KR102483916B1 - Method, apparatus and computer program for generating formalized research record data automatically for learning artificial intelligence - Google Patents

Abstract

A method, device, and computer program for automatically generating standardized research record data for learning artificial intelligence models are provided. A method of automatically generating standardized research record data for learning an artificial intelligence model according to various embodiments of the present invention is a method performed by a computing device, comprising the steps of receiving research record information about an experiment, pre-stored experiments and Processing the input research record information based on related data and generating standardized research record data using the processed research record information.

Description

Method, device, and computer program for automatically generating standardized research record data for learning artificial intelligence models

Various embodiments of the present invention relate to a method, apparatus, and computer program for automatically generating standardized research record data for learning an artificial intelligence model.

In general, bio and nano research is conducted through the steps of synthesizing substances and materials, confirming structures and configurations, measuring various properties to confirm application possibilities, and commercializing applications.

Here, if the structure and composition of an optimized material are not derived in the step of confirming the structure and composition, or if the desired properties are not specified in the step of measuring various properties for confirming application possibility, the first step, the structure and composition of the material Since the synthesizing step must be repeated, and the step of synthesizing the structure and composition of the material must be repeatedly performed to find the optimized material, it takes a minimum of several months to a maximum of several years to find the optimized material. There is a problem that a lot of cost is spent on synthesis and inspection.

On the other hand, in order to solve these conventional problems, a method of using an artificial intelligence model learned from various information and data related to bio and nano research has been proposed.

Here, in order to use the artificial intelligence model, it is essential to create research record data as data for learning the artificial intelligence model. Since it is written, there are various problems such as causing hassle such as repetition of similar records, omission of important conditions for experiments, necessity of reprocessing when using data, and difficulties in analysis and management due to the generation of large amounts of data.

The problem to be solved by the present invention is to increase the convenience of recording and greatly improve the accuracy of recording by processing unstructured research record information to automatically generate standardized research record data for artificial intelligence model learning, To provide a method, device, and computer program for automatically generating standardized research record data for learning artificial intelligence models that facilitate analysis, sharing, and management of research processes and results.

Another problem to be solved by the present invention is to shorten the synthesis period by learning an artificial intelligence model using standardized research record data and using it to suggest an optimal material structure, composition, and synthesis method to satisfy conditions. In addition, it is to provide a method, device, and computer program for automatically generating standardized research record data for learning an artificial intelligence model that can reduce the number of synthetic materials and further reduce the cost spent on experiments.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A method for automatically generating standardized research record data for learning of an artificial intelligence model according to an embodiment of the present invention for solving the above problems is a method performed by a computing device, in which research record information for an experiment is input. It may include receiving, processing the input research record information based on previously stored experiment-related data, and generating standardized research record data using the processed research record information.

In various embodiments, the step of receiving the research record information may include the step of receiving research record information in the form of a line including one or more text sentences and analyzing the input research record information in the form of a line to determine the experimental material. A step of extracting keywords, keywords related to research and experimentation processes, and keywords related to research results may be included.

In various embodiments, the step of receiving the research record information may include providing a user interface (UI) for inputting the research record information, and keywords related to experimental materials, research and experiments through the user interface. A step of receiving input of keywords related to processes and keywords related to research results may be included.

In various embodiments, the step of receiving the research record information includes providing a user interface (UI) for input of the research record information, receiving research record information in a voice form through the user interface, Natural language processing (NLP) of the input research record information in the form of voice to extract keywords related to experimental materials, keywords related to research and experiment process, and keywords related to research results, and equipment use information and materials through one or more sensors Recognizing usage information may be included.

In various embodiments, the input research record information includes keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research result information, and based on pre-stored data related to experimental materials, received from the user. Creating test material information by automatically completing keywords related to the input test material, and linking the generated test material information with data related to the previously stored test material, based on the data related to the previously stored research and experiment process , Creating research and experiment process information by automatically completing keywords related to the research and experiment process input from the user, and linking the generated research and experiment process information with data related to the previously stored research and experiment process, and Visualizing keywords related to the research result input from the user based on a pre-set result data format may be included.

In various embodiments, the generating of the standardized research record data may include simulating an experiment according to experiment materials, experiment equipment, and an experiment process included in the first standardized research record data, and a result of simulating the experiment. Matching and storing the first standardized research record data may be included.

In various embodiments, learning a first artificial intelligence model using the generated standardized research record data as learning data, configuring a substance that satisfies a condition input from a user using the learned first artificial intelligence model And predicting the structure and deriving an experimental method for synthesizing the material to have the predicted composition and structure, and information on the predicted composition and structure of the material and information about the derived experimental method. The method may further include providing result data to the user.

In various embodiments, the step of learning the first artificial intelligence model may include, in response to providing the result data to the user, an actual synthesis method of the substance input from the user and a result according to the actual synthesis method. and re-learning the first artificial intelligence model by comparing the result according to the derived experimental method and using each characteristic of a material generated according to the actual synthesis method as learning data.

In various embodiments, when scale-up is to be performed on a specific material derived using the first artificial intelligence model, a synthesizing method optimized for the specific material using a second artificial intelligence model And extracting information about the process, wherein the second artificial intelligence model is a model pre-learned using synthesis methods for each of a plurality of materials and information about yields according to the synthesis methods as learning data. can

An apparatus for automatically generating standardized research record data for learning an artificial intelligence model according to another embodiment of the present invention for solving the above problems is a processor, a network interface, a memory, and loaded into the memory, and the processor Including a computer program executed by, wherein the computer program includes an instruction for receiving research record information about an experiment, an instruction for processing the input research record information based on previously stored data related to an experiment, and the It may include instructions for generating standardized research record data using the processed research record information.

A computer program recorded on a computer-readable recording medium according to another embodiment of the present invention for solving the above problems is combined with a computing device, receiving research record information about experiments, experiments stored in advance It can be stored in a computer-readable recording medium to execute the step of processing the input research record information based on data related to and the step of generating standardized study record data using the processed research record information. .

Other specific details of the invention are included in the detailed description and drawings.

According to various embodiments of the present invention, for artificial intelligence model learning, unstructured research record information is processed to automatically generate standardized research record data, thereby increasing the convenience of recording, significantly improving the accuracy of recording, However, it has the advantage of making it easy to analyze, share, and manage the research process and results.

In addition, by learning an artificial intelligence model using standardized research record data and using it to suggest an optimal material structure, composition, and synthesis method to satisfy conditions, not only shortens the synthesis period but also increases the number of synthetic materials. There is an advantage in that it can reduce and further reduce the cost spent on experiments.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a diagram showing a system for automatically generating standardized research record data for learning an artificial intelligence model according to an embodiment of the present invention.
2 is a hardware configuration diagram of an apparatus for automatically generating standardized research record data for learning an artificial intelligence model according to another embodiment of the present invention.
3 is a flowchart of a method for automatically generating standardized research record data for learning an artificial intelligence model according to another embodiment of the present invention.
4 is a flowchart illustrating a method of extracting keywords from research record information in the form of a line, in various embodiments.
5 is a diagram exemplarily illustrating research record information in the form of a line and analysis results thereof, in various embodiments.
6 is a flowchart illustrating a method of receiving text-type research record information through a user interface (UI) in various embodiments.
7 is a flowchart illustrating a method of receiving audio-type research record information through a user interface (UI) in various embodiments.
8 is a flowchart illustrating a method of performing a simulation based on standardized research record data, in various embodiments.
9 is a flowchart of a method for training an artificial intelligence model using standardized study record data, in various embodiments.
10 to 13 are diagrams illustrating forms of user interfaces applicable to various embodiments by way of example.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into smaller numbers of components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means any kind of hardware device including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a computer may be understood as including a smartphone, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device, but is not limited thereto.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least a part of each step may be performed in different devices according to embodiments.

1 is a diagram showing a system for automatically generating standardized research record data for learning an artificial intelligence model according to an embodiment of the present invention.

Referring to FIG. 1, the system for automatically generating standardized research record data for learning of an artificial intelligence model according to an embodiment of the present invention includes an apparatus 100 for automatically generating standardized research record data, a user terminal 200, and an external server. (300).

Here, the system for automatically generating standardized research record data for learning of the artificial intelligence model shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and as needed may be added, changed or deleted. For example, a system for automatically generating standardized research record data for learning an artificial intelligence model does not separately include an external server 300 for storing and managing various information and data, and an apparatus for automatically generating standardized research record data (100 ), various types of information and data can be stored and managed using the storage space provided within.

In one embodiment, the apparatus 100 for automatically generating standardized research record data may receive research record information about an experiment from a user, and automatically process and record the input research record information according to a preset format, thereby generating artificial It is possible to generate standardized research record data for learning of the intelligence model.

In addition, the apparatus 100 for automatically generating standardized research record data may record and store the standardized research record data, and perform a virtual simulation of a specific experiment by computing various information included in the stored standardized research record data. Coded research record data can be generated by encoding the standardized study record data for the purpose. In this case, the standardized research record data and the coded research record data may be correlated and stored.

In various embodiments, the apparatus 100 for automatically generating standardized research record data may be connected to the user terminal 200 through the network 400, and a user interface (User Interface) for receiving research record information through the user terminal 200 Interface, UI) (eg, Graphic User Interface (GUI), 10 of FIGS. 10 to 13) may be provided, and research record information may be input through the UI.

Here, the network 400 may refer to a connection structure capable of exchanging information between nodes such as a plurality of terminals and servers. For example, the network 400 includes a local area network (LAN), a wide area network (WAN), a world wide web (WWW), a wired and wireless data communication network, a telephone network, a wired and wireless television communication network, and the like. can do.

In addition, here, the wireless data communication networks are 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi (Wi-Fi) Fi), Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, A near-field communication (NFC) network, a satellite broadcasting network, an analog broadcasting network, a digital multimedia broadcasting (DMB) network, and the like may be included. However, it is not limited thereto, and other general-purpose networks applicable to the same or similar technical fields may be further included.

In various embodiments, the apparatus 100 for automatically generating standardized research record data may generate standardized research record data based on research record information input from a specific user, and transmit the standardized research record data to other users. A standardized research record data sharing service can be provided.

For example, the apparatus 100 for automatically generating standardized research record data may store and manage a plurality of standardized research record data generated based on research record information input from a plurality of users, and each of the plurality of users It is possible to provide a search and reproduction service for pre-stored standardized research record data, and users can search and check the standardized research record data they want through the search and reproduction service (e.g., check and simulate information recorded in data) ) can be implemented.

In addition, the apparatus 100 for automatically generating standardized research record data provides an import function for importing and storing standardized research record data from the outside and an export function for exporting previously stored standardized research record data to the outside. Thus, it may be implemented so that the user can download a plurality of pre-stored standardized research record data or upload standardized research record data generated externally from the user.

At this time, the computing device 100 supports various extensions, and wide compatibility, such as converting the extension of standardized research record data exported to the outside according to the user's request or receiving standardized research record data having various extensions from the outside, is uploaded. It can be implemented to have

In various embodiments, the apparatus 100 for automatically generating standardized research record data provides a service for automatically generating standardized research record data for learning artificial intelligence models based on a web or application based on a user's request. It can, but is not limited to this.

In one embodiment, the user terminal 200 may be connected to the standardized research record data automatic generation device 100 through the network 400, and the research record data may be automatically generated through the UI provided from the standardized research record data automatic generation device 100. Record information may be input, and in response to input of the research record information, standardized research record data generated corresponding to the research record information may be provided.

In various embodiments, the user terminal 200 downloads, installs, and executes an application provided from the device 100 for automatically generating standardized research record data, thereby generating standardized research record data from the device 100 for automatically generating standardized research record data. Automatic generation service can be provided.

To this end, the user terminal 200 may be a smartphone including an operating system capable of driving applications and including a display for UI output on at least a portion of the user terminal 200 . However, it is not limited thereto, and the user terminal 200 is a wireless communication device that ensures portability and mobility, and includes navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet ) terminal, smart pad (Smartpad), tablet PC (Tablet PC), such as all kinds of handheld (Handheld) based wireless communication device can be included.

In one embodiment, the external server 300 may be connected to the standardized research record data automatic generation device 100 through the network 400, and the standardized research record data automatic generation device 100 learns the artificial intelligence model. Store and manage various information/data (e.g., data related to experimental materials, data related to research and experiment process, and result data form, etc.) necessary to perform the method of automatically generating standardized research record data for It is possible to store and manage various information/data (e.g., standardized (or coded) research record data) generated by performing the method of automatically generating standardized research record data for learning.

Here, the external server 300 may be a storage server provided separately outside the standardized research record data automatically generating device 100, but is not limited thereto. Hereinafter, the hardware configuration of the standardized research record data automatically generating device 100 will be described with reference to FIG. 2 .

2 is a hardware configuration diagram of an apparatus for automatically generating standardized research record data for learning an artificial intelligence model according to another embodiment of the present invention.

Referring to FIG. 2, an apparatus 100 for automatically generating standardized research record data according to another embodiment of the present invention (hereinafter referred to as “computing apparatus 100”) is provided by one or more processors 110 and the processor 110. It may include a memory 120 for loading the computer program 151 to be executed, a bus 130, a communication interface 140, and a storage 150 for storing the computer program 151. Here, in FIG. 2, only components related to the embodiment of the present invention are shown. Therefore, those skilled in the art to which the present invention pertains can know that other general-purpose components may be further included in addition to the components shown in FIG. 2 .

The processor 110 controls the overall operation of each component of the computing device 100 . The processor 110 includes a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art of the present invention. It can be.

Also, the processor 110 may perform an operation for at least one application or program for executing a method according to embodiments of the present invention, and the computing device 100 may include one or more processors.

In various embodiments, the processor 110 may temporarily and/or permanently store signals (or data) processed in the processor 110 (RAM: Random Access Memory, not shown) and ROM (ROM: Read -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphics processing unit, RAM, and ROM.

Memory 120 stores various data, commands and/or information. Memory 120 may load computer program 151 from storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151. The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 130 provides a communication function between components of the computing device 100 . The bus 130 may be implemented in various types of buses such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired and wireless Internet communication of the computing device 100 . Also, the communication interface 140 may support various communication methods other than internet communication. To this end, the communication interface 140 may include a communication module well known in the art. In some embodiments, communication interface 140 may be omitted.

The storage 150 may non-temporarily store the computer program 151 . When the standardized research record data automatic generation process is performed through the computing device 100, the storage 150 provides various information necessary to provide the standardized research record data automatic generation process (eg, data related to experimental materials, research and data related to the experimental process and result data forms, etc.) can be stored.

The storage 150 may be a non-volatile memory such as read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.

Computer program 151 may include one or more instructions that when loaded into memory 120 cause processor 110 to perform methods/operations in accordance with various embodiments of the invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present disclosure by executing the one or more instructions.

In one embodiment, the computer program 151 includes steps of receiving research record information about an experiment, processing the input research record information based on pre-stored data related to the experiment, and standardizing the research record information using the processed research record information. It may include one or more instructions for performing a method of automatically generating standardized research record data for learning of an artificial intelligence model, including generating research record data.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, with reference to FIGS. 3 to 13, a method for automatically generating standardized research record data for learning of an artificial intelligence model performed by the computing device 100 will be described.

3 is a flowchart of a method for automatically generating standardized research record data for learning an artificial intelligence model according to another embodiment of the present invention.

Referring to FIG. 3 , in step S110 , the computing device 100 may receive research record information about an experiment.

Here, the research record information may refer to information generated as a user conducts an experiment or research. For example, the research record information may include, but is not limited to, information about experimental materials, information about experimental equipment, information about experimental and research processes, and information about experimental and research results. Hereinafter, with reference to FIGS. 4 to 7 , a method for the computing device 100 to receive research record information from a user will be described.

4 is a flowchart illustrating a method of extracting keywords from research record information in the form of lines in various embodiments, and FIG. 5 exemplarily shows research record information in the form of lines and analysis results thereof in various embodiments. It is an illustrated drawing.

Referring to FIG. 4 , in step S210 , the computing device 100 may receive research record information in the form of lines including one or more text sentences. For example, as shown in FIG. 5 , the computing device 100 may receive research record information recorded in a line or handwritten form including one or more sentences.

In step S220, the computing device 100 may analyze the research record information in the form of lines input in step S210 to extract keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results.

For example, the computing device 100 may extract a plurality of keywords from the research record information in the form of lines by analyzing (eg, OCR analysis, etc.) the research record information in the form of lines, and the extracted keywords and the database By matching pre-stored data (e.g., keyword data for each experimental material, keyword data for each research and experiment process, and keyword data for each research result), a plurality of extracted keywords are converted into keywords related to experimental materials, keywords related to research and experimental processes, and Research results can be classified as keywords.

That is, the computing device 100 determines that each of a plurality of keywords extracted from the research record information in the form of a line is based on pre-stored keyword data for each experimental material, keyword data for each research and experiment process, and keyword data for each research result. It is determined whether the keywords are pointing to, research and experimentation processes, or research results, and keywords may be classified into categories according to the determination result.

Here, the computing device 100 uses pre-stored keyword data for each experimental material, keyword data for each research and experiment process, and keyword data for each research result as learning data, and performs string analysis and keyword extraction operations using a pre-learned artificial intelligence model. It may be performed, but is not limited thereto.

In various embodiments, the computing device 100 extracts keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results by analyzing research record information in the form of lines, and standardized research among the extracted keywords. If a keyword corresponding to information essential for generating record data is not extracted, the research record information in the form of lines may be repeatedly reanalyzed until the keyword is extracted.

At this time, the computing device 100 guides information to input information corresponding to the keyword when the corresponding keyword is not extracted even though the research record information in the form of a line is reanalyzed for a preset number of times (N times). can provide. Through this, in generating standardized research record data, it is possible to assist in inputting all important conditions and information that must be input without omission.

6 is a flowchart illustrating a method of receiving text-type research record information through a user interface (UI) in various embodiments.

Referring to FIG. 6 , in step S310, the computing device 100 may provide a UI (eg, 10 of FIGS. 10 to 13) for inputting research record information.

Here, since the research record information includes information on test materials, information on test equipment, and information on experiments and research processes, the UI 10 has a function of receiving information on test materials and information on test equipment. It may include a function of receiving a rule input and a function of receiving information about an experiment and research process.

In step S320 , the computing device 100 may receive input of keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results through the UI 10 .

First, the computing device 100 may provide the UI 14 for inputting information on experimental materials in response to a user selecting the first button 11 . At this time, as shown in FIG. 11 , the computing device 100 may provide the UI 14 for inputting information on experimental materials in a pop-up form as the user selects the first button 11 . Not limited.

Thereafter, the computing device 100 may receive a keyword related to the experimental material through the UI 14 for inputting information on the experimental material. For example, as shown in FIG. 11 , the computing device 100 inputs a keyword for a nickname, a keyword for an element symbol (Chemical) through the UI 14 for inputting information on a test material, Keywords for product number, keywords for state, keywords for solvent, keywords for volume, and keywords for concentration (in addition, a separate comments) can be entered.

Although not shown in the drawings, similarly to the above, the computing device 100 provides a UI for inputting information on the experiment equipment in response to the user selecting the second button 12, thereby providing information on the experiment equipment. It is possible to receive input of keywords related to experimental equipment through the UI for input, and in response to the user selecting the third button 13, by providing a UI for inputting information on experiments and research processes, experiments and Keywords related to the research process may be entered.

7 is a flowchart illustrating a method of receiving audio-type research record information through a user interface (UI) in various embodiments.

Referring to FIG. 7 , in step S410, the computing device 100 may provide the UI 10 for inputting research record information. Here, the UI 10 provision operation performed by the computing device 100 is the same as or similar to the UI 10 provision operation performed in step S310, and the UI 10 provided in step S410 is provided in step S310. It may be implemented in the same or similar form as the UI 10, but is not limited thereto.

In step S420, the computing device 100 may receive audio-type research record information through the UI 10 provided in step S410. For example, the computing device 100 may upload research record information in the form of audio through the UI 10, but is not limited thereto, and the user is included in the user terminal 200 in the process of conducting research and experiments. A voice input device (eg, a microphone) may be controlled to operate, and the computing device 100 may receive research record information in real time through the voice input device included in the user terminal 200 .

In step S430, the computing device 100 may extract keywords by analyzing the audio type research record information input through step S420.

In various embodiments, the computing device 100 may extract keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results by natural language processing (NLP) on research record information in the form of input voice. . For example, the computing device 100 converts research record information in the form of audio into text and processes the textualized research record information in natural language to perform keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results. can be extracted. However, it is not limited thereto, and various methods of extracting a specific keyword from voice data may be applied.

In various embodiments, the computing device 100 operates the voice input device when the research record information in the form of voice input in step S420 is input in real time through the voice input device provided in the user terminal 200. One or more keywords may be extracted by analyzing (eg, natural language processing) a voice input device input in real time from the point of view.

At this time, the computing device 100 may perform verification of one or more keywords extracted in real time by reanalyzing all research record information in the form of voice input through the voice input device when the driving of the voice input device is terminated. For example, the computing device 100 reanalyzes the entire research record information in the form of audio to determine the validity of the extracted keyword in real time (eg, whether the extracted keyword is correctly extracted or whether the wrong keyword is extracted), It is possible to check whether there are keywords that could not be extracted.

In step S440, the computing device 100 may recognize equipment usage information and material usage information through one or more sensors. For example, the computing device 100 may be connected to a sensor (eg, a position sensor, a motion sensor, etc.) provided in each equipment used for research and experimentation, and equipment use information based on sensor data collected from the corresponding sensor. (eg, whether or not the equipment is used and the type of equipment used) may be recognized (eg, when the location of a specific equipment moves more than a predetermined distance or when motion of a specific equipment is detected).

In addition, the computing device 100 may be connected to a sensor that measures the amount of the experimental material, and based on sensor data collected from the corresponding sensor, material usage information (eg, whether or not the material is used, the type and amount of the material used) etc.) can be recognized. However, it is not limited thereto.

As described above, the computing device 100 extracts keywords from research record information in the form of lines, receives keywords directly through the UI 10, and extracts keywords from research record information in the form of audio. It is possible to extract keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results using one method, but it is not limited thereto, and two or more methods are individually applied and the results are compared. It can be implemented in various forms, such as a method of extracting keywords by one method or a method of verifying the extracted keywords by other methods.

In addition, in addition to the method described above, other general-purpose methods may be applied to extract keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results from unstandardized research record information.

Again, referring to FIG. 3 , in step S120, the computing device 100 may process the research record information input in step S110 based on previously stored experiment-related data.

First, the computing device 100 may generate experimental material information by automatically completing keywords related to the experimental material input from the user based on pre-stored data related to the experimental material. For example, as shown in FIG. 8 , when the computing device 100 receives a keyword “ag” related to the experimental material from the user, one or more experimental material information corresponding to the keyword (eg, AgNo- ₃ and The list 15 including experimental materials including "ag" input from the user may be provided, and in response to selection of specific experimental material information included in the list, a keyword related to the experimental material may be entered from the user. Corresponding test material information can be automatically completed.

Thereafter, when the computing device 100 receives a keyword input from the user and generates experimental material information, the computing device 100 may interlock and store the generated experimental material information and previously stored data related to the experimental material, through which an accurate sample is generated. It can be implemented to enable information confirmation.

In addition, the computing device 100 automatically completes keywords related to the research and experiment process input from the user based on previously stored data related to the research and experiment process, identically or similarly to the above method, to obtain research and experiment process information. can be created, and the created research and experiment process information and data related to the previously stored research and experiment process can be linked and stored.

In addition, the computing device 100 may visualize (eg, image, graph, etc.) keywords related to the research result input from the user based on a preset result data form.

In step S130, the computing device 100 may generate standardized research record data using the processed research record information. For example, as shown in FIG. 12, the computing device 100 templates processed research record information (eg, automatically completed experiment material information, research and experiment process information, and visualized research results) to standardize it. Research records can be created.

In various embodiments, the computing device 100 may simulate experiments and research using the standardized research record data generated according to the above method. Hereinafter, it will be described with reference to FIG. 8 .

8 is a flowchart illustrating a method of performing a simulation based on standardized research record data, in various embodiments.

Referring to FIG. 8 , in step S510 , the computing device 100 may simulate an experiment according to experimental materials, experimental equipment, and experimental procedures included in the first standardized research record data.

First, the computing device 100 may build a cloud lab (unmanned automatic laboratory, virtual lab) to perform experimental simulation based on standardized research record data.

Here, the cloud lab is a platform-based research and experiment space utilizing a data sharing system such as the cloud, and specific research and experimentation based on external data (eg, standardized research record data provided from the computing device 100). It may mean a space where experiments can be virtually simulated or contents and results related to research and experiments can be shared with multiple users without time and space restrictions. Here, the method of building and using the cloud lab is a previously known technology, and even if a specific method of building and using the cloud lab is not described in this specification, those skilled in the art will be able to easily recognize it.

Thereafter, the computing device 100 generates coded research record data by converting the standardized research record data into a computer-readable code form in order to compute virtual research and experiments based on the standardized research record data. The generated coded research record data can be provided to the cloud lab, and the experiment based on the coded research record data can be simulated by operating the cloud lab.

Here, the coded research record data simulated through the cloud lab is automatically performed for all standardized research record data whenever standardized research record data is generated based on the research record information entered by the user, or If a simulation is requested for specific standardized research record data from the company, simulation may be performed only for the standardized research record data.

In step S520, the computing device 100 simulates the experiment based on the coded research record data through the cloud lab (e.g., the experimental materials included in the coded research record data and the structure of the material derived according to the experimental process). and composition and properties of the material, etc.) can be matched and stored with the coded research record data used in the simulation.

At this time, the simulation result may be added to and stored in the research result information included in the coded research record data in the cloud lab, and the computing device 100 receives and stores coded research record data in which the simulation result is added and stored from the cloud lab. and can be managed. Hereinafter, with reference to FIG. 9, a method for the computing device 100 to learn an artificial intelligence model using standardized (or coded) research record data generated according to the above method as learning data will be described.

9 is a flowchart of a method for training an artificial intelligence model using standardized study record data, in various embodiments.

Referring to FIG. 9 , in step S610, the computing device 100 may train the first artificial intelligence model using standardized research record data.

Here, the first artificial intelligence model (eg, neural network) is composed of one or more network functions, and the one or more network functions may be composed of a set of interconnected computational units that may be generally referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. One or more network functions include at least one or more nodes. Nodes (or neurons) that make up one or more network functions can be interconnected by one or more 'links'.

In the first artificial intelligence model, one or more nodes connected through a link may form a relationship of a relatively input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting an input node and an output node may have a weight. The weight may be variable, and may be variable by a user or an algorithm so that the first artificial intelligence model performs a desired function. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the first artificial intelligence model, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the first artificial intelligence model. Characteristics of the first artificial intelligence model may be determined according to the number of nodes and links in the first artificial intelligence model, a relationship between the nodes and links, and a value of a weight assigned to each link. For example, when there are two first artificial intelligence models having the same number of nodes and links and different weight values between the links, the two first artificial intelligence models may be recognized as different from each other.

Some of the nodes constituting the first artificial intelligence model may constitute one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the first artificial intelligence model may be defined in a method different from that described above. For example, a layer of nodes may be defined by a distance from a final output node.

The initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the first artificial intelligence model. Alternatively, in the relationship between nodes based on links in the first artificial intelligence model network, it may refer to nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the first artificial intelligence model. Also, the hidden node may refer to nodes constituting the first artificial intelligence model other than the first input node and the last output node. The first artificial intelligence model according to an embodiment of the present disclosure may have more nodes of an input layer than nodes of a hidden layer close to an output layer, and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. It can be an artificial intelligence model.

The first artificial intelligence model may include one or more hidden layers. A hidden node of a hidden layer may use outputs of previous layers and outputs of neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be operated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In various embodiments, the first artificial intelligence model may be a deep neural network (DNN). A deep neural network may refer to a first artificial intelligence model including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), deep belief network (DBN), Q network, U network, Siamese network, and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In various embodiments, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical.

Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to the number of remaining sensors after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

In addition, at least one of supervised learning, unsupervised learning, and semi-supervised learning may be applied to the method of learning the first artificial intelligence model.

Here, learning of the first artificial intelligence model is to minimize output errors. In the learning of the first artificial intelligence model, the learning data is repeatedly input into the first artificial intelligence model, the output of the first artificial intelligence model for the learning data and the error of the target are calculated, and the first artificial intelligence is directed to reduce the error. This is a process of updating the weight of each node of the first artificial intelligence model by backpropagating the error of the model from the output layer of the first artificial intelligence model to the input layer.

First, teacher learning is a method in which learning data is created by labeling specific data and information related to the specific data, and learning is performed using this. The learning data is generated by labeling two data having a causal relationship, and the generated learning It means learning from data.

More specifically, the computing device 100 may perform learning on one or more network functions constituting an artificial intelligence model using labeled training data. For example, the computing device 100 inputs each of the learning input data to one or more network functions, compares each of the output data calculated with the one or more network functions and each of the learning output data corresponding to a label of each of the learning input data, error can be derived. That is, in training of an artificial intelligence model, learning input data may be input to an input layer of one or more network functions, and learning output data may be compared with outputs of one or more network functions.

The computing device 100 may train an artificial intelligence model based on an error between an operation result of one or more network functions for learning input data and a learning output data (label).

Also, the computing device 100 may adjust weights of one or more network functions based on the error in a backpropagation method. That is, the computing device 100 may adjust the weight so that the output of one or more network functions is close to the learning output data based on an error between the calculation result of the one or more network functions for the learning input data and the learning output data.

When learning of one or more network functions is performed for a predetermined epoch or more, the computing device 100 may determine whether to stop learning by using verification data. The predetermined epochs may be part of an overall learning target epoch.

Verification data may consist of at least some of the labeled training data. That is, the computing device 100 performs learning of the artificial intelligence model through the learning data, and after the learning of the artificial intelligence model is repeated at least a predetermined epoch, the learning effect of the artificial intelligence model reaches a predetermined level using the verification data. It can be judged whether it is abnormal or not. For example, when the computing device 100 performs learning with a target number of learning iterations of 10 using 100 learning data, after performing iterative learning 10 times, which is a predetermined epoch, using 10 verification data By performing repeated learning three times, if the change in the output of the artificial intelligence model during the three iterative learning is less than a predetermined level, it is determined that further learning is meaningless and the learning may be terminated.

That is, the verification data can be used to determine the completion of learning based on whether the effect of learning per epoch is greater than or less than a certain level in repeated learning of the artificial intelligence model. The above-described number of learning data, verification data, and number of iterations are only examples, and the present disclosure is not limited thereto.

Next, in the case of comparative history learning, an error may be calculated by comparing input learning data with an output of the first artificial intelligence model. The calculated error is back-propagated in the first artificial intelligence model in a reverse direction (ie, from the output layer to the input layer), and connection weights of each node of each layer of the first artificial intelligence model may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The computation of the first artificial intelligence model on the input data and the backpropagation of errors may constitute a learning cycle (epoch).

The learning rate may be applied differently according to the number of iterations of the learning cycle of the first artificial intelligence model. For example, a high learning rate is used at the beginning of the learning of the first artificial intelligence model to ensure that the first artificial intelligence model quickly achieves a certain level of performance to increase efficiency, and a low learning rate is used in the later stage to increase accuracy. .

In the learning of the first artificial intelligence model, generally, the training data may be a subset of actual data (ie, data to be processed using the learned first artificial intelligence model). Accordingly, there may be a learning cycle in which errors for training data decrease but errors for actual data increase.

Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which the first artificial intelligence model, which has learned a cat by showing a yellow cat, does not recognize that the cat is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing errors in machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, methods such as increasing training data, regularization, and omitting some nodes of a network in the process of learning may be applied.

In various embodiments, the computing device 100 may generate an artificial intelligence model by testing performance of one or more network functions using test data to determine whether to activate one or more network functions. The test data may be used to verify the performance of the artificial intelligence model and may be composed of at least a part of training data. For example, 70% of the training data can be used for training of an AI model (i.e., learning to adjust weights to output similar results to labels), and 30% of the training data can be used to verify the performance of an AI model. It can be used as test data for The computing device 100 may determine whether to activate the artificial intelligence model according to whether or not the performance exceeds a predetermined level by inputting test data to the artificial intelligence model for which learning has been completed and measuring an error.

The computing device 100 verifies the performance of the learned artificial intelligence model using test data for the learned artificial intelligence model, and if the performance of the learned artificial intelligence model is above a predetermined standard, the artificial intelligence model is transferred to another application. can be activated for use.

In addition, the computing device 100 may deactivate and discard the AI model when the performance of the trained AI model is below a predetermined standard. For example, the computing device 100 may determine the performance of the generated artificial intelligence model based on factors such as accuracy, precision, and recall. The aforementioned performance evaluation criteria are only examples and the present disclosure is not limited thereto. According to an embodiment of the present disclosure, the computing device 100 may independently train each artificial intelligence model to generate a plurality of artificial intelligence models, evaluate performance, and use only artificial intelligence models having a certain performance or higher. .

In step S620, the computing device 100 may predict the composition and structure of a material that satisfies the condition input from the user by using the learned first artificial intelligence model.

For example, when a user wants to synthesize a substance that is effective for disease z and has no side effect y, the computing device 100 uses the first artificial intelligence model to optimize a substance that is effective for disease z and has no side effect y structure and composition can be predicted.

In step S630, the computing device 100 may derive an experimental method for synthesizing a substance that is effective for disease z and has no side effect y through step S620 to have a predicted composition and structure.

For example, the computing device 100 is effective for disease z when synthesizing a substance having structure a according to synthesis method A, but has a side effect y, and synthesizes a substance having structure b according to synthesis method B. A'B' synthesis method, which is a method of synthesizing a substance having an optimized structure and composition and an optimized structure and composition of a substance that is effective for disease z and has no side effect y, considering that it is effective for disease y' in the case of can be derived.

In step S640, the computing device 100 may provide result data including information on the composition and structure of the material predicted in step S620 and information on the experimental method derived in step S630 to the user, and may provide the result data to the user. In response to the information provided to the user, feedback information may be received from the user.

For example, the computing device 100 responds to providing information on the experimental method derived in step S630 to the user, and how the user actually synthesized the material according to the derived experimental method (actual synthesizing method of the material). can be input.

In addition, in response to providing information on the composition and structure of the material predicted in step S620 to the user, the computing device 100 results according to the actual synthesis method (composition and structure of the material produced according to the actual synthesis method). Comparison results between the results (composition and structure of the predicted material) according to the derived experimental method may be input.

In addition, the computing device 100 may receive property information of a material produced according to an actual synthesis method from the user in response to providing result data to the user.

In step S650, the computing device 100 may relearn the first artificial intelligence model by using the user's feedback information input through step S640 as learning data.

For example, the computing device 100 may learn the synthesis method using the actual synthesis method of the material input from the user as first learning data, and the result according to the actual synthesis method (configuration of the material produced according to the actual synthesis method) And structure) and the result (predicted composition and structure of the material) according to the derived experimental method can be used as the second learning data to learn the composition and structure of the material. The characteristic can be learned by using the characteristic information as the third learning data.

That is, the computing device 100 learns the first artificial intelligence model using the standardized research record data, and derives a substance that meets the conditions input from the user and a method for synthesizing the substance using the same, and the synthesis period. Not only can reduce the cost of experiments and research by reducing the number of synthetic materials, but also the first artificial intelligence model using feedback information as the user actually conducts experiments and researches based on the derived result data. By re-learning, there is an advantage in that the reliability of such result data can be improved.

In various embodiments, the computing device 100 may perform scale-up on a specific material derived using the first artificial intelligence model, using the second artificial intelligence model for the specific material. Information on synthesis methods and processes optimized for mass production can be extracted.

Here, the second artificial intelligence model may be a model pre-learned by using information on a synthesis method for each of a plurality of materials and a yield according to the synthesis method as learning data.

In addition, here, the computing device 100 has been described as extracting information on a synthesis method and process optimized for a specific material using a second artificial intelligence model provided separately in addition to the first artificial intelligence model, but this is one It is an example of, but not limited to, the second artificial intelligence model is set to distinguish functional differences from the first artificial intelligence model, and in fact, both the function of the first artificial intelligence model and the function of the second artificial intelligence model. It is implemented as an artificial intelligence model that can be performed, and through the artificial intelligence model, the operation of extracting result data including the composition, structure, and synthesis method of a substance, and information on the synthesis method and process optimized for a specific substance All operations to extract can be performed.

A method for automatically generating standardized research record data for learning the aforementioned artificial intelligence model has been described with reference to the flowchart shown in the drawing. For a brief explanation, the method for automatically generating standardized research record data for learning artificial intelligence models has been illustrated and described as a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are shown here It may be performed in a different order or concurrently with the procedures performed. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed. Hereinafter, the UI 10 provided by the computing device 100 will be described with reference to FIGS. 10 to 13 .

10 to 13 are diagrams illustrating forms of user interfaces applicable to various embodiments by way of example.

First, referring to FIGS. 10 to 13 , the UI 10 includes a first button 11 for inputting information on experimental materials, a second button 12 for inputting information on experimental equipment, and experiments and research. A third button 13 for inputting information about a process may be included.

In response to a selection of at least one of the first button 11, the second button 12, and the third button 13 by the user, the computing device 100 provides information about the experiment material and information about the experiment equipment. A UI for receiving any one of information and information on experiments and research processes may be provided. For example, when the user selects the first button 11 on the UI 10 (eg, inputting a mouse click), the UI 14 for inputting information on experimental materials in a pop-up form may be provided. .

In addition, although not shown in the drawings, as described above, a UI for inputting information on experimental equipment may be provided in response to the user selecting the second button 12, and the user may use the third button 13 In response to selecting ), a UI for inputting information on experiments and research processes may be provided. At this time, each of the UIs may be output in a pop-up form similarly to the UI 14 for inputting information on the experiment material.

The computing device 100 includes a UI 14 for inputting information on experimental materials, a UI for inputting information on experimental equipment, and experimental materials input through the UI for inputting information on experiments and research processes; It is possible to create experimental material information, research and experiment process information, and research result information by automatically completing and interlocking keywords for research and experiment processes and research results, and UI (10) according to each generated information according to a preset template. can be placed and registered on the For example, each piece of information may be implemented in the form of an individual box as shown in FIG. 12, but information belonging to the same category may be arranged in the same column.

At this time, the card including specific information disposed (registered) on the UI 10 may be freely copied and disposed in another area on the UI 10 according to a user's request. For example, the computing device 100 requests copying of the first card 16 including information on a specific experimental material (eg, selection of the first card 16, a preset key (eg, Ctrl) + drag input), a second card 16', which is a copy of the first card 16, can be created, displayed, and registered in an area designated by the user (eg, an area where the mouse pointer is located after a drag input). there is.

In addition, a plurality of cards including specific information disposed (registered) on the UI 10 may be freely mixed according to a user's request. For example, the computing device 100 may request a combination of a first card including information on a first test material and a second card including information on a second test material (eg, selecting and selecting a first card). In response to obtaining an action of dragging and placing the second card on top of the second card, selecting and dragging the second card and placing it on the first card, etc.), combining the information included in the first card with the information included in the second card A new third card (eg, including information on the first test material and information on the second test material) may be created.

When all information is placed (registered) on the UI 10 and generation of the finally standardized research record data is completed, the computing device 100 provides information about the standardized research record data through the summary information providing UI 17. Summary information can be provided.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: standardized research record data automatic generation device (computing device)
200: user terminal
300: external server
400: network

Claims (11)

In a method performed by a computing device,
Receiving research record information about an experiment;
Processing the input research record information based on previously stored experiment-related data, wherein the input research record information includes keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research result information step; and
Generating standardized research record data using the processed research record information,
The step of processing the input research record information,
Based on data related to previously stored experimental materials, generating experimental material information by automatically completing keywords related to experimental materials input from a user, and interlocking the generated experimental material information with data related to the previously stored experimental materials. ;
Based on data related to the pre-stored research and experiment process, keywords related to the research and experiment process input from the user are automatically completed to generate research and experiment process information, and the generated research and experiment process information and the pre-stored research and experiment process information are generated. Linking data related to research and experimentation processes; and
Visualizing a keyword related to the research result input from the user based on a predetermined result data form,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 1,
The step of receiving the research record information,
Receiving research record information in the form of a line containing one or more text sentences; and
Analyzing the input research record information in the form of lines and extracting keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 1,
The step of receiving the research record information,
Providing a user interface (UI) for input of the research record information; and
Receiving keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results through the user interface,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 1,
The step of receiving the research record information,
Provides a user interface (UI) for inputting the research record information, receives research record information in the form of voice through the user interface, and processes the received research record information in the form of natural language (NLP) extracting keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research results; and
Recognizing equipment usage information and material usage information through one or more sensors,
A method for automatically generating standardized research record data for learning artificial intelligence models. delete According to claim 1,
The step of generating the standardized research record data,
simulating an experiment according to experimental materials, experimental equipment, and experimental procedures included in the first standardized research record data; and
Comprising the step of matching and storing the result of simulating the experiment with the first standardized research record data,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 1,
learning a first artificial intelligence model using the generated standardized research record data as learning data;
predicting a composition and structure of a material that satisfies a condition input from a user using the learned first artificial intelligence model, and deriving an experimental method for synthesizing the material to have the predicted composition and structure; and
Further comprising providing result data including information on the composition and structure of the predicted material and information on the derived experimental method to the user,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 7,
The step of learning the first artificial intelligence model,
In response to providing the result data to the user, the actual synthesis method of the substance input from the user, the comparison between the result according to the actual synthesis method and the result according to the derived experimental method, and the actual synthesis method Re-learning the first artificial intelligence model using each of the characteristics of the material generated according to the learning data,
A method for automatically generating standardized research record data for learning artificial intelligence models. According to claim 7,
Information on a synthesis method and process optimized for the specific material using the second artificial intelligence model when scale-up is to be performed for a specific material derived using the first artificial intelligence model Extracting, but the second artificial intelligence model is a model pre-learned by using a synthesis method for each of a plurality of substances and information on yield according to the synthesis method as learning data.
A method for automatically generating standardized research record data for learning artificial intelligence models. processor;
network interface;
Memory; and
A computer program loaded into the memory and executed by the processor,
The computer program,
An instruction for receiving research record information about an experiment;
Processing the input research record information based on previously stored experiment-related data, wherein the input research record information includes keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research result information instruction; and
Including instructions for generating standardized research record data using the processed research record information,
The instruction for processing the input research record information,
Based on the data related to the pre-stored experimental material, an instruction for generating experimental material information by automatically completing a keyword related to the experimental material input from the user, and linking the generated experimental material information with data related to the previously stored experimental material ;
Based on data related to the pre-stored research and experiment process, keywords related to the research and experiment process input from the user are automatically completed to generate research and experiment process information, and the generated research and experiment process information and the pre-stored research and experiment process information are generated. Instructions for linking data related to research and experimentation processes; and
Including instructions for visualizing keywords related to the research results input from the user based on a predetermined result data form,
A device for automatically generating standardized research record data for learning artificial intelligence models. Combined with a computing device,
Receiving research record information about an experiment;
Processing the input research record information based on previously stored experiment-related data, wherein the input research record information includes keywords related to experimental materials, keywords related to research and experimental processes, and keywords related to research result information step; and
Generating standardized research record data using the processed research record information,
The step of processing the input research record information,
Based on data related to previously stored experimental materials, generating experimental material information by automatically completing keywords related to experimental materials input from a user, and interlocking the generated experimental material information with data related to the previously stored experimental materials. ;
Based on data related to the pre-stored research and experiment process, keywords related to the research and experiment process input from the user are automatically completed to generate research and experiment process information, and the generated research and experiment process information and the pre-stored research and experiment process information are generated. Linking data related to research and experimentation processes; and
A computer-readable record for executing a method of automatically generating standardized research record data for learning of an artificial intelligence model comprising the step of visualizing a keyword related to the research result input from the user based on a pre-set result data form stored on media,
computer program.

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