Analysis system, analysis method, and program product

Abstract

The present application provides an analysis system, an analysis method, and an analysis program that can flexibly utilize insights obtained by an experimenter in an experiment. The analysis system according to the present disclosure includes an analysis target data acquisition unit, a text acquisition unit, an extraction unit, and an analysis unit. The analysis target data acquisition unit acquires analysis target data, which is at least one of measurement data obtained by measuring a sample and numerical data related to the sample. The text acquisition unit acquires text data that records an experimental content of the sample. The extraction unit extracts extraction information related to the experimental content from the text data. The analysis unit performs analysis based on the analysis target data and the extraction information.

Description

Technical Field

[0001] This disclosure relates to analytical systems, analytical methods, and analytical procedures. Background Technology

[0002] Patent Document 1 describes a data analysis system for analyzing measurement data of analyzed materials. The data analysis system described in Patent Document 1 receives measurement data via a communication device, processes the measurement data using a learned machine learning model, and outputs the analysis results.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2021-092467

[0004] It should be noted that experimenters and developers gain various insights during the experiments. These insights may contain information that is important for the analysis.

[0005] However, it is difficult to incorporate such insights into the analysis. Therefore, the analytical systems involved in the prior art cannot fully and flexibly utilize such insights.

[0006] In other words, existing analytical systems suffer from the problem of not being able to fully and flexibly utilize the insights gained by the experimenter during the experiment. Patent Document 1 does not disclose a technology that can solve this problem. Summary of the Invention

[0007] This disclosure was made to address such a problem, with the aim of providing an analytical system, analytical method, and analytical procedure that can flexibly utilize the insights gained by the experimenter in the experiment.

[0008] The analytical system disclosed herein includes an analytical object data acquisition unit, a text acquisition unit, an extraction unit, and an analysis unit. The analytical object data acquisition unit acquires analytical object data, which is at least one of measurement data obtained by measuring a sample and numerical data related to the sample. The text acquisition unit acquires text data recording experimental details of the sample. The extraction unit extracts extraction information related to the experimental details from the text data. The analysis unit performs analysis based on the analytical object data and the extracted information.

[0009] Based on this configuration, the analysis system according to this embodiment can acquire the insights obtained by the experimenter through experiments as textual data. As a result, the analysis system according to this embodiment can flexibly utilize the insights obtained by the experimenter during experiments.

[0010] In the analytical system disclosed herein, the extracted information may include at least one of information related to the experimental conditions of the sample and information related to the physical properties of the sample.

[0011] In the analytical system disclosed herein, the extraction unit extracts at least one of the items relating to the experimental conditions of the sample and the physical properties of the sample.

[0012] In the analytical system disclosed herein, the extraction unit can extract items related to the physical properties of the sample and quantify the degree of those items for each sample.

[0013] In the analysis system disclosed herein, the extraction unit can extract items related to the physical properties of the sample, assign a first predetermined value to samples that exhibit the physical property, and assign a second predetermined value to samples that do not exhibit the physical property, thereby quantifying the degree of the physical property.

[0014] The analysis system disclosed herein may also include a designated receiving department for user-submitted projects. Furthermore, the analysis department can evaluate the correlation between the received designated projects and the data of the analysis object.

[0015] The analysis system disclosed herein may further include an unknown data acquisition unit, which acquires unknown data of the analysis object data, which is an unknown sample. Furthermore, the extracted information may include information related to the physical properties of the sample, and the analysis unit can predict the physical properties of the unknown sample based on the unknown data, the analysis object data, and the extracted information.

[0016] In the analysis system disclosed herein, the text acquisition unit can acquire image data of a piece of paper containing experimental content, perform image processing on the acquired image data to convert the text recorded on the paper into text data, and obtain the converted text data as text data containing experimental content of the sample.

[0017] The analytical methods disclosed herein involve the following steps.

[0018] The analytical object data is obtained from at least one of measurement data obtained by measuring the sample and numerical data related to the sample.

[0019] Obtain textual data related to the sample.

[0020] Extract relevant information from text data.

[0021] The analysis is performed based on the data of the object being analyzed and the information extracted.

[0022] The analytical procedures disclosed herein cause a computer to perform the following actions.

[0023] The analytical object data is obtained from at least one of measurement data obtained by measuring the sample and numerical data related to the sample.

[0024] Obtain textual data related to the sample.

[0025] Extract relevant information from text data.

[0026] The analysis is performed based on the data of the object being analyzed and the information extracted.

[0027] According to this disclosure, analytical systems, analytical methods, and analytical procedures can be provided that can flexibly utilize the insights gained by experimenters in experiments.

[0028] The above and other objectives, features and advantages of this disclosure will be more fully understood from the detailed description and accompanying drawings given below. Attached Figure Description

[0029] Figure 1 This is a block diagram illustrating the configuration of the analysis system according to the first embodiment.

[0030] Figure 2 This is a block diagram illustrating the configuration of the server according to the first embodiment.

[0031] Figure 3 This is a block diagram illustrating the configuration of the server according to the first embodiment.

[0032] Figure 4 This is a flowchart illustrating the operation of the analysis system according to the first embodiment.

[0033] Figure 5 This is a block diagram illustrating the configuration of the server according to the second embodiment.

[0034] Figure 6 This is a block diagram illustrating the configuration of the server according to the third embodiment. Detailed Implementation

[0035] <First Embodiment>

[0036] (Analysis of the system's structure)

[0037] Hereinafter, the first embodiment of this disclosure will be described in detail with reference to the accompanying drawings. First, the configuration of the analysis system according to this embodiment will be described in detail.

[0038] Figure 1 This is a block diagram illustrating the configuration of the analysis system according to the first embodiment. For example... Figure 1 As shown, the analysis system 1 according to this embodiment is connected to a server 100 and a user terminal 200 via a network N such as the Internet.

[0039] The analysis system 1 involved in this embodiment is typically provided as part of a data cloud service used in materials development and research development, and is used as a system for promoting research and development using so-called materials informatics (MI) and data science.

[0040] Analysis system 1 stores various data related to experimental samples. Moreover, analysis system 1 analyzes the stored data based on instructions from the user.

[0041] The various data related to the experimental samples mentioned here include the data of the analysis objects and text data, which will be described later.

[0042] In analysis system 1, user terminal 200 sends various data related to the experimental samples to server 100, and server 100 analyzes the received data. Furthermore, server 100 sends the analysis results to user terminal 200, and user terminal 200 displays the received analysis results.

[0043] The user terminal 200 involved in this embodiment is a user-operated terminal, typically a computer device with a display device.

[0044] User terminal 200 sends various data related to the experimental samples to server 100 via network N. Furthermore, user terminal 200 receives analysis results of the various data related to the experimental samples from server 100 via network N.

[0045] In this embodiment, the server 100 receives various data related to the experimental samples from the user terminal 200 via network N and analyzes the received data. Furthermore, the server 100 sends the analysis results to the user terminal 200 via network N.

[0046] Figure 2 This is a block diagram illustrating the hardware configuration of the server according to the first embodiment.

[0047] like Figure 2 As shown, server 100 includes processor 110, memory 120, storage device 130, input / output interface 140, network interface 150, and internal bus 160.

[0048] The internal bus 160 is a data transmission path for the processor 110, memory 120, storage device 130, input / output interface 140, and network interface 150 to send and receive data with each other. However, the method of interconnecting the processor 110 and the others is not limited to bus connection.

[0049] Memory 120 is a main storage device implemented using RAM (Random Access Memory) or the like. Storage device 130 is an auxiliary storage device implemented using a hard disk, SSD (Solid State Drive), memory card, or ROM (Read Only Memory) or the like. Storage device 130 stores programs used to perform the desired functions.

[0050] Processor 110 is any processor, such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), or FPGA (Field-Programmable Gate Array). Processor 110 executes the program (described later) by reading it from storage device 130 into memory 120 and executing it. Figure 3 The functions of each module shown in the diagram.

[0051] Input / output interface 140 is an interface used to connect server 100 to input / output devices. For example, input devices such as keyboards and output devices such as displays can be connected to input / output interface 140.

[0052] Network interface 150 is an interface used to connect server 100 to a network.

[0053] The program includes a set of commands (or software code) for causing a computer to perform one or more functions described in the implementation when read by the computer. The program may be stored on a non-transitory computer-readable medium or a physical storage medium. Without limitation, examples of computer-readable media or physical storage media include RAM, ROM, flash memory, SSD or other memory technologies, CD-ROM, DVD (digital versatile disc), Blu-ray disc or other optical disc storage, cassette tape, magnetic tape, disk storage, or other magnetic storage devices. The program may also be transmitted on a transient computer-readable medium or communication medium. Without limitation, examples of transient computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagation signals.

[0054] Figure 3 This is a block diagram illustrating the configuration of the server according to the first embodiment. For example... Figure 3 As shown, the server 100 in this embodiment includes an analysis object data acquisition unit 111, a text acquisition unit 112, an extraction unit 113, and an analysis unit 114 as functional modules.

[0055] The analysis object data acquisition unit 111 acquires analysis object data. More specifically, the analysis object data acquisition unit 111 of this embodiment acquires multiple analysis object data from the user terminal 200 via network N. The analysis object data acquisition unit 111 outputs the acquired analysis object data to the analysis unit 114.

[0056] Furthermore, the analysis object data acquisition unit 111 can store the acquired analysis object data in the storage device 130 whenever analysis object data is acquired. Moreover, when performing the analysis described later, the analysis object data can be read from the storage device 130 and output to the analysis unit 114.

[0057] In addition, in this case, the analysis object data acquisition unit 111 does not need to read all the analysis object data stored in the storage device 130, but can only read the specified analysis object data received from the user and output it to the analysis unit 114.

[0058] In other words, the analysis object data acquisition unit 111 can create a database storing analysis object data. Furthermore, the analysis object data acquisition unit 111 according to this embodiment can be configured such that the user can appropriately select data as the object of analysis from the created database.

[0059] Here, the analytical object data acquired by the analytical object data acquisition unit 111 according to this embodiment is at least one of measurement data obtained by measuring the sample and numerical data related to the sample.

[0060] In other words, the analytical object data acquisition unit 111 according to this embodiment acquires analytical object data as at least one of measurement data obtained by measuring the sample and numerical data related to the sample.

[0061] The measurement data obtained by measuring the sample can be raw data output from the measuring device that measured the sample, or data that has undergone parsing processing. The measurement data obtained by measuring the sample can be any data that is recorded in a computer-readable format.

[0062] Measurement data obtained by measuring samples can include, for example, spectral data, waveform data, graph data, two-dimensional image data, three-dimensional image data, etc.

[0063] Examples of spectral data include those obtained using nuclear magnetic resonance spectroscopy (NMR), infrared spectroscopy (IR), ultraviolet / visible spectroscopy (UV-vis), X-ray absorption spectroscopy (XAS), Raman spectroscopy, X-ray diffraction (XRD), small-angle X-ray scattering (SAXS), and mass spectrometry (MS).

[0064] In addition, as two-dimensional image data, examples include image data captured using optical microscopes, scanning electron microscopes (SEM), transmission electron microscopes (TEM), computed tomography (CT), etc.

[0065] In addition, examples of three-dimensional image data include imaging data created by overlaying tomographic images taken using computed tomography (CT) scans, and model data created using CAD (Computer-Aided design).

[0066] Furthermore, waveform data can include, for example, time-series data and displacement data. Time-series data can include, for example, acoustic data and vibration data; any data whose values ​​change over time can be used. Displacement data can include, for example, the surface height and surface profile of a sample; any data whose values ​​change with changes in coordinates and other parameters can be used.

[0067] In addition, other data such as cyclic voltammetry, gas chromatography (GC) spectra, and coordinate data such as CIF (Crystallographic Information) files can be cited.

[0068] Numerical data related to a sample can be, for example, numerical data that defines the experimental conditions of the sample, or numerical data that represents the composition of the sample.

[0069] For example, when the sample is a composition, the numerical data related to the sample could be the content percentage of each component contained in the composition. Alternatively, when the sample is a product, the numerical data related to the sample could be values ​​related to reaction conditions such as reaction temperature, reaction time, the weight of the substrate used in the reaction, and the reaction scale.

[0070] Alternatively, the data analyzed can be either measurement data obtained by measuring a sample or numerical data related to the sample. For example, physical property values ​​of a sample can be cited as examples of data that combine measurement data obtained by measuring a sample and numerical data related to the sample.

[0071] Examples of physical properties that can be used as samples include mechanical properties such as strength, hardness, toughness, and wear resistance; physical properties such as density, electrical conductivity, magnetism, thermal conductivity, and coefficient of thermal expansion; and chemical properties such as corrosion resistance.

[0072] In addition, the data to be analyzed is not limited to data that is only associated with the sample; for example, it may also include the performance values ​​of products and modules created using the sample.

[0073] Examples of performance values ​​for products and modules created using samples include the photoelectric conversion efficiency of solar cells with photoelectric conversion layers using samples as materials, and the durability of vehicles manufactured using samples as body materials.

[0074] In other words, the analysis object data acquired by the analysis object data acquisition unit 111 may be data that directly or indirectly defines the composition of the sample as the analysis object, or data that directly or indirectly evaluates the performance of the sample as the analysis object.

[0075] In this embodiment, the data of the analysis object is data that records information related to the experimental content in a form other than an article.

[0076] The text acquisition unit 112 acquires text data containing experimental information about the samples. The text acquisition unit 112 outputs the acquired text data to the extraction unit 113.

[0077] Furthermore, the text acquisition unit 112 can store the acquired text data in the storage device 130 whenever text data is acquired. Moreover, when performing the analysis described later, the text data can be read from the storage device 130 and output to the extraction unit 113.

[0078] Furthermore, in this case, the text acquisition unit 112 does not need to read all the text data stored in the storage device 130, but can only read the specified text data received from the user and output it to the extraction unit 113.

[0079] In other words, the text acquisition unit 112 can create a database storing text data. Moreover, the text acquisition unit 112 according to this embodiment can be configured so that the user can appropriately select data as the object of analysis from the created database.

[0080] Furthermore, in the database mentioned here, text data can be stored in a correspondence with the aforementioned analysis object data. In other words, the server 100 involved in this embodiment can store the acquired analysis object data and text data in the same database on a sample-by-sample basis.

[0081] In this case, the text acquisition unit 112 can acquire text data corresponding to the data of the specified analysis object that was received from the user as the analysis object.

[0082] Here, the text data involved in this embodiment refers to the data recorded in text form by the experimenter who performed the experiment related to the sample and created a document to record the experimental content.

[0083] For example, text data can be created by the experimenter inputting an article recording the experimental content into the user terminal 200 using an input unit such as a keyboard. In this case, the text acquisition unit 112 acquires the text data recording the experimental content of the sample by receiving the input text data from the user terminal 200. In this case, the text data can also be referred to as electronic experimental notes.

[0084] Alternatively, the document recording the experimental content can also be content written on paper by the experimenter using writing tools. In this case, the text acquisition unit 112 can convert the written text into text data by acquiring image data of the paper on which the user recorded the experimental content from the user terminal 200 and performing image processing on the acquired image data. Furthermore, the text acquisition unit 112 can acquire the converted text data as text data recording the experimental content of the sample. In this case, the document recording the experimental content can be a so-called experimental notebook.

[0085] As described above, the text acquisition unit 112 of this embodiment can acquire text data that has been recorded as electronic data from the beginning, or it can acquire text data generated by converting text information that was originally recorded on paper.

[0086] In other words, the text acquisition unit 112 can acquire text data by acquiring data other than text data and converting or extracting the acquired data.

[0087] For example, the text acquisition unit 112 can extract text data from electronic data containing both text data and image data. The image referred to here could be, for example, a photograph showing the appearance of a sample, a sketch of experimental equipment, or an image representing the measured spectral data of the sample. In other words, the image referred to here can be any image recorded in experimental notes or electronic experimental notes.

[0088] Although details will be described later, the analysis system according to this embodiment uses information extracted from text data acquired by the text acquisition unit to perform analysis. In other words, the analysis system according to this embodiment uses information extracted from documents created by the experimenter to record experimental content to perform analysis. As a result, the analysis system according to this embodiment can flexibly utilize the insights gained by the experimenter through the experiment.

[0089] The extraction unit 113 acquires text data from the text acquisition unit 112. The extraction unit 113 extracts extraction information related to the experimental content from the text data. The extraction unit 113 outputs the extracted information to the analysis unit 114.

[0090] The extracted information involved in this embodiment is used to analyze the sample. Therefore, it is preferable that the extracted information involved in this embodiment includes, for example, information related to the experimental conditions of the sample and information related to the physical properties of the sample.

[0091] The extraction information extracted by the extraction unit 113 is information related to the experimental content contained in the text data that can be analyzed and processed. Therefore, the extraction method of the extraction unit 113 can be freely set according to the method used for analysis.

[0092] For example, when the analysis method used is multivariate analysis, such as principal component analysis, the extraction unit 113 can extract the extraction information as numerical data containing multiple variables.

[0093] In this case, the extraction unit 113 can first extract items related to at least one of the information related to the experimental conditions of the sample and the information related to the physical properties of the sample. Moreover, the extraction unit 113 can extract the extracted information into numerical data by quantifying the degree of the extracted items for each sample.

[0094] In the above-described case, the extraction unit 113 can, for example, refer to a database containing vocabulary related to experimental conditions and vocabulary related to physical properties. Furthermore, the extraction unit 113 can extract items by retrieving vocabulary recorded in the database from text data.

[0095] Alternatively, the extraction unit 113 may also extract items by using artificial intelligence (AI) trained in a manner that takes text data as input data and outputs items.

[0096] Alternatively, the extraction unit 113 can also quantify the degree of an item by extracting the value representing the degree of the item recorded in the text data.

[0097] For example, if the text data states "the reaction solution was heated at a reaction temperature of 60 degrees Celsius for 1 hour," the extraction unit 113 first extracts "reaction temperature" and "reaction time" as items related to the experimental conditions. Furthermore, for the "reaction temperature" item, the degree of the item is quantified by extracting the value "60 degrees Celsius." Similarly, for the "reaction time" item, the degree of the item is quantified by extracting the value "1 hour."

[0098] In addition, the extraction unit 113 can extract items related to the physical properties of the sample, assign a first predetermined value to samples that exhibit the physical property, and assign a second predetermined value to samples that do not exhibit the physical property, thereby quantifying the degree of the physical property.

[0099] For example, if text data is obtained stating "a product with foaming properties is obtained" and text data is obtained stating "a product is obtained" without mentioning foaming properties, the extraction unit 113 first extracts "foaming properties" as an item related to the physical properties of the sample.

[0100] Furthermore, the extraction unit 113 assigns a first predetermined value (e.g., "1") to the "foaming property" item of the sample corresponding to the text data that states "a foaming product is obtained". Additionally, the extraction unit 113 assigns a second predetermined value (e.g., "0") to the "foaming property" item of the sample corresponding to the text data that states "a product is obtained" but does not mention foaming property.

[0101] Based on this structure, information in textual data that does not contain specific numerical values ​​can also be processed as numerical data. As a result, the analysis system according to this embodiment can more flexibly utilize the insights gained by the experimenter during the experiment.

[0102] In addition, the extraction unit 113 can also extract words that indicate the degree of an item from the text data and quantify the degree of the item based on the extracted words.

[0103] For example, when there is text data stating "a slightly foamed product is obtained", text data stating "a foamed product is obtained", and text data stating "a vigorously foamed product is obtained", the extraction unit 113 first extracts "foaming property" as an item related to the physical properties of the sample. In addition, the extraction unit 113 extracts "slightly" and "vigorously" as words indicating the degree of the item.

[0104] Furthermore, the extraction unit 113 assigns a first predetermined value (e.g., "1") to the "foaming property" item of the sample corresponding to the text data described as "obtaining a slightly foamed product". Additionally, the extraction unit 113 assigns a second predetermined value (e.g., "2") larger than the first predetermined value to the "foaming property" item of the sample corresponding to the text data described as "obtaining a foamed product". Furthermore, the extraction unit 113 assigns a third predetermined value (e.g., "3") larger than the second predetermined value to the "foaming property" item of the sample corresponding to the text data described as "obtaining a violently foamed product".

[0105] Based on this configuration, information in textual data that does not contain specific numerical values ​​can be quantified in greater detail. As a result, the analysis system according to this embodiment can more flexibly utilize the insights gained by the experimenter during experiments.

[0106] In addition, the extraction unit 113 can extract the identification information related to each extracted item as extraction information.

[0107] In this case, the extraction unit 113 may, for example, extract the "production batch number of the compound" as an item and extract the corresponding "production batch number" as identification information.

[0108] In addition, the extraction unit 113 can extract, for example, the "date and time of sample creation" as an item, and extract the corresponding "date and time" as identification information.

[0109] Additionally, the extraction unit 113 can extract, for example, the "sample creation method" as an item. In this case, the extraction unit 113 can pre-assign an identifier to the type of "sample creation method". Moreover, the corresponding sample creation method can be identified based on information recorded in text data, and the identifier corresponding to the identified creation method is recorded as extraction information.

[0110] Thus, the extraction unit 113 of this embodiment extracts information related to the experimental content from text data. The extraction method is not particularly limited and can be freely set according to the method used for analysis. In other words, for the extraction unit 113 of this embodiment, any method that can extract experimentally related information recorded in a form applicable to the analysis method from text data can be used to extract the information.

[0111] In this embodiment, the extraction unit 113 may, for example, use artificial intelligence (AI) to extract information from text data.

[0112] In this case, the extraction unit 113 extracts the extraction information by using artificial intelligence (AI) trained in a manner that takes text data as input data and outputs extracted data in the form of an analysis method performed by the analysis unit 114 described later.

[0113] The analysis unit 114 obtains analysis object data from the analysis object data acquisition unit 111 and extracts extraction information from the extraction unit 113. The analysis unit 114 performs analysis based on the analysis object data and the extraction information.

[0114] For example, the analysis unit 114 can acquire analysis object data containing multiple variables and extract information, and perform analysis using multivariate analysis. In multivariate analysis, the information recorded in the extracted information can be used as either explanatory variables or target variables.

[0115] For example, the analysis unit 114 according to this embodiment can extract feature quantities, such as principal components, from the data to be analyzed. Furthermore, the analysis unit 114 can evaluate the correlation between the extracted feature quantities and the extracted information.

[0116] For example, the analysis unit 114 can evaluate the correlation between the feature quantities extracted from the data of the analysis object and information related to the physical properties of the sample.

[0117] In this case, the analysis unit 114 can, for example, perform principal component analysis on the assigned analysis object data and calculate the principal component scores of each principal component. Furthermore, the analysis unit 114 can evaluate the correlation between the obtained principal component scores and information related to the physical properties of the sample.

[0118] Based on this configuration, the analysis system of this embodiment can clearly indicate the relationship between the characteristic quantities of the analyzed object data and the physical properties shown by the sample. As a result, the analysis system of this embodiment can assist users in examining the physical properties of the sample.

[0119] Furthermore, in this case, for example, the analysis unit 114 can perform principal component analysis on the assigned analysis object data and calculate the principal component scores of each principal component. Moreover, the analysis unit 114 can evaluate the correlation between the obtained principal component scores and information related to the experimental conditions of the sample.

[0120] In other words, the analysis unit 114 can evaluate the correlation between the feature quantities extracted from the data of the analysis object and the information related to the experimental conditions of the sample.

[0121] Based on this configuration, the analysis system of this embodiment can clearly indicate the relationship between the characteristic quantities of the analyzed data and the experimental conditions of the sample. As a result, the analysis system of this embodiment can assist users in investigating the impact of experimental conditions on the sample.

[0122] (Analyze the system's actions)

[0123] Next, the operation of the analysis system, namely the analysis method involved in the first embodiment, will be described in detail. Figure 4 This is a flowchart illustrating the operation of the analysis system according to the first embodiment. Reference will be made appropriately in the following description. Figures 1-3 .

[0124] exist Figure 4 In the processing steps, the processor 110 of the server 100 reads the program stored in the storage device 130 into the memory 120 and executes it, thereby functioning as the data acquisition unit 111, text acquisition unit 112, extraction unit 113, and analysis unit 114.

[0125] In the analysis method according to this embodiment, firstly, the processor 110 acquires the analysis target data (step ST1). More specifically, in step ST1, the processor 110 acquires the analysis target data as at least one of measurement data obtained by measuring a sample and numerical data related to the sample. In other words, in step ST1, the processor 110 functions as an analysis target data acquisition unit 111.

[0126] For example, in step ST1, the processor 110 receives a selection of analysis object data from the user terminal 200 to perform the analysis. Furthermore, the processor 110 retrieves the selected analysis object data from the storage device 130.

[0127] Next, processor 110 acquires text data (step ST2). More specifically, in step ST2, processor 110 acquires text data related to the sample. In other words, in step ST2, processor 110 functions as a text acquisition unit 112.

[0128] For example, in step ST2, the processor 110 accepts the selection of text data from the user terminal 200. Furthermore, the processor 110 can retrieve the selected text data from the storage device 130. Additionally, in step ST2, the processor 110 can also retrieve from the storage device 130 the text data stored in association with the analysis object data obtained in step ST1.

[0129] The execution order of steps ST1 and ST2 can be reversed. Alternatively, steps ST1 and ST2 can be executed in parallel.

[0130] Next, processor 110 extracts information from the text data (step ST3). More specifically, in step ST3, processor 110 extracts information related to the experimental content from the text data. In other words, in step ST3, processor 110 functions as extraction unit 113.

[0131] As described above, the extraction information involved in this embodiment may include at least one of information related to the experimental conditions of the sample and information related to the physical properties of the sample.

[0132] Finally, the processor 110 performs analysis based on the data of the object being analyzed and the extracted information (step ST4), and the analysis system 1 concludes a series of actions. In other words, in step ST4, the processor 110 functions as the analysis unit 114.

[0133] Thus, in the analysis method according to this embodiment, analysis is performed using information extracted from documents created by the experimenter to record experimental content. As a result, the analysis method according to this embodiment can flexibly utilize the insights gained by the experimenter through experiments.

[0134] As described above, the analysis system involved in this embodiment extracts information from text data and performs analysis based on the extracted information.

[0135] Based on this configuration, the analysis system according to this embodiment can extract the insights gained by the experimenter through experiments into textual data. As a result, the analysis system according to this embodiment can flexibly utilize the insights gained by the experimenter during experiments.

[0136] <Second Implementation>

[0137] (Analysis of the system's structure)

[0138] Hereinafter, the second embodiment of this disclosure will be described in detail with reference to the accompanying drawings. The analysis system according to this embodiment is an application example of the analysis system according to the first embodiment.

[0139] Figure 5 This is a block diagram illustrating the configuration of the server according to the second embodiment. The analysis system according to this embodiment differs from the first embodiment in that the server 100 includes a receiving unit 115; otherwise, the configuration is the same as that of the analysis system according to the first embodiment.

[0140] In this embodiment, the extraction unit 113 extracts items related to at least one of information related to the experimental conditions of the sample and information related to the physical properties of the sample. Furthermore, the extraction unit 113 of this embodiment prompts the user with the extracted items.

[0141] For example, the extraction unit 113 prompts the user with the extracted items by displaying a list of extracted items on the user terminal 200.

[0142] The receiving unit 115 receives the user's designated project information. The receiving unit 115 outputs identification information of the designated project to the analysis unit 114.

[0143] In this embodiment, the analysis unit 114 obtains identification information of the designated item accepted from the acceptance unit 115. Furthermore, the analysis unit 114 in this embodiment evaluates the correlation between the designated item accepted and the data of the analysis object.

[0144] Specifically, the analysis unit 114 in this embodiment can perform principal component analysis on the analysis object data and perform regression analysis between the principal component scores obtained as a result of the principal component analysis and the items contained in the extraction information extracted by the extraction unit 113.

[0145] Furthermore, the analysis unit 114 can determine the principal component scores and the principal components corresponding to the principal component scores by providing user prompts.

[0146] For example, if the item specified by the user is related to the physical properties of the sample, according to the above configuration, the analysis system according to this embodiment can prompt the user with analysis object data that is highly correlated with the physical properties of the sample.

[0147] As a result, the analysis system described in this embodiment can assist in the examination of physical properties of interest to the user.

[0148] Furthermore, for example, if the user-specified item is related to the experimental conditions of the sample, the analysis system according to the above configuration can prompt the user with analysis object data that is highly correlated with the experimental conditions of the sample.

[0149] As a result, the analysis system described in this embodiment can assist users in examining the impact of experimental conditions on samples. Furthermore, as a result, the analysis system described in this embodiment can assist in examinations related to experimental conditions of interest to the user.

[0150] <Third Implementation>

[0151] (Analysis of the system's structure)

[0152] Hereinafter, the third embodiment of this disclosure will be described in detail with reference to the accompanying drawings. The analysis system according to this embodiment is an application example of the analysis system according to the first embodiment.

[0153] Figure 6 This is a block diagram illustrating the configuration of the server according to the third embodiment. The analysis system according to this embodiment differs from the first embodiment in that the aforementioned server 100 has an unknown data acquisition unit 116; otherwise, the configuration is the same as the analysis system according to the first embodiment.

[0154] In this embodiment, the extraction unit 113 extracts information related to the physical properties of the sample. In other words, the extracted information in this embodiment includes information related to the physical properties of the sample.

[0155] The unknown data acquisition unit 116 acquires unknown data from the analysis object data, which is an unknown sample. The unknown data acquisition unit 116 outputs unknown data to the analysis unit 114.

[0156] The analysis object data for the unknown sample mentioned here can be analysis object data obtained by actually measuring the unknown sample, or it can be simulated data created by the user. In other words, the unknown sample in this embodiment can be a real sample or a non-real sample simulated by the user.

[0157] Furthermore, in cases where unknown samples actually exist, the unknown data may be, for example, a portion of the analysis object data acquired by the analysis object data acquisition unit 111. In this case, the unknown sample involved in this embodiment can be defined as a sample in which details regarding some physical properties are unclear.

[0158] In this embodiment, the analysis unit 114 predicts the physical properties of the unknown sample based on unknown data, analysis object data, and extracted information.

[0159] In this scenario, the analysis unit 114 can first extract feature quantities from the data of the object to be analyzed, and then perform regression analysis between the extracted feature quantities and the information related to physical properties contained in the extracted information. Furthermore, the analysis unit 114 can predict the physical properties of unknown samples based on the unknown data and the results of the regression analysis.

[0160] As a specific example of the above, the analysis unit 114 can perform principal component analysis on the analysis target data and unknown data to obtain principal components and principal component scores. Next, the analysis unit 114 can create mapping data with principal component scores and values ​​representing the degree of physical properties of the samples as axes, and plot points representing each sample on this mapping data. Furthermore, the analysis unit 114 can predict the degree of physical properties of the unknown samples by referring to the principal component scores of the unknown data and the mapping data.

[0161] Based on the above configuration, the analysis system according to this embodiment is able to predict the physical properties of unknown samples.

[0162] <Other Implementation Methods>

[0163] The display system described in the first and second embodiments is implemented as a server 100, but the configuration of the display system disclosed herein is not limited to this. For example, the analysis system disclosed herein may also be implemented by two or more computer devices. Furthermore, the analysis system disclosed herein may also be implemented as a user terminal 200, in part or in whole.

[0164] The present invention has been described above based on the above embodiments. However, the present invention is not limited to the above embodiments. Of course, it includes various modifications, alterations and combinations that can be made by those skilled in the art within the scope of the invention of the technical solution of this application.

[0165] Based on the disclosure described herein, it will be apparent that embodiments of this disclosure can be modified in various ways. Such modifications should not be considered as departing from the spirit and scope of this disclosure, and all such modifications are intended to be included within the scope of the technical solutions by those skilled in the art.

Claims

1. An analysis system, wherein, have: The analysis object data acquisition unit acquires analysis object data, which includes at least one of measurement data obtained by measuring a sample and numerical data related to the sample; The text acquisition unit acquires text data that records the experimental content of the sample. The extraction unit extracts relevant information from the text data related to the experimental content. as well as The analysis unit performs analysis based on the data of the object to be analyzed and the extracted information.

2. The analysis system according to claim 1, wherein, The extracted information includes at least one of information related to the experimental conditions of the sample and information related to the physical properties of the sample.

3. The analysis system according to claim 2, wherein, The extraction unit extracts at least one of the items related to the experimental conditions of the sample and the physical properties of the sample.

4. The analysis system according to claim 3, wherein, When the extraction unit extracts items related to the physical properties of the sample, it quantifies the degree of each item for each sample.

5. The analysis system according to claim 4, wherein, When the extraction unit extracts items related to the physical properties of the sample, it quantifies the degree of the physical property by assigning a first predetermined value to samples that exhibit the physical property and a second predetermined value to samples that do not exhibit the physical property.

6. The analysis system according to any one of claims 3 to 5, wherein, It also has a designated reception department for users to handle the projects. The analysis department evaluates the correlation between the data of the designated project and the data of the analysis object.

7. The analysis system according to any one of claims 1 to 5, wherein, It also includes an unknown data acquisition unit, wherein the unknown data is the analysis object data of an unknown sample. The extracted information includes information related to the physical properties of the sample. The analysis unit predicts the physical properties of the unknown sample based on the unknown data, the data of the analysis object, and the extracted information.

8. The analysis system according to any one of claims 1 to 5, wherein, The text acquisition unit acquires image data of a paper document containing the experimental content. The text written on the paper is converted into text data by performing image processing on the acquired image data. The converted text data is used as text data recording the experimental content of the sample.

9. An analytical method, wherein, The analytical object data is obtained, which includes at least one of measurement data obtained by measuring the sample and numerical data related to the sample. Obtain text data related to the sample. Extract information related to the sample from the text data. The analysis is performed based on the data of the object being analyzed and the extracted information.

10. A program product, wherein, To make the computer perform the following actions: The analytical object data is obtained, which includes at least one of measurement data obtained by measuring the sample and numerical data related to the sample. Obtain text data related to the sample. Extract information related to the sample from the text data. The analysis is performed based on the data of the object being analyzed and the extracted information.

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