learning materials or resources

[1056] Output: User input data

[1057] Step 2:

[1058] The device sends a request to the server. Specifically, the device sends data to the server using an HTTP GET request.

[1059] Input: User-entered data

[1060] Output: HTTP request

[1061] Step 3:

[1062] The server searches for relevant learning materials and resources from databases and external repositories and returns the results to the device. Specifically, the server searches for materials from GitHub, ArXiv, etc. based on the search keywords and returns them in JSON format to the device.

[1063] Input: HTTP request

[1064] Output: Learning materials and resource data

[1065] Step 4:

[1066] The device displays the search results to the user. Specifically, the device uses JavaScript to display the results in HTML.

[1067] Input: Study materials and resource data

[1068] Output: Displayed learning materials and resources

[1069] Feature 5: Adding an emotion engine

[1070] Step 1:

[1071] The user types input into the terminal. Specifically, the user types "I'm worried about this project" into a browser form.

[1072] Input: Sentiment text

[1073] Output: User input data

[1074] Step 2:

[1075] The device analyzes the input information using a natural language processing module (TextBlob) and obtains an emotion score. Specifically, the device calculates the emotion score using the TextBlob library.

[1076] Input: User-entered data

[1077] Output: Sentiment score

[1078] Step 3:

[1079] The device converts the data containing emotion information into JSON format and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1080] Input: Sentiment score

[1081] Output: JSON data containing emotion information

[1082] Step 4:

[1083] The server then adjusts advice and solutions based on the emotion, providing personalized support. Specifically, the server analyzes the emotion score, generates personalized advice and resources, and returns them to the device in JSON format.

[1084] Input: JSON data containing emotion information

[1085] Output: personalized advice and solution data

[1086] Step 5:

[1087] The device then displays the adjusted advice to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[1088] Input: personalized advice and solution data

[1089] Output: The displayed advice or solution

[1090] (Application example 2)

[1091] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1092] In modern manufacturing environments, there is a need to quickly detect problems and troubles that manufacturing robots face and provide optimal solutions. Furthermore, there is a lack of means to understand the emotions of operators involved in the manufacturing process and provide personalized support. This will improve manufacturing efficiency while reducing operator stress and creating an environment where operators can perform their work with peace of mind.

[1093] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the latest technical information, means for analyzing input related to a user's ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for analyzing and recognizing user emotion data, means for a manufacturing robot to detect problems during the manufacturing process and provide related technical information, and means for providing personalized support based on the user's emotions. This enables the manufacturing robot to quickly detect problems during the manufacturing process and provide optimal solutions, as well as provide personalized support based on the operator's emotions.

[1094] "Latest technological information" refers to information on the latest research results, technological trends, methodologies and tools related to manufacturing and technological development.

[1095] "User idea and project input" refers to new technology ideas and detailed information about ongoing projects that researchers and developers provide to the system.

[1096] "Past projects and ideas" refers to previously conducted research projects and development ideas, as well as data containing their results and findings.

[1097] "Advice and solutions" is information that provides users with specific guidelines or solutions to their problems or challenges.

[1098] "Hypotheses and experimental ideas" are proposals based on experimental plans and thought processes to predict or solve phenomena or problems.

[1099] "Appropriate experimental design and parameter settings" refers to the specific experimental methods and conditions used to verify a hypothesis.

[1100] "Specific learning materials and resources" are digital content such as specific information, tools, or guidelines that users need to learn.

[1101] "User emotional data" refers to emotional information extracted from text or voice input by a user to the system.

[1102] A "manufacturing robot" is a mechanical system that automates manufacturing processes such as product assembly and processing.

[1103] A "problem during the manufacturing process" is an anomaly, error, or factor that reduces efficiency that occurs during the manufacturing process.

[1104] "Means for providing technical information" refers to a method or system for presenting collected technical information to users.

[1105] "Personalized support" is support that aims to provide assistance and advice tailored to each individual situation, based on the user's emotions and specific needs.

[1106] This invention provides an integrated system for increasing the efficiency of manufacturing robots and operators in manufacturing operations. The system consists of the following major components:

[1107] System configuration

[1108] Hardware

[1109] Manufacturing robots: Assemble and process products

[1110] Server: Processes and manages the core of the system

[1111] User device: computer or tablet used by the operator

[1112] software

[1113] Sentiment Engine: Analyze user emotions

[1114] Natural Language Processing (NLP) module: Parsing user input

[1115] Technical information collection module: Collect the latest technical information through web scraping

[1116] Project Advisor module: Search past projects and generate advice

[1117] Experimental Design Module: Propose an experimental design for hypothesis testing

[1118] Data processing and calculation

[1119] Providing the latest technical information

[1120] The server periodically collects the latest technical information from reliable sources using a technical information collection module and stores it in a database. When a user requests the latest technical information through their device, the server receives the HTTP request, searches the database for relevant technical information, and provides it to the user.

[1121] Discovering ideas and promoting projects

[1122] Users input ideas and project-related issues into their devices. The devices use an NLP module to parse the input and send it to the server as structured data. The server then uses a project advisor module to search past projects and ideas, generate relevant advice and solutions, and provide them to the user.

[1123] Support for hypothesis testing and experimental design

[1124] When a user inputs a hypothesis or experiment idea into the device, the device sends the information as structured data to the server, which then analyzes the hypothesis using an experiment design module and proposes an appropriate experimental design and parameter settings.

[1125] Providing learning materials and resources

[1126] When a user requests specific learning materials or resources, the server retrieves the required information from databases or external repositories and provides it to the user.

[1127] Using the Emotion Engine

[1128] The device analyzes the user's input using an NLP module and emotion engine, and sends the structured data and emotion data to the server, which analyzes the emotion data and tailors the provision of advice, solutions, and personalized support based on the user's emotions.

[1129] Specific examples

[1130] For example, if a manufacturing robot detects a defect during a process and the operator inputs, "I'm concerned about this new manufacturing process," the system will use its emotion engine to recognize the emotion "concern." At the same time, the server will launch a technical information collection module, searching for and displaying relevant technical information and past projects.

[1131] Prompt Sentence Examples

[1132] Analyze the sentiment of the following sentence and generate a corresponding assistance message:

[1133] "I'm worried about problems with the new manufacturing process."

[1134] Emotion: Worry

[1135] Assistance message: Don't worry, the following solutions have worked for similar processes in the past:

[1136] The system enables rapid resolution of problems faced by manufacturing robots and provides personalized support to operators, improving the efficiency and quality of manufacturing operations.

[1137] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1138] Step 1:

[1139] The user inputs information about an idea or project into the terminal, using natural language such as "I'm concerned about the new manufacturing process." This input becomes the starting point for the system's processing.

[1140] Step 2:

[1141] The device analyzes the input text using a natural language processing (NLP) module. The NLP module converts the text into structured data and sentiment data. This analysis clarifies the user's intent and sentiment. The input is text data, and the output is structured data and sentiment data.

[1142] Step 3:

[1143] The device sends the analyzed structured data and emotional data to the server. The server analyzes the emotional data and recognizes the user's emotions. Based on this recognition, the system prepares to provide personalized support. The input is the structured data and emotional data, and the output is the emotion recognition result.

[1144] Step 4:

[1145] The server launches a technical information collection module based on the structured data. The technical information collection module scrapes the latest technical information from reliable external sources and searches the information stored in the database. The input is the structured data, and the output is related technical information.

[1146] Step 5:

[1147] The server uses the Project Advisor module to search past projects and ideas, extract relevant advice and solutions from the past project database, and generate them. The input is structured data, and the output is advice and solutions.

[1148] Step 6:

[1149] The server analyzes hypotheses and experimental ideas, and proposes appropriate experimental designs and parameter settings using the experimental design module. The input is structured data of hypotheses and experimental ideas, and the output is experimental designs and parameter settings.

[1150] Step 7:

[1151] The server generates personalized support messages based on the user's emotions. The generated support messages reflect the user's emotional data and are designed to reassure the user. The input is emotional data, and the output is personalized support messages.

[1152] Step 8:

[1153] The server compiles the generated technical information, advice, experimental design, and personalized support messages and sends them to the user's device, which displays this information and makes it easily accessible to the user. The input is multiple generated data, and the output is the integrated support information.

[1154] Step 9:

[1155] The user's device displays technical information, advice, experimental designs, and personalized support messages received from the server. The user reviews this information and decides the next steps in the manufacturing process or project. The input is the integrated support information, and the output is the user's action to confirm the displayed content.

[1156] In this way, the system quickly resolves issues on the shop floor and provides personalized assistance to operators.

[1157] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1158] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1159] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1160] [Third embodiment]

[1161] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1162] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1164] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1165] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1168] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1169] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1171] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1172] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1173] The present invention is a system for solving problems faced by researchers and developers and improving the efficiency of research and development work. This system provides the following main functions:

[1174] 1. Providing the latest technical information

[1175] The server periodically collects the latest technical information from reliable technical information databases and external information sources. The collected information is stored in the database and provided to users as needed. This allows users to always have access to the latest knowledge and apply it to their own research and development.

[1176] Examples:

[1177] When a user searches for "latest machine learning algorithms" through a terminal, the server collects relevant technical information and displays it to the user.

[1178] 2. Discovering ideas and promoting projects

[1179] Users input information about their ideas and projects through their devices. The input information is analyzed by a natural language processing module and sent to the server as structured data. The server then searches past projects and ideas and generates relevant advice and solutions. This allows users to gain new perspectives and ideas, helping to advance their projects.

[1180] Examples:

[1181] When a user types into their device, "I'd like some ideas for new image recognition methods," the server searches for past related projects and displays suggestions to the user.

[1182] 3. Support for hypothesis verification and experimental design

[1183] When a user inputs a hypothesis or experimental idea into a terminal, the server receives and analyzes it. Based on the input hypothesis or experimental idea, the server proposes appropriate experimental design and parameter settings. It also evaluates the validity of the hypothesis and provides feedback. This allows users to design effective experiments and improve the accuracy of verification.

[1184] Examples:

[1185] When a user inputs that they would like to validate a new model on a specific dataset, the server suggests appropriate validation methods and parameter settings and displays them to the user.

[1186] 4. Providing learning materials and resources

[1187] Users can request specific learning materials and resources through their devices. In response to this request, the server searches for the necessary information from databases and external repositories and provides it to the user. This allows users to easily obtain the materials and resources they need and use them to improve their skills and knowledge.

[1188] Examples:

[1189] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[1190] The system works by relying on communication between the server and the terminal, providing appropriate information and support in response to user input. It also combines natural language processing, database search, analysis, and feedback functions to provide comprehensive research and development support. This allows researchers and developers to quickly obtain the information and resources they need and work efficiently.

[1191] The processing flow will be explained below.

[1192] 1. Providing the latest technical information

[1193] Processing Steps:

[1194] Step 1:

[1195] A user types "latest tech info" into the search bar on their device.

[1196] Step 2:

[1197] The terminal sends this input to the server as an HTTP request.

[1198] Step 3:

[1199] The server queries a database of technical information to obtain the latest information.

[1200] Step 4:

[1201] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[1202] Step 5:

[1203] The server updates the database with the latest information (if necessary).

[1204] Step 6:

[1205] The server formats the latest acquired technical information and returns it to the terminal.

[1206] Step 7:

[1207] The terminal receives the data from the server and displays it to the user.

[1208] 2. Discovering ideas and promoting projects

[1209] Processing Steps:

[1210] Step 1:

[1211] Users input their ideas and project-related issues into the terminal.

[1212] Step 2:

[1213] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[1214] Step 3:

[1215] The server searches a database for similar past projects and ideas.

[1216] Step 4:

[1217] The server uses an internal analytics engine to generate relevant solutions and advice.

[1218] Step 5:

[1219] The server returns the search results and generated advice to the terminal.

[1220] Step 6:

[1221] The terminal displays the response from the server to the user.

[1222] 3. Support for hypothesis verification and experimental design

[1223] Processing Steps:

[1224] Step 1:

[1225] The user inputs hypotheses and experiment ideas into the terminal.

[1226] Step 2:

[1227] The terminal sends this input as structured data to the server.

[1228] Step 3:

[1229] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[1230] Step 4:

[1231] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[1232] Step 5:

[1233] The server performs hypothesis validation and generates feedback.

[1234] Step 6:

[1235] The server returns the analysis results and suggestions to the device.

[1236] Step 7:

[1237] The terminal displays feedback and suggestions from the server to the user.

[1238] 4. Providing learning materials and resources

[1239] Processing Steps:

[1240] Step 1:

[1241] A user inputs a request for a particular learning material or resource into a terminal.

[1242] Step 2:

[1243] The terminal sends this request to the server.

[1244] Step 3:

[1245] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[1246] Step 4:

[1247] The server also searches external repositories and databases to retrieve the required resources.

[1248] Step 5:

[1249] The server organizes the acquired information and returns it to the terminal.

[1250] Step 6:

[1251] The terminal displays the learning materials and resources provided by the server to the user.

[1252] Example 1

[1253] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1254] In modern research and development, researchers and developers are required to efficiently gather the latest information from a vast amount of technical information and to quickly and effectively verify their own ideas and hypotheses. However, processes such as information gathering, idea generation, and experimental design require a great deal of time and effort, which creates a bottleneck in research and development. The purpose of this invention is to solve these problems and dramatically improve the efficiency of research and development.

[1255] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1256] In this invention, the server includes means for collecting the latest technical information and storing it in a database, means for analyzing input from users about ideas and projects and converting them into structured data, means for searching past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for searching for and providing specific learning materials and resources, means for receiving requests from users via their terminals and providing information and data in response, and means for generating information based on prompt statements using a generative AI model. This allows users to quickly collect the latest technical information and efficiently discover ideas and verify hypotheses.

[1257] "Latest technical information" refers to the latest research results and technical reports that are novel and inventive, and is information that is regularly collected from external reliable sources.

[1258] A "database" is an information management system that can efficiently store, manage, and search collected technical information, project data, and resources.

[1259] "Users" refer to researchers and developers who use this system to gather information, generate ideas, and test hypotheses.

[1260] "Input related to ideas and projects" refers to text data that users input into a terminal in natural language about their thoughts and plans regarding new research themes or development projects.

[1261] "Structured Data" means data that has been analyzed by a natural language processing module and converted into a specific format that allows the server to effectively process, search, and create suggestions.

[1262] "Past projects and ideas" is data containing information about previous research and development that can be used to generate new ideas and solutions.

[1263] The "means for generating advice and solutions" is a module that allows the server to generate relevant suggestions and problem-solving methods based on past data and provide them to the user.

[1264] "Hypothesis and experimental ideas" refers to theoretical claims that the user wants to verify and plans for actual experiments.

[1265] "Experimental design" refers to a plan for the methods, procedures, parameter settings, etc. required to conduct scientific verification.

[1266] "Learning materials and resources" are the educational materials and reference materials that users need to improve their skills and acquire knowledge.

[1267] A "terminal" is a device, such as a computer or smartphone, that a user uses to input and receive information.

[1268] A "request" refers to input sent by a user to a server via a terminal requesting specific information or assistance.

[1269] A "generative AI model" is an artificial intelligence model for natural language processing and data generation that is used to generate information based on user prompts.

[1270] A "prompt sentence" is an instruction sentence entered into a generative AI model to prompt it to generate specific information.

[1271] MODE FOR CARRYING OUT THE INVENTION

[1272] This invention is a comprehensive support system that allows researchers and developers to efficiently gather information, develop ideas, and verify hypotheses. This system provides optimal information and support in response to user requests through collaboration between the server and terminals.

[1273] 1. Configuring the environment

[1274] The server collects the latest technical information and stores it in a database. It also uses a natural language processing module, a data search module, and a generative AI model to analyze user input and provide relevant information. The terminal is a device through which users input and retrieve information, and communicates with the server via a browser or dedicated application.

[1275] The servers use cloud infrastructure with high-performance processors and large storage capacity for hardware, while the terminals are ordinary computers or smartphones.

[1276] The software uses various open source libraries and tools, such as "spaCy" for natural language processing, "BeautifulSoup" for data collection, and "GPT-4" for generative AI models.

[1277] 2. Collecting technical information

[1278] The server periodically collects the latest technical information from external reliable sources (e.g., academic paper databases, specialized websites), and organizes and stores the collected information in a database.

[1279] Examples:

[1280] If a user searches for "latest machine learning algorithms," the server collects relevant information from internal databases and external sources and provides it to the user.

[1281] 3. Promoting ideas and projects

[1282] When a user inputs information about an idea or project through a device, the server uses a natural language processing module to analyze it and convert it into structured data, which is then used to search a database of past projects and generate related information and proposals.

[1283] Examples:

[1284] When a user types in "I'd like some ideas for new image recognition methods," the server searches for past related projects and presents suggestions to the user.

[1285] 4. Hypothesis Verification and Experimental Design

[1286] When a user inputs a hypothesis or experimental idea, the server analyzes it, suggests appropriate experimental designs and parameter settings, evaluates the validity of the hypothesis, and provides feedback.

[1287] Examples:

[1288] When a user inputs, "I would like to validate a new model on a specific dataset," the server suggests appropriate validation methods and parameter settings and displays feedback.

[1289] 5. Providing learning materials and resources

[1290] Upon user request, the server retrieves the necessary learning materials and resources from internal databases and external repositories and provides them to the user.

[1291] Examples:

[1292] When a user requests "I want the latest papers on deep learning," the server searches for relevant papers and provides them to the user.

[1293] Prompt Sentence Examples

[1294] "Tell me about the latest machine learning algorithms."

[1295] "I'd like some ideas for new image recognition methods."

[1296] "I want to validate my new model on a specific dataset."

[1297] "I want the latest paper on deep learning."

[1298] In this way, the present invention realizes a system that provides efficient information and support to researchers and developers.

[1299] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1300] Step 1:

[1301] The user inputs information into the terminal. For example, they type, "Please tell me about the latest machine learning algorithms." The user's input is sent to the server by pressing the execute button.

[1302] Input: The text entered by the user.

[1303] Output: Request from the device to the server.

[1304] Specific behavior:

[1305] The terminal retrieves the text entered by the user into the form and sends an HTTP POST request to the server.

[1306] Step 2:

[1307] The server parses the received request, which contains the text entered by the user, and uses a natural language processing module to parse it.

[1308] Input: The request the user sent to the server.

[1309] Output: Structured data parsed by the natural language processing module.

[1310] Specific behavior:

[1311] The server uses natural language processing libraries such as spaCy to analyze the user's input text and extract keywords and contextual information, which is then converted into structured data.

[1312] Step 3:

[1313] The server uses the structured data to generate appropriate database queries to retrieve relevant information from internal databases or external sources.

[1314] Input: Structured data.

[1315] Output: The retrieved technical information.

[1316] Specific behavior:

[1317] The server generates SQL queries or API requests to retrieve relevant technical information from databases or external sources (e.g., academic paper databases).

[1318] Step 4:

[1319] The server organizes the search results, converts them into a format useful to the user, and uses generative AI models to generate information based on prompts (if needed).

[1320] Input: The retrieved technical information and the prompt statement.

[1321] Output: The organized technical information or information generated.

[1322] Specific behavior:

[1323] The server organizes the information it receives and, if necessary, uses a generative AI model (e.g., GPT-4) to generate information based on the user's prompt.

[1324] Step 5:

[1325] The server transmits the data to the terminal to provide the organized or generated information to the user.

[1326] Input: Organized technical information or generated information.

[1327] Output: Information provided to the user.

[1328] Specific behavior:

[1329] The server sends the organized information to the terminal in an appropriate format, such as JSON, and the terminal parses it and displays it to the user.

[1330] In this way, this system realizes a mechanism for efficiently collecting, analyzing, and providing information in response to user requests through a series of processing steps.

[1331] (Application example 1)

[1332] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1333] Researchers and developers are required to collect the latest technological information, discover and promote their own ideas, and effectively verify hypotheses and design experiments. However, it is difficult to efficiently obtain the necessary data from vast amounts of information and to optimally design experiments and set parameters. Furthermore, accessing learning materials and resources is time-consuming, and the latest information is constantly being updated, making it difficult to obtain the necessary information.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1335] In this invention, the server includes means for collecting the latest technical information, means for analyzing input from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for generating and displaying advice related to projects based on user input, means for proposing ideas and experimental designs based on input information using a generative AI model, and means for automating access to the latest technologies and learning materials using prompt sentences. This enables researchers and developers to efficiently and quickly obtain the information and resources they need, improving the efficiency of research and development work.

[1336] "Means for collecting the latest technological information" refers to means for regularly collecting information on the latest technologies from reliable databases and external information sources and storing it in a database.

[1337] The "means for analyzing inputs from users regarding ideas and projects" refers to a means for analyzing information regarding ideas and projects input by users via their terminals and transmitting the information to a server as structured data.

[1338] "Means for searching relevant past projects and ideas and generating advice and solutions" means means for searching a database of past projects and generating relevant advice and solutions based on user input.

[1339] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to means for analyzing hypotheses and experimental ideas submitted by users and proposing optimal experimental designs and parameter settings based on those.

[1340] "Means for providing specific learning materials and resources" refers to means for searching for and providing the necessary learning materials and resources from databases and external repositories in response to a user's request.

[1341] The "means for generating and displaying advice related to a project based on user input" refers to means for analyzing project-related information input by a user and generating and displaying related advice.

[1342] "Means for using a generative AI model to propose ideas and experimental designs based on input information" refers to means for proposing new ideas and experimental designs using an artificial intelligence model generated based on input information.

[1343] "Means for automating access to the latest technology and learning materials using prompt sentences" means means for automatically accessing the latest technology and necessary learning materials using specific prompt sentences and providing the results to the user.

[1344] This invention is a system that helps researchers and developers efficiently collect the latest technical information, discover and promote ideas, verify hypotheses, and provide learning materials and resources. This system is composed of a server, terminals, and user interactions.

[1345] System configuration

[1346] The system consists of the following main modules:

[1347] A module that collects the latest technical information

[1348] A module that analyzes idea and project inputs

[1349] A module that searches past projects and ideas and generates advice

[1350] A module that analyzes hypotheses and experimental ideas and proposes experimental designs and parameter settings

[1351] Modules that provide learning materials and resources

[1352] A module that displays project-related advice based on user input.

[1353] A module that proposes ideas and experimental designs using generative AI models

[1354] A module that automates access to the latest technology and learning materials using prompts

[1355] Program processing overview

[1356] The server periodically obtains the latest technical information from reliable external sources and stores the information in a database. In response to a user's request, the server searches this database and provides the latest technical information.

[1357] When a user inputs a new project or idea through their device, the information is analyzed by a natural language processing module and sent as structured data to the server, which then searches for relevant information in a database of past projects and provides the user with the best advice and solutions.

[1358] Similarly, for hypotheses and experimental ideas, the system receives user input and suggests appropriate experimental designs and parameter settings, and uses generative AI models to suggest new ideas and experimental designs based on the information entered by the user.

[1359] Regarding learning materials and resources, upon user request, the system searches for the necessary materials from databases and external repositories and provides them to the user, improving user efficiency by automating access to specific technical information and papers, especially using prompts.

[1360] Hardware and software used

[1361] Hardware: Servers, devices (PCs, smartphones, etc.)

[1362] Software: Flask (web framework), natural language processing modules (e.g., spaCy, NLTK), database management systems (e.g., PostgreSQL)

[1363] Specific examples

[1364] For example, if a user searches for "latest machine learning algorithms," they might use the following prompt:

[1365] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[1366] This is a procedure by which the server retrieves relevant technical information from a database and provides it to the user.

[1367] Also, if the user wants ideas for new image recognition techniques, they can use the following prompt:

[1368] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[1369] This prompt will cause the server to use a generative AI model to search a database of relevant projects and provide appropriate advice to the user.

[1370] As described above, the present invention is a multi-functional system for efficiently collecting the latest technical information and supporting research and development.

[1371] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1372] Step 1:

[1373] The user sends a request through the terminal, for example, by entering a prompt to get information about the latest machine learning algorithms:

[1374] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[1375] This request is sent to the server.

[1376] Step 2:

[1377] The server receives the request and analyzes the specified query. It analyzes the input query (e.g., "latest machine learning algorithms") and prepares to perform a database search based on the query. Specifically, it searches for related technical information using the query as a key.

[1378] Step 3:

[1379] The server searches the database for relevant technical information. It uses a database management system (e.g., PostgreSQL) to find the latest technical information that corresponds to the query. It retrieves the search results and formats the data as needed.

[1380] Step 4:

[1381] The server returns the search results to the user. The searched technical information is sent to the user as structured data in JSON format, allowing the user to view the requested technical information on their device.

[1382] Step 5:

[1383] A user submits input about a new idea or project. For example, a user might type "I'm looking for ideas for new image recognition methods":

[1384] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[1385] This request is sent to the server.

[1386] Step 6:

[1387] The server receives the user's input and parses it using a natural language processing module. The input text ("I'd like some ideas for new image recognition methods") is parsed and converted into structured data.

[1388] Step 7:

[1389] The server uses a generative AI model to generate ideas and advice based on the user's input. Using the analyzed data, the generative AI model suggests appropriate ideas and experimental designs.

[1390] Step 8:

[1391] The server returns the generated ideas and advice to the user. The advice generated by the generative AI model is sent to the user as structured data in JSON format, allowing the user to check the requested ideas and advice on their device.

[1392] Step 9:

[1393] A user inputs a hypothesis or experiment idea and sends it to the server. For example, the user might input, "I want to test a new model on a specific dataset." The server receives this request.

[1394] Step 10:

[1395] The server analyzes the user's hypotheses and experiment ideas, proposes appropriate experimental designs and parameter settings, and uses a natural language processing module to analyze the input and search for appropriate experimental designs and parameter settings from past projects and databases.

[1396] Step 11:

[1397] The server returns the proposed experimental design and parameter settings to the user. The analysis results are sent to the user as structured data in JSON format, allowing the user to check the proposed experimental design and parameter settings on their device.

[1398] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1399] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[1400] Function 1: Providing the latest technical information

[1401] The server periodically collects the latest technical information from a technical information database and external reliable information sources, and stores the collected information in the database.

[1402] When a user requests the "latest technical information," the device sends an HTTP request to the server.

[1403] The server retrieves the collected technical information and provides it to the user.

[1404] Examples:

[1405] When a user searches for "latest machine learning algorithms" through their device, the server provides relevant technical information and displays it on the device.

[1406] Function 2: Idea generation and project promotion

[1407] Users input ideas and project-related issues into the device.

[1408] The terminal analyzes the input information using a natural language processing (NLP) module and sends it to the server as structured data.

[1409] The server searches past projects and ideas and generates relevant advice and solutions.

[1410] Examples:

[1411] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[1412] Function 3: Support for hypothesis testing and experimental design

[1413] Users input hypotheses and experiment ideas into the terminal.

[1414] The terminal transmits the input hypotheses and experimental ideas to the server as structured data.

[1415] The server analyzes the hypothesis and suggests appropriate experimental designs and parameter settings.

[1416] Examples:

[1417] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[1418] Function 4: Providing learning materials and resources

[1419] The user types a request for specific learning materials or resources into the device.

[1420] The terminal sends a request to the server.

[1421] The server retrieves the required information from databases and external repositories and provides it to the user.

[1422] Examples:

[1423] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[1424] Feature 5: Adding an emotion engine

[1425] When analyzing user input with the natural language processing module, an emotion engine is also used.

[1426] The emotion engine recognizes emotions from user input and reflects that information in the analysis results.

[1427] The server tailors advice and solutions based on emotions, providing more personalized support.

[1428] Examples:

[1429] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[1430] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[1431] The processing flow will be explained below.

[1432] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[1433] Function 1: Providing the latest technical information

[1434] Processing Steps:

[1435] Step 1:

[1436] A user types "latest tech info" into the search bar on their device.

[1437] Step 2:

[1438] The terminal sends this input to the server as an HTTP request.

[1439] Step 3:

[1440] The server queries a database of technical information to obtain the latest information.

[1441] Step 4:

[1442] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[1443] Step 5:

[1444] The server updates the database with the latest information (if necessary).

[1445] Step 6:

[1446] The server formats the latest acquired technical information and returns it to the terminal.

[1447] Step 7:

[1448] The terminal receives the data from the server and displays it to the user.

[1449] Function 2: Idea generation and project promotion

[1450] Processing Steps:

[1451] Step 1:

[1452] Users input their ideas and project-related issues into the terminal.

[1453] Step 2:

[1454] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[1455] Step 3:

[1456] The server searches a database for similar past projects and ideas.

[1457] Step 4:

[1458] The server uses an internal analytics engine to generate relevant solutions and advice.

[1459] Step 5:

[1460] The server returns the search results and generated advice to the terminal.

[1461] Step 6:

[1462] The terminal displays the response from the server to the user.

[1463] Function 3: Support for hypothesis testing and experimental design

[1464] Processing Steps:

[1465] Step 1:

[1466] The user inputs hypotheses and experiment ideas into the terminal.

[1467] Step 2:

[1468] The terminal sends this input as structured data to the server.

[1469] Step 3:

[1470] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[1471] Step 4:

[1472] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[1473] Step 5:

[1474] The server performs hypothesis validation and generates feedback.

[1475] Step 6:

[1476] The server returns the analysis results and suggestions to the device.

[1477] Step 7:

[1478] The terminal displays feedback and suggestions from the server to the user.

[1479] Function 4: Providing learning materials and resources

[1480] Processing Steps:

[1481] Step 1:

[1482] A user inputs a request for a particular learning material or resource into a terminal.

[1483] Step 2:

[1484] The terminal sends this request to the server.

[1485] Step 3:

[1486] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[1487] Step 4:

[1488] The server also searches external repositories and databases to retrieve the required resources.

[1489] Step 5:

[1490] The server organizes the acquired information and returns it to the terminal.

[1491] Step 6:

[1492] The terminal displays the learning materials and resources provided by the server to the user.

[1493] Feature 5: Adding an emotion engine

[1494] Processing Steps:

[1495] Step 1:

[1496] The user expresses emotions through text or voice data entered into the terminal.

[1497] Step 2:

[1498] The device passes the user's input to a natural language processing module and an emotion engine to analyze emotions.

[1499] Step 3:

[1500] The emotion engine recognizes the user's emotions and sends that information to the server as structured data.

[1501] Step 4:

[1502] The server receives the user's input, including the emotion data, and performs the necessary analysis.

[1503] Step 5:

[1504] The server tailors the advice and solutions it provides based on the perceived sentiment.

[1505] Step 6:

[1506] The server generates suggestions and feedback based on the emotion and sends them to the device.

[1507] Step 7:

[1508] The terminal displays feedback to the user according to the emotion from the server.

[1509] Examples:

[1510] When a user types, "I'm feeling anxious about this project," the emotion engine recognizes the user's anxiety and the server suggests advice and resources to provide reassurance.

[1511] Example 2

[1512] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1513] Conventional R&D support systems have limitations in collecting technical information and proposing ideas, and lack personalized support based on the user's emotions. In particular, it is difficult to provide advice and solutions that take the user's emotions into consideration, and there has been a demand for a means to reduce the psychological burden on users in R&D work.

[1514] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1515] In this invention, the server includes means for collecting the latest technical information, means for analyzing inputs from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experiment ideas and proposing appropriate experiment designs and parameter settings, means for providing specific learning materials and resources, means for recognizing the user's emotions and reflecting that information in the analysis results, and means for adjusting advice and solutions based on the emotions and providing personalized support. This makes it possible to provide personalized support that takes the user's emotions into consideration, thereby improving the efficiency of research and development work and reducing psychological burden.

[1516] "Means for collecting the latest technical information" refers to the technology for regularly collecting the latest technical information from reliable external information sources and databases, and storing and managing it.

[1517] The "means for analyzing inputs regarding ideas and projects from users" refers to a technology that uses natural language processing technology to analyze ideas and project-related information input by users and extract necessary information.

[1518] "Means for searching for relevant past projects and ideas and generating advice and solutions" refers to technology for searching for past projects and ideas in a database based on analyzed user input and generating appropriate advice and solutions.

[1519] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to technology for analyzing hypotheses and experimental ideas proposed by users and setting appropriate experimental designs and parameters based on them.

[1520] "Means for providing specific learning materials and resources" refers to the technology used to search for and provide the learning materials and resources requested by the user from databases or external repositories.

[1521] "Means for recognizing user emotions and reflecting that information in the analysis results" refers to technology that analyzes emotions from user input and reflects that emotional information in the analysis results and suggestions.

[1522] "Means for tailoring advice and solutions based on emotions and providing personalized support" refers to technology for tailoring advice and solutions based on the recognized emotions of a user and providing personalized support.

[1523] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[1524] Hardware and software used

[1525] The following hardware and software are used to implement this system: A general cloud server (e.g., Amazon Web Services or Google Cloud Platform) is used as the server, and MongoDB is used to store data. Computer devices such as PCs, smartphones, and tablets are used as terminals. Software such as Python, Django, SpaCy, TextBlob, SciPy, Pandas, and ElasticSearch are used.

[1526] Function 1: Providing the latest technical information

[1527] The server periodically collects the latest technical information from technical information databases and external reliable sources (such as ArXiv and IEEE) and stores it in a database such as MongoDB. When a user requests the "latest technical information" through their device, the device sends an HTTP request to the server. The server searches the collected technical information and returns the results to the device. The device then displays the search results to the user.

[1528] Examples:

[1529] When a user searches for "latest machine learning algorithms," the server provides relevant information and displays it on the device.

[1530] Example prompt sentence:

[1531] "Please provide information on the latest machine learning algorithms."

[1532] Function 2: Idea generation and project promotion

[1533] When a user inputs an idea or project-related issue into the device, the device analyzes it using a natural language processing (NLP) module (e.g., SpaCy) and sends it to the server as structured data. The server then searches past projects and ideas using ElasticSearch, generates relevant advice and solutions, and returns them to the device. The device then displays the advice to the user.

[1534] Examples:

[1535] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[1536] Example prompt sentence:

[1537] "Please tell me your ideas for new image recognition methods."

[1538] Function 3: Support for hypothesis testing and experimental design

[1539] When a user inputs a hypothesis or experiment idea into the terminal, the terminal sends it as structured data to the server, which uses libraries such as SciPy and Pandas to analyze the hypothesis and propose appropriate experimental designs and parameter settings. The proposals are then returned to the terminal and displayed to the user.

[1540] Examples:

[1541] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[1542] Example prompt sentence:

[1543] "How do I validate a new model on a specific dataset?"

[1544] Function 4: Providing learning materials and resources

[1545] When a user requests a specific learning material or resource (e.g., a recent paper or tutorial), the device sends it to the server, which retrieves the required information from a database or external repository and returns it to the device, which then displays the search results to the user.

[1546] Examples:

[1547] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[1548] Example prompt sentence:

[1549] "Please provide the latest papers on deep learning."

[1550] Feature 5: Adding an emotion engine

[1551] When analyzing user input using the natural language processing module, an emotion engine (e.g., TextBlob) is also used to obtain an emotion score. The device then sends the analysis data, including the emotion score, to the server. The server then adjusts advice and solutions based on the emotion, providing personalized support. The adjusted advice is then returned to the device and displayed to the user.

[1552] Examples:

[1553] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[1554] Example prompt sentence:

[1555] "I'm feeling anxious about this project. Can you give me some advice?"

[1556] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[1557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1558] Function 1: Providing the latest technical information

[1559] Step 1:

[1560] The server periodically collects the latest technical information from technical information databases and external reliable sources. Specifically, the server runs scraping scripts to retrieve data from, for example, academic paper repositories. The collected data is then stored in MongoDB.

[1561] Input: URL or API key of external source

[1562] Output: Latest technical information data

[1563] Step 2:

[1564] When a user searches for the latest technological information, they enter the search keyword into their device. Specifically, the user enters "latest machine learning algorithms" into the browser search form.

[1565] Input: Search keyword

[1566] Output: Search request

[1567] Step 3:

[1568] The device sends the user's request to the server. Specifically, the device sends an HTTP GET request to the server.

[1569] Input: Search request

[1570] Output: HTTP request

[1571] Step 4:

[1572] The server searches for information based on the search keywords from the technical information database and returns the results to the terminal. Specifically, the server searches for information from MongoDB using Django view functions and returns the search results to the terminal in JSON format.

[1573] Input: Search request

[1574] Output: Search result data

[1575] Step 5:

[1576] The device displays the search results to the user. Specifically, the device uses JavaScript to display the search results in HTML.

[1577] Input: Search result data

[1578] Output: Displayed technical information

[1579] Function 2: Idea generation and project promotion

[1580] Step 1:

[1581] The user inputs ideas and project-related issues into the device. Specifically, the user enters "I'm looking for ideas for new image recognition methods" into the browser input form.

[1582] Input: Text about your idea or assignment

[1583] Output: User input data

[1584] Step 2:

[1585] The device analyzes the input information using a natural language processing (NLP) module. Specifically, the device uses SpaCy to analyze user input and extract keywords and key phrases.

[1586] Input: User-entered data

[1587] Output: Parsed data

[1588] Step 3:

[1589] The device converts the parsed data into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1590] Input: Parsed data

[1591] Output: Structured data (JSON)

[1592] Step 4:

[1593] The server searches past projects and ideas, generates relevant advice and solutions, and uses ElasticSearch to search the database, collects relevant information, and returns the generated advice in JSON format to the device.

[1594] Input: Structured data (JSON)

[1595] Output: Advice and solution data

[1596] Step 5:

[1597] The device displays advice and solutions to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[1598] Input: Advice and solution data

[1599] Output: The displayed advice or solution

[1600] Function 3: Support for hypothesis testing and experimental design

[1601] Step 1:

[1602] The user inputs hypotheses and experimental ideas into the device, and specifically, the user writes in the notebook-style UI that they want to validate a new model on a specific dataset.

[1603] Input: Hypothesis or experimental idea text

[1604] Output: User input data

[1605] Step 2:

[1606] The device converts the input information into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1607] Input: User-entered data

[1608] Output: Structured data (JSON)

[1609] Step 3:

[1610] The server analyzes the hypotheses and proposes appropriate experimental designs and parameter settings. Specifically, the server analyzes the hypotheses using SciPy and Pandas, proposes experimental designs, and returns the proposed designs to the terminal in JSON format.

[1611] Input: Structured data (JSON)

[1612] Output: Experimental design proposal data

[1613] Step 4:

[1614] The device displays the suggestions to the user. Specifically, the device uses JavaScript to display the suggestions in HTML.

[1615] Input: Experimental design proposal data

[1616] Output: Experimental design proposal displayed

[1617] Function 4: Providing learning materials and resources

[1618] Step 1:

[1619] The user inputs specific learning materials and resources into the device. Specifically, the user types "I want the latest papers on deep learning" into a browser form.

[1620] Input: Text of specific learning materials or resources

[1621] Output: User input data

[1622] Step 2:

[1623] The device sends a request to the server. Specifically, the device sends data to the server using an HTTP GET request.

[1624] Input: User-entered data

[1625] Output: HTTP request

[1626] Step 3:

[1627] The server searches for relevant learning materials and resources from databases and external repositories and returns the results to the device. Specifically, the server searches for materials from GitHub, ArXiv, etc. based on the search keywords and returns them in JSON format to the device.

[1628] Input: HTTP request

[1629] Output: Learning materials and resource data

[1630] Step 4:

[1631] The device displays the search results to the user. Specifically, the device uses JavaScript to display the results in HTML.

[1632] Input: Study materials and resource data

[1633] Output: Displayed learning materials and resources

[1634] Feature 5: Adding an emotion engine

[1635] Step 1:

[1636] The user types input into the terminal. Specifically, the user types "I'm worried about this project" into a browser form.

[1637] Input: Sentiment text

[1638] Output: User input data

[1639] Step 2:

[1640] The device analyzes the input information using a natural language processing module (TextBlob) and obtains an emotion score. Specifically, the device calculates the emotion score using the TextBlob library.

[1641] Input: User-entered data

[1642] Output: Sentiment score

[1643] Step 3:

[1644] The device converts the data containing emotion information into JSON format and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[1645] Input: Sentiment score

[1646] Output: JSON data containing emotion information

[1647] Step 4:

[1648] The server then adjusts advice and solutions based on the emotion, providing personalized support. Specifically, the server analyzes the emotion score, generates personalized advice and resources, and returns them to the device in JSON format.

[1649] Input: JSON data containing emotion information

[1650] Output: personalized advice and solution data

[1651] Step 5:

[1652] The device then displays the adjusted advice to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[1653] Input: personalized advice and solution data

[1654] Output: The displayed advice or solution

[1655] (Application example 2)

[1656] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1657] In modern manufacturing environments, there is a need to quickly detect problems and troubles that manufacturing robots face and provide optimal solutions. Furthermore, there is a lack of means to understand the emotions of operators involved in the manufacturing process and provide personalized support. This will improve manufacturing efficiency while reducing operator stress and creating an environment where operators can perform their work with peace of mind.

[1658] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the latest technical information, means for analyzing input related to a user's ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for analyzing and recognizing user emotion data, means for a manufacturing robot to detect problems during the manufacturing process and provide related technical information, and means for providing personalized support based on the user's emotions. This enables the manufacturing robot to quickly detect problems during the manufacturing process and provide optimal solutions, as well as provide personalized support based on the operator's emotions.

[1659] "Latest technological information" refers to information on the latest research results, technological trends, methodologies and tools related to manufacturing and technological development.

[1660] "User idea and project input" refers to new technology ideas and detailed information about ongoing projects that researchers and developers provide to the system.

[1661] "Past projects and ideas" refers to previously conducted research projects and development ideas, as well as data containing their results and findings.

[1662] "Advice and solutions" is information that provides users with specific guidelines or solutions to their problems or challenges.

[1663] "Hypotheses and experimental ideas" are proposals based on experimental plans and thought processes to predict or solve phenomena or problems.

[1664] "Appropriate experimental design and parameter settings" refers to the specific experimental methods and conditions used to verify a hypothesis.

[1665] "Specific learning materials and resources" are digital content such as specific information, tools, or guidelines that users need to learn.

[1666] "User emotional data" refers to emotional information extracted from text or voice input by a user to the system.

[1667] A "manufacturing robot" is a mechanical system that automates manufacturing processes such as product assembly and processing.

[1668] A "problem during the manufacturing process" is an anomaly, error, or factor that reduces efficiency that occurs during the manufacturing process.

[1669] "Means for providing technical information" refers to a method or system for presenting collected technical information to users.

[1670] "Personalized support" is support that aims to provide assistance and advice tailored to each individual situation, based on the user's emotions and specific needs.

[1671] This invention provides an integrated system for increasing the efficiency of manufacturing robots and operators in manufacturing operations. The system consists of the following major components:

[1672] System configuration

[1673] Hardware

[1674] Manufacturing robots: Assemble and process products

[1675] Server: Processes and manages the core of the system

[1676] User device: computer or tablet used by the operator

[1677] software

[1678] Sentiment Engine: Analyze user emotions

[1679] Natural Language Processing (NLP) module: Parsing user input

[1680] Technical information collection module: Collect the latest technical information through web scraping

[1681] Project Advisor module: Search past projects and generate advice

[1682] Experimental Design Module: Propose an experimental design for hypothesis testing

[1683] Data processing and calculation

[1684] Providing the latest technical information

[1685] The server periodically collects the latest technical information from reliable sources using a technical information collection module and stores it in a database. When a user requests the latest technical information through their device, the server receives the HTTP request, searches the database for relevant technical information, and provides it to the user.

[1686] Discovering ideas and promoting projects

[1687] Users input ideas and project-related issues into their devices. The devices use an NLP module to parse the input and send it to the server as structured data. The server then uses a project advisor module to search past projects and ideas, generate relevant advice and solutions, and provide them to the user.

[1688] Support for hypothesis testing and experimental design

[1689] When a user inputs a hypothesis or experiment idea into the device, the device sends the information as structured data to the server, which then analyzes the hypothesis using an experiment design module and proposes an appropriate experimental design and parameter settings.

[1690] Providing learning materials and resources

[1691] When a user requests specific learning materials or resources, the server retrieves the required information from databases or external repositories and provides it to the user.

[1692] Using the Emotion Engine

[1693] The device analyzes the user's input using an NLP module and emotion engine, and sends the structured data and emotion data to the server, which analyzes the emotion data and tailors the provision of advice, solutions, and personalized support based on the user's emotions.

[1694] Specific examples

[1695] For example, if a manufacturing robot detects a defect during a process and the operator inputs, "I'm concerned about this new manufacturing process," the system will use its emotion engine to recognize the emotion "concern." At the same time, the server will launch a technical information collection module, searching for and displaying relevant technical information and past projects.

[1696] Prompt Sentence Examples

[1697] Analyze the sentiment of the following sentence and generate a corresponding assistance message:

[1698] "I'm worried about problems with the new manufacturing process."

[1699] Emotion: Worry

[1700] Assistance message: Don't worry, the following solutions have worked for similar processes in the past:

[1701] The system enables rapid resolution of problems faced by manufacturing robots and provides personalized support to operators, improving the efficiency and quality of manufacturing operations.

[1702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1703] Step 1:

[1704] The user inputs information about an idea or project into the terminal, using natural language such as "I'm concerned about the new manufacturing process." This input becomes the starting point for the system's processing.

[1705] Step 2:

[1706] The device analyzes the input text using a natural language processing (NLP) module. The NLP module converts the text into structured data and sentiment data. This analysis clarifies the user's intent and sentiment. The input is text data, and the output is structured data and sentiment data.

[1707] Step 3:

[1708] The device sends the analyzed structured data and emotional data to the server. The server analyzes the emotional data and recognizes the user's emotions. Based on this recognition, the system prepares to provide personalized support. The input is the structured data and emotional data, and the output is the emotion recognition result.

[1709] Step 4:

[1710] The server launches a technical information collection module based on the structured data. The technical information collection module scrapes the latest technical information from reliable external sources and searches the information stored in the database. The input is the structured data, and the output is related technical information.

[1711] Step 5:

[1712] The server uses the Project Advisor module to search past projects and ideas, extract relevant advice and solutions from the past project database, and generate them. The input is structured data, and the output is advice and solutions.

[1713] Step 6:

[1714] The server analyzes hypotheses and experimental ideas, and proposes appropriate experimental designs and parameter settings using the experimental design module. The input is structured data of hypotheses and experimental ideas, and the output is experimental designs and parameter settings.

[1715] Step 7:

[1716] The server generates personalized support messages based on the user's emotions. The generated support messages reflect the user's emotional data and are designed to reassure the user. The input is emotional data, and the output is personalized support messages.

[1717] Step 8:

[1718] The server compiles the generated technical information, advice, experimental design, and personalized support messages and sends them to the user's device, which displays this information and makes it easily accessible to the user. The input is multiple generated data, and the output is the integrated support information.

[1719] Step 9:

[1720] The user's device displays technical information, advice, experimental designs, and personalized support messages received from the server. The user reviews this information and decides the next steps in the manufacturing process or project. The input is the integrated support information, and the output is the user's action to confirm the displayed content.

[1721] In this way, the system quickly resolves issues on the shop floor and provides personalized assistance to operators.

[1722] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1723] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1724] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1725] [Fourth embodiment]

[1726] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1727] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1728] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1729] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1730] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1731] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1732] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1733] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1734] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1735] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1736] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1737] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1738] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1739] The present invention is a system for solving problems faced by researchers and developers and improving the efficiency of research and development work. This system provides the following main functions:

[1740] 1. Providing the latest technical information

[1741] The server periodically collects the latest technical information from reliable technical information databases and external information sources. The collected information is stored in the database and provided to users as needed. This allows users to always have access to the latest knowledge and apply it to their own research and development.

[1742] Examples:

[1743] When a user searches for "latest machine learning algorithms" through a terminal, the server collects relevant technical information and displays it to the user.

[1744] 2. Discovering ideas and promoting projects

[1745] Users input information about their ideas and projects through their devices. The input information is analyzed by a natural language processing module and sent to the server as structured data. The server then searches past projects and ideas and generates relevant advice and solutions. This allows users to gain new perspectives and ideas, helping to advance their projects.

[1746] Examples:

[1747] When a user types into their device, "I'd like some ideas for new image recognition methods," the server searches for past related projects and displays suggestions to the user.

[1748] 3. Support for hypothesis verification and experimental design

[1749] When a user inputs a hypothesis or experimental idea into a terminal, the server receives and analyzes it. Based on the input hypothesis or experimental idea, the server proposes appropriate experimental design and parameter settings. It also evaluates the validity of the hypothesis and provides feedback. This allows users to design effective experiments and improve the accuracy of verification.

[1750] Examples:

[1751] When a user inputs that they would like to validate a new model on a specific dataset, the server suggests appropriate validation methods and parameter settings and displays them to the user.

[1752] 4. Providing learning materials and resources

[1753] Users can request specific learning materials and resources through their devices. In response to this request, the server searches for the necessary information from databases and external repositories and provides it to the user. This allows users to easily obtain the materials and resources they need and use them to improve their skills and knowledge.

[1754] Examples:

[1755] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[1756] The system works by relying on communication between the server and the terminal, providing appropriate information and support in response to user input. It also combines natural language processing, database search, analysis, and feedback functions to provide comprehensive research and development support. This allows researchers and developers to quickly obtain the information and resources they need and work efficiently.

[1757] The processing flow will be explained below.

[1758] 1. Providing the latest technical information

[1759] Processing Steps:

[1760] Step 1:

[1761] A user types "latest tech info" into the search bar on their device.

[1762] Step 2:

[1763] The terminal sends this input to the server as an HTTP request.

[1764] Step 3:

[1765] The server queries a database of technical information to obtain the latest information.

[1766] Step 4:

[1767] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[1768] Step 5:

[1769] The server updates the database with the latest information (if necessary).

[1770] Step 6:

[1771] The server formats the latest acquired technical information and returns it to the terminal.

[1772] Step 7:

[1773] The terminal receives the data from the server and displays it to the user.

[1774] 2. Discovering ideas and promoting projects

[1775] Processing Steps:

[1776] Step 1:

[1777] Users input their ideas and project-related issues into the terminal.

[1778] Step 2:

[1779] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[1780] Step 3:

[1781] The server searches a database for similar past projects and ideas.

[1782] Step 4:

[1783] The server uses an internal analytics engine to generate relevant solutions and advice.

[1784] Step 5:

[1785] The server returns the search results and generated advice to the terminal.

[1786] Step 6:

[1787] The terminal displays the response from the server to the user.

[1788] 3. Support for hypothesis verification and experimental design

[1789] Processing Steps:

[1790] Step 1:

[1791] The user inputs hypotheses and experiment ideas into the terminal.

[1792] Step 2:

[1793] The terminal sends this input as structured data to the server.

[1794] Step 3:

[1795] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[1796] Step 4:

[1797] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[1798] Step 5:

[1799] The server performs hypothesis validation and generates feedback.

[1800] Step 6:

[1801] The server returns the analysis results and suggestions to the device.

[1802] Step 7:

[1803] The terminal displays feedback and suggestions from the server to the user.

[1804] 4. Providing learning materials and resources

[1805] Processing Steps:

[1806] Step 1:

[1807] A user inputs a request for a particular learning material or resource into a terminal.

[1808] Step 2:

[1809] The terminal sends this request to the server.

[1810] Step 3:

[1811] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[1812] Step 4:

[1813] The server also searches external repositories and databases to retrieve the required resources.

[1814] Step 5:

[1815] The server organizes the acquired information and returns it to the terminal.

[1816] Step 6:

[1817] The terminal displays the learning materials and resources provided by the server to the user.

[1818] Example 1

[1819] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1820] In modern research and development, researchers and developers are required to efficiently gather the latest information from a vast amount of technical information and to quickly and effectively verify their own ideas and hypotheses. However, processes such as information gathering, idea generation, and experimental design require a great deal of time and effort, which creates a bottleneck in research and development. The purpose of this invention is to solve these problems and dramatically improve the efficiency of research and development.

[1821] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1822] In this invention, the server includes means for collecting the latest technical information and storing it in a database, means for analyzing input from users about ideas and projects and converting them into structured data, means for searching past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for searching for and providing specific learning materials and resources, means for receiving requests from users via their terminals and providing information and data in response, and means for generating information based on prompt statements using a generative AI model. This allows users to quickly collect the latest technical information and efficiently discover ideas and verify hypotheses.

[1823] "Latest technical information" refers to the latest research results and technical reports that are novel and inventive, and is information that is regularly collected from external reliable sources.

[1824] A "database" is an information management system that can efficiently store, manage, and search collected technical information, project data, and resources.

[1825] "Users" refer to researchers and developers who use this system to gather information, generate ideas, and test hypotheses.

[1826] "Input related to ideas and projects" refers to text data that users input into a terminal in natural language about their thoughts and plans regarding new research themes or development projects.

[1827] "Structured Data" means data that has been analyzed by a natural language processing module and converted into a specific format that allows the server to effectively process, search, and create suggestions.

[1828] "Past projects and ideas" is data containing information about previous research and development that can be used to generate new ideas and solutions.

[1829] The "means for generating advice and solutions" is a module that allows the server to generate relevant suggestions and problem-solving methods based on past data and provide them to the user.

[1830] "Hypothesis and experimental ideas" refers to theoretical claims that the user wants to verify and plans for actual experiments.

[1831] "Experimental design" refers to a plan for the methods, procedures, parameter settings, etc. required to conduct scientific verification.

[1832] "Learning materials and resources" are the educational materials and reference materials that users need to improve their skills and acquire knowledge.

[1833] A "terminal" is a device, such as a computer or smartphone, that a user uses to input and receive information.

[1834] A "request" refers to input sent by a user to a server via a terminal requesting specific information or assistance.

[1835] A "generative AI model" is an artificial intelligence model for natural language processing and data generation that is used to generate information based on user prompts.

[1836] A "prompt sentence" is an instruction sentence entered into a generative AI model to prompt it to generate specific information.

[1837] MODE FOR CARRYING OUT THE INVENTION

[1838] This invention is a comprehensive support system that allows researchers and developers to efficiently gather information, develop ideas, and verify hypotheses. This system provides optimal information and support in response to user requests through collaboration between the server and terminals.

[1839] 1. Configuring the environment

[1840] The server collects the latest technical information and stores it in a database. It also uses a natural language processing module, a data search module, and a generative AI model to analyze user input and provide relevant information. The terminal is a device through which users input and retrieve information, and communicates with the server via a browser or dedicated application.

[1841] The servers use cloud infrastructure with high-performance processors and large storage capacity for hardware, while the terminals are ordinary computers or smartphones.

[1842] The software uses various open source libraries and tools, such as "spaCy" for natural language processing, "BeautifulSoup" for data collection, and "GPT-4" for generative AI models.

[1843] 2. Collecting technical information

[1844] The server periodically collects the latest technical information from external reliable sources (e.g., academic paper databases, specialized websites), and organizes and stores the collected information in a database.

[1845] Examples:

[1846] If a user searches for "latest machine learning algorithms," the server collects relevant information from internal databases and external sources and provides it to the user.

[1847] 3. Promoting ideas and projects

[1848] When a user inputs information about an idea or project through a device, the server uses a natural language processing module to analyze it and convert it into structured data, which is then used to search a database of past projects and generate related information and proposals.

[1849] Examples:

[1850] When a user types in "I'd like some ideas for new image recognition methods," the server searches for past related projects and presents suggestions to the user.

[1851] 4. Hypothesis Verification and Experimental Design

[1852] When a user inputs a hypothesis or experimental idea, the server analyzes it, suggests appropriate experimental designs and parameter settings, evaluates the validity of the hypothesis, and provides feedback.

[1853] Examples:

[1854] When a user inputs, "I would like to validate a new model on a specific dataset," the server suggests appropriate validation methods and parameter settings and displays feedback.

[1855] 5. Providing learning materials and resources

[1856] Upon user request, the server retrieves the necessary learning materials and resources from internal databases and external repositories and provides them to the user.

[1857] Examples:

[1858] When a user requests "I want the latest papers on deep learning," the server searches for relevant papers and provides them to the user.

[1859] Prompt Sentence Examples

[1860] "Tell me about the latest machine learning algorithms."

[1861] "I'd like some ideas for new image recognition methods."

[1862] "I want to validate my new model on a specific dataset."

[1863] "I want the latest paper on deep learning."

[1864] In this way, the present invention realizes a system that provides efficient information and support to researchers and developers.

[1865] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1866] Step 1:

[1867] The user inputs information into the terminal. For example, they type, "Please tell me about the latest machine learning algorithms." The user's input is sent to the server by pressing the execute button.

[1868] Input: The text entered by the user.

[1869] Output: Request from the device to the server.

[1870] Specific behavior:

[1871] The terminal retrieves the text entered by the user into the form and sends an HTTP POST request to the server.

[1872] Step 2:

[1873] The server parses the received request, which contains the text entered by the user, and uses a natural language processing module to parse it.

[1874] Input: The request the user sent to the server.

[1875] Output: Structured data parsed by the natural language processing module.

[1876] Specific behavior:

[1877] The server uses natural language processing libraries such as spaCy to analyze the user's input text and extract keywords and contextual information, which is then converted into structured data.

[1878] Step 3:

[1879] The server uses the structured data to generate appropriate database queries to retrieve relevant information from internal databases or external sources.

[1880] Input: Structured data.

[1881] Output: The retrieved technical information.

[1882] Specific behavior:

[1883] The server generates SQL queries or API requests to retrieve relevant technical information from databases or external sources (e.g., academic paper databases).

[1884] Step 4:

[1885] The server organizes the search results, converts them into a format useful to the user, and uses generative AI models to generate information based on prompts (if needed).

[1886] Input: The retrieved technical information and the prompt statement.

[1887] Output: The organized technical information or information generated.

[1888] Specific behavior:

[1889] The server organizes the information it receives and, if necessary, uses a generative AI model (e.g., GPT-4) to generate information based on the user's prompt.

[1890] Step 5:

[1891] The server transmits the data to the terminal to provide the organized or generated information to the user.

[1892] Input: Organized technical information or generated information.

[1893] Output: Information provided to the user.

[1894] Specific behavior:

[1895] The server sends the organized information to the terminal in an appropriate format, such as JSON, and the terminal parses it and displays it to the user.

[1896] In this way, this system realizes a mechanism for efficiently collecting, analyzing, and providing information in response to user requests through a series of processing steps.

[1897] (Application example 1)

[1898] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1899] Researchers and developers are required to collect the latest technological information, discover and promote their own ideas, and effectively verify hypotheses and design experiments. However, it is difficult to efficiently obtain the necessary data from vast amounts of information and to optimally design experiments and set parameters. Furthermore, accessing learning materials and resources is time-consuming, and the latest information is constantly being updated, making it difficult to obtain the necessary information.

[1900] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1901] In this invention, the server includes means for collecting the latest technical information, means for analyzing input from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for generating and displaying advice related to projects based on user input, means for proposing ideas and experimental designs based on input information using a generative AI model, and means for automating access to the latest technologies and learning materials using prompt sentences. This enables researchers and developers to efficiently and quickly obtain the information and resources they need, improving the efficiency of research and development work.

[1902] "Means for collecting the latest technological information" refers to means for regularly collecting information on the latest technologies from reliable databases and external information sources and storing it in a database.

[1903] The "means for analyzing inputs from users regarding ideas and projects" refers to a means for analyzing information regarding ideas and projects input by users via their terminals and transmitting the information to a server as structured data.

[1904] "Means for searching relevant past projects and ideas and generating advice and solutions" means means for searching a database of past projects and generating relevant advice and solutions based on user input.

[1905] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to means for analyzing hypotheses and experimental ideas submitted by users and proposing optimal experimental designs and parameter settings based on those.

[1906] "Means for providing specific learning materials and resources" refers to means for searching for and providing the necessary learning materials and resources from databases and external repositories in response to a user's request.

[1907] The "means for generating and displaying advice related to a project based on user input" refers to means for analyzing project-related information input by a user and generating and displaying related advice.

[1908] "Means for using a generative AI model to propose ideas and experimental designs based on input information" refers to means for proposing new ideas and experimental designs using an artificial intelligence model generated based on input information.

[1909] "Means for automating access to the latest technology and learning materials using prompt sentences" means means for automatically accessing the latest technology and necessary learning materials using specific prompt sentences and providing the results to the user.

[1910] This invention is a system that helps researchers and developers efficiently collect the latest technical information, discover and promote ideas, verify hypotheses, and provide learning materials and resources. This system is composed of a server, terminals, and user interactions.

[1911] System configuration

[1912] The system consists of the following main modules:

[1913] A module that collects the latest technical information

[1914] A module that analyzes idea and project inputs

[1915] A module that searches past projects and ideas and generates advice

[1916] A module that analyzes hypotheses and experimental ideas and proposes experimental designs and parameter settings

[1917] Modules that provide learning materials and resources

[1918] A module that displays project-related advice based on user input.

[1919] A module that proposes ideas and experimental designs using generative AI models

[1920] A module that automates access to the latest technology and learning materials using prompts

[1921] Program processing overview

[1922] The server periodically obtains the latest technical information from reliable external sources and stores the information in a database. In response to a user's request, the server searches this database and provides the latest technical information.

[1923] When a user inputs a new project or idea through their device, the information is analyzed by a natural language processing module and sent as structured data to the server, which then searches for relevant information in a database of past projects and provides the user with the best advice and solutions.

[1924] Similarly, for hypotheses and experimental ideas, the system receives user input and suggests appropriate experimental designs and parameter settings, and uses generative AI models to suggest new ideas and experimental designs based on the information entered by the user.

[1925] Regarding learning materials and resources, upon user request, the system searches for the necessary materials from databases and external repositories and provides them to the user, improving user efficiency by automating access to specific technical information and papers, especially using prompts.

[1926] Hardware and software used

[1927] Hardware: Servers, devices (PCs, smartphones, etc.)

[1928] Software: Flask (web framework), natural language processing modules (e.g., spaCy, NLTK), database management systems (e.g., PostgreSQL)

[1929] Specific examples

[1930] For example, if a user searches for "latest machine learning algorithms," they might use the following prompt:

[1931] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[1932] This is a procedure by which the server retrieves relevant technical information from a database and provides it to the user.

[1933] Also, if the user wants ideas for new image recognition techniques, they can use the following prompt:

[1934] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[1935] This prompt will cause the server to use a generative AI model to search a database of relevant projects and provide appropriate advice to the user.

[1936] As described above, the present invention is a multi-functional system for efficiently collecting the latest technical information and supporting research and development.

[1937] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1938] Step 1:

[1939] The user sends a request through the terminal, for example, by entering a prompt to get information about the latest machine learning algorithms:

[1940] curl 'http: / / localhost:5000 / latest-tech-info?query=Latest Machine Learning Algorithms'

[1941] This request is sent to the server.

[1942] Step 2:

[1943] The server receives the request and analyzes the specified query. It analyzes the input query (e.g., "latest machine learning algorithms") and prepares to perform a database search based on the query. Specifically, it searches for related technical information using the query as a key.

[1944] Step 3:

[1945] The server searches the database for relevant technical information. It uses a database management system (e.g., PostgreSQL) to find the latest technical information that corresponds to the query. It retrieves the search results and formats the data as needed.

[1946] Step 4:

[1947] The server returns the search results to the user. The searched technical information is sent to the user as structured data in JSON format, allowing the user to view the requested technical information on their device.

[1948] Step 5:

[1949] A user submits input about a new idea or project. For example, a user might type "I'm looking for ideas for new image recognition methods":

[1950] curl -X POST -H "Content-Type: application / json" -d '{"input": "I'd like some ideas for new image recognition methods"}' 'http: / / localhost:5000 / generate-advice'

[1951] This request is sent to the server.

[1952] Step 6:

[1953] The server receives the user's input and parses it using a natural language processing module. The input text ("I'd like some ideas for new image recognition methods") is parsed and converted into structured data.

[1954] Step 7:

[1955] The server uses a generative AI model to generate ideas and advice based on the user's input. Using the analyzed data, the generative AI model suggests appropriate ideas and experimental designs.

[1956] Step 8:

[1957] The server returns the generated ideas and advice to the user. The advice generated by the generative AI model is sent to the user as structured data in JSON format, allowing the user to check the requested ideas and advice on their device.

[1958] Step 9:

[1959] A user inputs a hypothesis or experiment idea and sends it to the server. For example, the user might input, "I want to test a new model on a specific dataset." The server receives this request.

[1960] Step 10:

[1961] The server analyzes the user's hypotheses and experiment ideas and proposes appropriate experimental designs and parameter settings. It uses a natural language processing module to analyze the input and searches for appropriate experimental designs and parameter settings from past projects and databases.

[1962] Step 11:

[1963] The server returns the proposed experimental designs and parameter settings to the user. The analysis results are sent to the user as structured data such as JSON format. Thus, the user can check the proposed experimental designs and parameter settings on the terminal.

[1964] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[1965] The present invention is a system for solving the problems faced by researchers and developers and improving the efficiency of research and development work. In this system, a new function combined with an emotion engine is added to recognize the user's emotions and reflect them in the analysis results, providing more personalized support.

[1966] Function 1: Provision of the latest technical information

[1967] The server regularly collects the latest technical information from a database of technical information and external reliable information sources. The collected information is stored in the database.

[1968] When the user requests "the latest technical information", the terminal sends an HTTP request to the server.

[1969] The server retrieves the collected technical information and provides it to the user.

[1970] Examples:

[1971] When a user searches for "latest machine learning algorithms" through their device, the server provides relevant technical information and displays it on the device.

[1972] Function 2: Idea generation and project promotion

[1973] Users input ideas and project-related issues into the device.

[1974] The terminal analyzes the input information using a natural language processing (NLP) module and sends it to the server as structured data.

[1975] The server searches past projects and ideas and generates relevant advice and solutions.

[1976] Examples:

[1977] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[1978] Function 3: Support for hypothesis testing and experimental design

[1979] Users input hypotheses and experiment ideas into the terminal.

[1980] The terminal transmits the input hypotheses and experimental ideas to the server as structured data.

[1981] The server analyzes the hypothesis and suggests appropriate experimental designs and parameter settings.

[1982] Examples:

[1983] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[1984] Function 4: Providing learning materials and resources

[1985] The user types a request for specific learning materials or resources into the device.

[1986] The terminal sends a request to the server.

[1987] The server retrieves the required information from databases and external repositories and provides it to the user.

[1988] Examples:

[1989] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[1990] Feature 5: Adding an emotion engine

[1991] When analyzing user input with the natural language processing module, an emotion engine is also used.

[1992] The emotion engine recognizes emotions from user input and reflects that information in the analysis results.

[1993] The server tailors advice and solutions based on emotions, providing more personalized support.

[1994] Examples:

[1995] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[1996] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[1997] The processing flow will be explained below.

[1998] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[1999] Function 1: Providing the latest technical information

[2000] Processing Steps:

[2001] Step 1:

[2002] A user types "latest tech info" into the search bar on their device.

[2003] Step 2:

[2004] The terminal sends this input to the server as an HTTP request.

[2005] Step 3:

[2006] The server queries a database of technical information to obtain the latest information.

[2007] Step 4:

[2008] The server scrapes external trusted technical sites and paper repositories to see if there is new information.

[2009] Step 5:

[2010] The server updates the database with the latest information (if necessary).

[2011] Step 6:

[2012] The server formats the latest acquired technical information and returns it to the terminal.

[2013] Step 7:

[2014] The terminal receives the data from the server and displays it to the user.

[2015] Function 2: Idea generation and project promotion

[2016] Processing Steps:

[2017] Step 1:

[2018] Users input their ideas and project-related issues into the terminal.

[2019] Step 2:

[2020] The device analyzes this input using a natural language processing (NLP) module and sends it to the server as structured data.

[2021] Step 3:

[2022] The server searches a database for similar past projects and ideas.

[2023] Step 4:

[2024] The server uses an internal analytics engine to generate relevant solutions and advice.

[2025] Step 5:

[2026] The server returns the search results and generated advice to the terminal.

[2027] Step 6:

[2028] The terminal displays the response from the server to the user.

[2029] Function 3: Support for hypothesis testing and experimental design

[2030] Processing Steps:

[2031] Step 1:

[2032] The user inputs hypotheses and experiment ideas into the terminal.

[2033] Step 2:

[2034] The terminal sends this input as structured data to the server.

[2035] Step 3:

[2036] The server analyzes the input hypotheses and experimental ideas and searches a database for relevant past data and experimental results.

[2037] Step 4:

[2038] The server uses a specialized experiment design module to generate methods that suggest appropriate experiment designs and parameter settings.

[2039] Step 5:

[2040] The server performs hypothesis validation and generates feedback.

[2041] Step 6:

[2042] The server returns the analysis results and suggestions to the device.

[2043] Step 7:

[2044] The terminal displays feedback and suggestions from the server to the user.

[2045] Function 4: Providing learning materials and resources

[2046] Processing Steps:

[2047] Step 1:

[2048] A user inputs a request for a particular learning material or resource into a terminal.

[2049] Step 2:

[2050] The terminal sends this request to the server.

[2051] Step 3:

[2052] The server queries the database to find requested articles, books, tutorials, datasets, etc.

[2053] Step 4:

[2054] The server also searches external repositories and databases to retrieve the required resources.

[2055] Step 5:

[2056] The server organizes the acquired information and returns it to the terminal.

[2057] Step 6:

[2058] The terminal displays the learning materials and resources provided by the server to the user.

[2059] Feature 5: Adding an emotion engine

[2060] Processing Steps:

[2061] Step 1:

[2062] The user expresses emotions through text or voice data entered into the terminal.

[2063] Step 2:

[2064] The device passes the user's input to a natural language processing module and an emotion engine to analyze emotions.

[2065] Step 3:

[2066] The emotion engine recognizes the user's emotions and sends that information to the server as structured data.

[2067] Step 4:

[2068] The server receives the user's input, including the emotion data, and performs the necessary analysis.

[2069] Step 5:

[2070] The server tailors the advice and solutions it provides based on the perceived sentiment.

[2071] Step 6:

[2072] The server generates suggestions and feedback based on the emotion and sends them to the device.

[2073] Step 7:

[2074] The terminal displays feedback to the user according to the emotion from the server.

[2075] Examples:

[2076] When a user types, "I'm feeling anxious about this project," the emotion engine recognizes the user's anxiety and the server suggests advice and resources to provide reassurance.

[2077] Example 2

[2078] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2079] Conventional R&D support systems have limitations in collecting technical information and proposing ideas, and lack personalized support based on the user's emotions. In particular, it is difficult to provide advice and solutions that take the user's emotions into consideration, and there has been a demand for a means to reduce the psychological burden on users in R&D work.

[2080] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2081] In this invention, the server includes means for collecting the latest technical information, means for analyzing inputs from users regarding ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experiment ideas and proposing appropriate experiment designs and parameter settings, means for providing specific learning materials and resources, means for recognizing the user's emotions and reflecting that information in the analysis results, and means for adjusting advice and solutions based on the emotions and providing personalized support. This makes it possible to provide personalized support that takes the user's emotions into consideration, thereby improving the efficiency of research and development work and reducing psychological burden.

[2082] "Means for collecting the latest technical information" refers to the technology for regularly collecting the latest technical information from reliable external information sources and databases, and storing and managing it.

[2083] The "means for analyzing inputs regarding ideas and projects from users" refers to a technology that uses natural language processing technology to analyze ideas and project-related information input by users and extract necessary information.

[2084] "Means for searching for relevant past projects and ideas and generating advice and solutions" refers to technology for searching for past projects and ideas in a database based on analyzed user input and generating appropriate advice and solutions.

[2085] "Means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings" refers to technology for analyzing hypotheses and experimental ideas proposed by users and setting appropriate experimental designs and parameters based on them.

[2086] "Means for providing specific learning materials and resources" refers to the technology used to search for and provide the learning materials and resources requested by the user from databases or external repositories.

[2087] "Means for recognizing user emotions and reflecting that information in the analysis results" refers to technology that analyzes emotions from user input and reflects that emotional information in the analysis results and suggestions.

[2088] "Means for tailoring advice and solutions based on emotions and providing personalized support" refers to technology for tailoring advice and solutions based on the recognized emotions of a user and providing personalized support.

[2089] This invention is a system that solves the problems faced by researchers and developers and improves the efficiency of research and development work. This system has been added with a new function that combines an emotion engine, which recognizes the user's emotions and reflects them in the analysis results, enabling more personalized support.

[2090] Hardware and software used

[2091] The following hardware and software are used to implement this system: A general cloud server (e.g., Amazon Web Services or Google Cloud Platform) is used as the server, and MongoDB is used to store data. Computer devices such as PCs, smartphones, and tablets are used as terminals. Software such as Python, Django, SpaCy, TextBlob, SciPy, Pandas, and ElasticSearch are used.

[2092] Function 1: Providing the latest technical information

[2093] The server periodically collects the latest technical information from technical information databases and external reliable sources (such as ArXiv and IEEE) and stores it in a database such as MongoDB. When a user requests the "latest technical information" through their device, the device sends an HTTP request to the server. The server searches the collected technical information and returns the results to the device. The device then displays the search results to the user.

[2094] Examples:

[2095] When a user searches for "latest machine learning algorithms," the server provides relevant information and displays it on the device.

[2096] Example prompt sentence:

[2097] "Please provide information on the latest machine learning algorithms."

[2098] Function 2: Idea generation and project promotion

[2099] When a user inputs an idea or project-related issue into the device, the device analyzes it using a natural language processing (NLP) module (e.g., SpaCy) and sends it to the server as structured data. The server then searches past projects and ideas using ElasticSearch, generates relevant advice and solutions, and returns them to the device. The device then displays the advice to the user.

[2100] Examples:

[2101] When a user types in "I'm looking for ideas for new image recognition methods," the server searches for relevant past projects and provides advice.

[2102] Example prompt sentence:

[2103] "Please tell me your ideas for new image recognition methods."

[2104] Function 3: Support for hypothesis testing and experimental design

[2105] When a user inputs a hypothesis or experiment idea into the terminal, the terminal sends it as structured data to the server, which uses libraries such as SciPy and Pandas to analyze the hypothesis and propose appropriate experimental designs and parameter settings. The proposals are then returned to the terminal and displayed to the user.

[2106] Examples:

[2107] When a user types, "I want to validate a new model on a specific dataset," the server suggests an appropriate validation method and displays it on the terminal.

[2108] Example prompt sentence:

[2109] "How do I validate a new model on a specific dataset?"

[2110] Function 4: Providing learning materials and resources

[2111] When a user requests a specific learning material or resource (e.g., a recent paper or tutorial), the device sends it to the server, which retrieves the required information from a database or external repository and returns it to the device, which then displays the search results to the user.

[2112] Examples:

[2113] When a user requests, "I want the latest paper on deep learning," the server searches for the relevant paper and provides it to the user.

[2114] Example prompt sentence:

[2115] "Please provide the latest papers on deep learning."

[2116] Feature 5: Adding an emotion engine

[2117] When analyzing user input using the natural language processing module, an emotion engine (e.g., TextBlob) is also used to obtain an emotion score. The device then sends the analysis data, including the emotion score, to the server. The server then adjusts advice and solutions based on the emotion, providing personalized support. The adjusted advice is then returned to the device and displayed to the user.

[2118] Examples:

[2119] When a user types, "I'm worried about this project," the server recognizes the user's anxiety through its emotion engine and provides reassuring advice and resources.

[2120] Example prompt sentence:

[2121] "I'm feeling anxious about this project. Can you give me some advice?"

[2122] These functions enable the present invention to provide more precise and personalized support to meet user needs. Each function works together to provide users with the information and support they need quickly and effectively, significantly improving the quality and efficiency of research and development.

[2123] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2124] Function 1: Providing the latest technical information

[2125] Step 1:

[2126] The server periodically collects the latest technical information from technical information databases and external reliable sources. Specifically, the server runs scraping scripts to retrieve data from, for example, academic paper repositories. The collected data is then stored in MongoDB.

[2127] Input: URL or API key of external source

[2128] Output: Latest technical information data

[2129] Step 2:

[2130] When a user searches for the latest technological information, they enter the search keyword into their device. Specifically, the user enters "latest machine learning algorithms" into the browser search form.

[2131] Input: Search keyword

[2132] Output: Search request

[2133] Step 3:

[2134] The device sends the user's request to the server. Specifically, the device sends an HTTP GET request to the server.

[2135] Input: Search request

[2136] Output: HTTP request

[2137] Step 4:

[2138] The server searches for information based on the search keywords from the technical information database and returns the results to the terminal. Specifically, the server searches for information from MongoDB using Django view functions and returns the search results to the terminal in JSON format.

[2139] Input: Search request

[2140] Output: Search result data

[2141] Step 5:

[2142] The device displays the search results to the user. Specifically, the device uses JavaScript to display the search results in HTML.

[2143] Input: Search result data

[2144] Output: Displayed technical information

[2145] Function 2: Idea generation and project promotion

[2146] Step 1:

[2147] The user inputs ideas and project-related issues into the device. Specifically, the user enters "I'm looking for ideas for new image recognition methods" into the browser input form.

[2148] Input: Text about your idea or assignment

[2149] Output: User input data

[2150] Step 2:

[2151] The device analyzes the input information using a natural language processing (NLP) module. Specifically, the device uses SpaCy to analyze user input and extract keywords and key phrases.

[2152] Input: User-entered data

[2153] Output: Parsed data

[2154] Step 3:

[2155] The device converts the parsed data into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[2156] Input: Parsed data

[2157] Output: Structured data (JSON)

[2158] Step 4:

[2159] The server searches past projects and ideas, generates relevant advice and solutions, and uses ElasticSearch to search the database, collects relevant information, and returns the generated advice in JSON format to the device.

[2160] Input: Structured data (JSON)

[2161] Output: Advice and solution data

[2162] Step 5:

[2163] The device displays advice and solutions to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[2164] Input: Advice and solution data

[2165] Output: The displayed advice or solution

[2166] Function 3: Support for hypothesis testing and experimental design

[2167] Step 1:

[2168] The user inputs hypotheses and experimental ideas into the device, and specifically, the user writes in the notebook-style UI that they want to validate a new model on a specific dataset.

[2169] Input: Hypothesis or experimental idea text

[2170] Output: User input data

[2171] Step 2:

[2172] The device converts the input information into a structured data format (JSON) and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[2173] Input: User-entered data

[2174] Output: Structured data (JSON)

[2175] Step 3:

[2176] The server analyzes the hypotheses and proposes appropriate experimental designs and parameter settings. Specifically, the server analyzes the hypotheses using SciPy and Pandas, proposes experimental designs, and returns the proposed designs to the terminal in JSON format.

[2177] Input: Structured data (JSON)

[2178] Output: Experimental design proposal data

[2179] Step 4:

[2180] The device displays the suggestions to the user. Specifically, the device uses JavaScript to display the suggestions in HTML.

[2181] Input: Experimental design proposal data

[2182] Output: Experimental design proposal displayed

[2183] Function 4: Providing learning materials and resources

[2184] Step 1:

[2185] The user inputs specific learning materials and resources into the device. Specifically, the user types "I want the latest papers on deep learning" into a browser form.

[2186] Input: Text of specific learning materials or resources

[2187] Output: User input data

[2188] Step 2:

[2189] The device sends a request to the server. Specifically, the device sends data to the server using an HTTP GET request.

[2190] Input: User-entered data

[2191] Output: HTTP request

[2192] Step 3:

[2193] The server searches for relevant learning materials and resources from databases and external repositories and returns the results to the device. Specifically, the server searches for materials from GitHub, ArXiv, etc. based on the search keywords and returns them in JSON format to the device.

[2194] Input: HTTP request

[2195] Output: Learning materials and resource data

[2196] Step 4:

[2197] The device displays the search results to the user. Specifically, the device uses JavaScript to display the results in HTML.

[2198] Input: Study materials and resource data

[2199] Output: Displayed learning materials and resources

[2200] Feature 5: Adding an emotion engine

[2201] Step 1:

[2202] The user types input into the terminal. Specifically, the user types "I'm worried about this project" into a browser form.

[2203] Input: Sentiment text

[2204] Output: User input data

[2205] Step 2:

[2206] The device analyzes the input information using a natural language processing module (TextBlob) and obtains an emotion score. Specifically, the device calculates the emotion score using the TextBlob library.

[2207] Input: User-entered data

[2208] Output: Sentiment score

[2209] Step 3:

[2210] The device converts the data containing emotion information into JSON format and sends it to the server. Specifically, the device sends the data via an HTTP POST request.

[2211] Input: Sentiment score

[2212] Output: JSON data containing emotion information

[2213] Step 4:

[2214] The server then adjusts advice and solutions based on the emotion, providing personalized support. Specifically, the server analyzes the emotion score, generates personalized advice and resources, and returns them to the device in JSON format.

[2215] Input: JSON data containing emotion information

[2216] Output: personalized advice and solution data

[2217] Step 5:

[2218] The device then displays the adjusted advice to the user. Specifically, the device uses JavaScript to display the advice in HTML.

[2219] Input: personalized advice and solution data

[2220] Output: The displayed advice or solution

[2221] (Application example 2)

[2222] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2223] In modern manufacturing environments, there is a need to quickly detect problems and troubles that manufacturing robots face and provide optimal solutions. Furthermore, there is a lack of means to understand the emotions of operators involved in the manufacturing process and provide personalized support. This will improve manufacturing efficiency while reducing operator stress and creating an environment where operators can perform their work with peace of mind.

[2224] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting the latest technical information, means for analyzing input related to a user's ideas and projects, means for searching for related past projects and ideas and generating advice and solutions, means for analyzing hypotheses and experimental ideas and proposing appropriate experimental designs and parameter settings, means for providing specific learning materials and resources, means for analyzing and recognizing user emotion data, means for a manufacturing robot to detect problems during the manufacturing process and provide related technical information, and means for providing personalized support based on the user's emotions. This enables the manufacturing robot to quickly detect problems during the manufacturing process and provide optimal solutions, as well as provide personalized support based on the operator's emotions.

[2225] "Latest technological information" refers to information on the latest research results, technological trends, methodologies and tools related to manufacturing and technological development.

[2226] "User idea and project input" refers to new technology ideas and detailed information about ongoing projects that researchers and developers provide to the system.

[2227] "Past projects and ideas" refers to previously conducted research projects and development ideas, as well as data containing their results and findings.

[2228] "Advice and solutions" is information that provides users with specific guidelines or solutions to their problems or challenges.

[2229] "Hypotheses and experimental ideas" are proposals based on experimental plans and thought processes to predict or solve phenomena or problems.

[2230] "Appropriate experimental design and parameter settings" refers to the specific experimental methods and conditions used to verify a hypothesis.

[2231] "Specific learning materials and resources" are digital content such as specific information, tools, or guidelines that users need to learn.

[2232] "User emotional data" refers to emotional information extracted from text or voice input by a user to the system.

[2233] A "manufacturing robot" is a mechanical system that automates manufacturing processes such as product assembly and processing.

[2234] A "problem during the manufacturing process" is an anomaly, error, or factor that reduces efficiency that occurs during the manufacturing process.

[2235] "Means for providing technical information" refers to a method or system for presenting collected technical information to users.

[2236] "Personalized support" is support that aims to provide assistance and advice tailored to each individual situation, based on the user's emotions and specific needs.

[2237] This invention provides an integrated system for increasing the efficiency of manufacturing robots and operators in manufacturing operations. The system consists of the following major components:

[2238] System configuration

[2239] Hardware

[2240] Manufacturing robots: Assemble and process products

[2241] Server: Processes and manages the core of the system

[2242] User device: computer or tablet used by the operator

[2243] software

[2244] Sentiment Engine: Analyze user emotions

[2245] Natural Language Processing (NLP) module: Parsing user input

[2246] Technical information collection module: Collect the latest technical information through web scraping

[2247] Project Advisor module: Search past projects and generate advice

[2248] Experimental Design Module: Propose an experimental design for hypothesis testing

[2249] Data processing and calculation

[2250] Providing the latest technical information

[2251] The server periodically collects the latest technical information from reliable sources using a technical information collection module and stores it in a database. When a user requests the latest technical information through their device, the server receives the HTTP request, searches the database for relevant technical information, and provides it to the user.

[2252] Discovering ideas and promoting projects

[2253] Users input ideas and project-related issues into their devices. The devices use an NLP module to parse the input and send it to the server as structured data. The server then uses a project advisor module to search past projects and ideas, generate relevant advice and solutions, and provide them to the user.

[2254] Support for hypothesis testing and experimental design

[2255] When a user inputs a hypothesis or experiment idea into the device, the device sends the information as structured data to the server, which then analyzes the hypothesis using an experiment design module and proposes an appropriate experimental design and parameter settings.

[2256] Providing learning materials and resources

[2257] When a user requests specific learning materials or resources, the server retrieves the required information from databases or external repositories and provides it to the user.

[2258] Using the Emotion Engine

[2259] The device analyzes the user's input using an NLP module and emotion engine, and sends the structured data and emotion data to the server, which analyzes the emotion data and tailors the provision of advice, solutions, and personalized support based on the user's emotions.

[2260] Specific examples

[2261] For example, if a manufacturing robot detects a defect during a process and the operator inputs, "I'm concerned about this new manufacturing process," the system will use its emotion engine to recognize the emotion "concern." At the same time, the server will launch a technical information collection module, searching for and displaying relevant technical information and past projects.

[2262] Prompt Sentence Examples

[2263] Analyze the sentiment of the following sentence and generate a corresponding assistance message:

[2264] "I'm worried about problems with the new manufacturing process."

[2265] Emotion: Worry

[2266] Assistance message: Don't worry, the following solutions have worked for similar processes in the past:

[2267] The system enables rapid resolution of problems faced by manufacturing robots and provides personalized support to operators, improving the efficiency and quality of manufacturing operations.

[2268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2269] Step 1:

[2270] The user inputs information about an idea or project into the terminal, using natural language such as "I'm concerned about the new manufacturing process." This input becomes the starting point for the system's processing.

[2271] Step 2:

[2272] The device analyzes the input text using a natural language processing (NLP) module. The NLP module converts the text into structured data and sentiment data. This analysis clarifies the user's intent and sentiment. The input is text data, and the output is structured data and sentiment data.

[2273] Step 3:

[2274] The device sends the analyzed structured data and emotional data to the server. The server analyzes the emotional data and recognizes the user's emotions. Based on this recognition, the system prepares to provide personalized support. The input is the structured data and emotional data, and the output is the emotion recognition result.

[2275] Step 4:

[2276] The server launches a technical information collection module based on the structured data. The technical information collection module scrapes the latest technical information from reliable external sources and searches the information stored in the database. The input is the structured data, and the output is related technical information.

[2277] Step 5:

[2278] The server uses the Project Advisor module to search past projects and ideas, extract relevant advice and solutions from the past project database, and generate them. The input is structured data, and the output is advice and solutions.

[2279] Step 6:

[2280] The server analyzes hypotheses and experimental ideas, and proposes appropriate experimental designs and parameter settings using the experimental design module. The input is structured data of hypotheses and experimental ideas, and the output is experimental designs and parameter settings.

[2281] Step 7:

[2282] The server generates personalized support messages based on the user's emotions. The generated support messages reflect the user's emotional data and are designed to reassure the user. The input is emotional data, and the output is personalized support messages.

[2283] Step 8:

[2284] The server compiles the generated technical information, advice, experimental design, and personalized support messages and sends them to the user's device, which displays this information and makes it easily accessible to the user. The input is multiple generated data, and the output is the integrated support information.

[2285] Step 9:

[2286] The user's device displays technical information, advice, experimental designs, and personalized support messages received from the server. The user reviews this information and decides the next steps in the manufacturing process or project. The input is the integrated support information, and the output is the user's action to confirm the displayed content.

[2287] In this way, the system quickly resolves issues on the shop floor and provides personalized assistance to operators.

[2288] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2289] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2290] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2291] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2292] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2293] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2294] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2295] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2296] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2297] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2298] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2299] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2300] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2301] 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.

[2302] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2303] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2304] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hard...

Claims

1. A means of collecting the latest technical information; a means of analyzing user input regarding ideas and projects; A way to search for relevant past projects and ideas, generate advice and solutions, and A means to analyze hypotheses and experimental ideas and propose appropriate experimental designs and parameter settings; A system that includes the means to provide specific learning materials and resources.

2. The system of claim 1 , further comprising means for scraping data from external trusted sources when collecting current technical information.

3. 10. The system of claim 1, further comprising means for analyzing input from a user using a natural language processing module and converting the input into structured data.

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