Information processing device, information processing method, and information processing program

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program that can generate spectral data on a material for which spectral data is not measured.SOLUTION: A server 14 sets each of pieces of spectral data that are measured from each of a plurality of materials as vector data. The server 14 generates two-dimensional map data having each of a plurality of pieces of vector data projected to two dimensions as each of a plurality of plot points by applying a dimension reduction technique for the plurality of pieces of vector data. The server 14 identifies unknown data representing a plot point being different from a plot point already existing on two-dimensional map data. The server 14 converts the unknown data to spectral data. The server 14 outputs the converted spectral data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Patent Document 1 discloses an electronic device and its control method that achieves both analytical accuracy and efficiency when analyzing an object using multiple spectral components. This electronic device selects one of multiple parameters generated by machine learning, and uses the selected parameter to analyze spectral data indicating the spectral intensities of multiple spectral components in response to light reflected from the food being analyzed (e.g., [Abstract]). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-176951 Summary of the Invention [Problem to be solved by the invention]

[0004] Generally, when an analysis of a material is performed, it is necessary to measure the spectral data of the material, and various analyses of the material are performed using the measured spectral data.

[0005] On the other hand, there are cases where a user wants to know, for example, what kind of spectral data corresponds to a material that has the desired performance. In this case, if the material that has the desired performance is unknown and its spectral data has not yet been measured, it is necessary to obtain the spectral data of the material in some way.

[0006] The electronic device of Patent Document 1 selects one of multiple parameters generated by machine learning, and uses the selected parameter to analyze spectral data indicating the spectral intensities of multiple spectral components in response to light reflected from the food being analyzed.

[0007] However, the electronic device of Patent Document 1 is not intended to acquire spectral data of materials for which spectral data has not been measured.

[0008] An object of the present disclosure is to provide an information processing device, an information processing method, and an information processing program that are capable of generating spectral data for a material for which spectral data has not been measured. [Means for solving the problem]

[0009] An information processing device according to a first aspect includes a setting unit that sets each of spectral data measured from a plurality of materials as vector data, a generating unit that applies a dimension reduction technique to the plurality of vector data to generate two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points, an identifying unit that identifies unknown data representing plot points different from plot points already present on the two-dimensional map data, a converting unit that converts the unknown data into spectral data, and an output unit that outputs the converted spectral data. The information processing device according to the first aspect can generate spectral data for a material whose spectral data has not yet been measured. Specifically, when a material having a desired performance value is anticipated, the spectral data of the unknown material expected to have the desired performance value can be obtained without actually measuring the spectral data of the unknown material.

[0010] The spectral data of the information processing device according to the second aspect is data generated by frequency analysis of an image of a material, the conversion unit converts the unknown data into the spectral data and generates an image of the unknown material from the converted spectral data, and the output unit outputs the converted spectral data and the generated image of the material. According to the information processing device according to the second aspect, when a material having a desired performance value is anticipated, an image of the unknown material can be obtained without actually capturing an image of the unknown material expected to have the desired performance value. Furthermore, the user can recognize what kind of image the material having the desired performance value looks like. Note that in the second aspect, multiple sets of training data in which spectral data and images are associated may be prepared, a trained model may be generated based on the multiple sets of training data, and an image may be generated using the trained model. In this case, a known machine learning model for image generation is used as the trained model. The trained model may be generated, for example, by training the machine learning model using a deep learning algorithm.

[0011] In the information processing device according to the third aspect, the unknown data is data on the two-dimensional map data specified by a user, the conversion unit converts the unknown data specified by the user into spectral data, and the output unit displays the converted spectral data on a display unit. According to the information processing device according to the third aspect, it is possible to obtain spectral data of an unknown material corresponding to a plot point specified by a user operation.

[0012] An information processing method according to a fourth aspect is an information processing method in which a computer executes the following processes: setting each of spectral data measured from each of a plurality of materials as vector data, applying a dimension reduction technique to the plurality of vector data to generate two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points, identifying unknown data representing plot points different from plot points already present on the two-dimensional map data, converting the unknown data into spectral data, and outputting the converted spectral data.The information processing method according to the fourth aspect makes it possible to generate spectral data for materials for which spectral data has not been measured.

[0013] An information processing program according to a fifth aspect causes a computer to execute the following process: setting each of spectral data measured from each of a plurality of materials as vector data, applying a dimension reduction technique to the plurality of vector data to generate two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points, identifying unknown data representing plot points different from plot points already present on the two-dimensional map data, converting the unknown data into spectral data, and outputting the converted spectral data.The information processing program according to the fifth aspect allows spectral data to be generated for materials whose spectral data has not been measured. [Effects of the Invention]

[0014] As described above, the present disclosure has the advantage of being able to generate spectral data for materials for which spectral data has not been measured. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a block diagram showing an example of a functional configuration of an information processing system 10 according to an embodiment. [Figure 2] FIG. 2 is a diagram for explaining two-dimensional map data and spectrum data. [Figure 3] FIG. 2 is a diagram illustrating an example of the configuration of a server and a computer of a user terminal according to the embodiment. [Figure 4] 10 is a flowchart illustrating an example of processing performed by a server according to the embodiment. [Figure 5] FIG. 10 is a diagram for explaining generation of an image from spectral data. [Figure 6] FIG. 10 is a diagram for explaining a process of converting a portion of spectral data that has a high correlation with performance into an image. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, an information processing system according to an embodiment will be described with reference to the drawings.

[0017] Fig. 1 is a block diagram showing an example of the functional configuration of an information processing system 10 according to an embodiment. As shown in Fig. 1, the information processing system 10 includes a user terminal 12 and a server 14, which is an example of an information processing device. The user terminal 12 and the server 14 are connected to each other so as to be able to communicate with each other via a network 16 such as the Internet.

[0018] The information processing system 10 of this embodiment projects each of the spectral data measured from each of a plurality of materials onto two-dimensional map data. Note that one plot point on the two-dimensional map data corresponds to one spectral data, and one spectral data corresponds to one material. The spectral data is, for example, data measured by various methods.

[0019] The information processing system 10 of this embodiment then converts unknown data representing plot points that are different from plot points already present on the two-dimensional map data and are specified by the user into spectral data. This makes it possible to generate spectral data for materials for which spectral data has not yet been measured. This will be described in detail below.

[0020] (user terminal)

[0021] The user terminal 12 is operated by a user. Functionally, the user terminal 12 includes a control unit 120 and a display unit 122, as shown in FIG.

[0022] The control unit 120 controls the operation of the user terminal 12. The display unit 122 displays various types of information in accordance with the control of the control unit 120.

[0023] (server)

[0024] As shown in FIG. 1, the server 14 functionally includes an acquisition unit 140, a data storage unit 141, a setting unit 142, a generation unit 144, an identification unit 146, a conversion unit 148, and an output unit 150.

[0025] The acquiring unit 140 acquires a plurality of pieces of spectrum data transmitted from the user terminal 12. Then, the acquiring unit 140 stores the plurality of pieces of spectrum data in the data storage unit 141.

[0026] The data storage unit 141 stores each of the spectrum data measured from each of a plurality of materials.

[0027] The setting unit 142 sets each of the plurality of spectral data as vector data. For example, when the spectral data is illustrated on a graph in which the horizontal axis represents frequency and the vertical axis represents spectral values, vector data having, as components, spectral values corresponding to each frequency is set.

[0028] The generation unit 144 applies principal component analysis, which is an example of a dimension reduction technique, to the plurality of vector data. Then, based on the result of applying the principal component analysis, the generation unit 144 generates two-dimensional map data in which each of the plurality of vector data is projected into two dimensions as each of a plurality of plot points. The generation unit 144 transmits the two-dimensional map data and related information to the user terminal 12.

[0029] Fig. 2 is a diagram showing an example of two-dimensional map data and related information. As the related information in Fig. 2, principal component values (hereinafter also simply referred to as "PC values") displayed as bars and spectrum data corresponding to the principal component values are shown. A user operates his / her user terminal 12 to display the two-dimensional map data and related information as shown in Fig. 2 on the display unit 122 of the user terminal 12.

[0030] In the two-dimensional map data in Figure 2(A), "pc1" on the horizontal axis represents the value of the first principal component, and "pc2" on the vertical axis represents the value of the second principal component. Each plot point in Figure 2(A) corresponds to a single piece of spectral data. Also, as shown in Figure 2(A), the color intensity of the plot point represents the performance of the material corresponding to that plot point. Specifically, when the color of the plot point is white, the material has poor performance, and when the color of the plot point is black, the material has good performance. Therefore, the further to the right a plot point is in Figure 2(A), the higher the performance of the material. Pressing the "Back" button in Figure 2(A) will take you to another screen.

[0031] 2(B), 2(C), and 2(D) are diagrams showing an example of principal component values displayed as bars and the spectral data corresponding to those principal component values. On the left side of FIGS. 2(B), 2(C), and 2(D), 10 PC values, pc1 to pc10, are displayed as bars. On the right side of FIGS. 2(B), 2(C), and 2(D), spectral data corresponding to the PC values on the left side are shown. The conversion from PC values to spectral data is uniquely determined. The "0SET" button shown in FIGS. 2(B), 2(C), and 2(D) is a button for resetting the 10 PC values, pc1 to pc10, to zero. The "RESET" button shown in FIGS. 2(B), 2(C), and 2(D) is a button for resetting the 10 PC values, pc1 to pc10, to their original values.

[0032] The two-dimensional map data in FIG. 2(A) is configured so that the position of each plot can be adjusted. Therefore, for example, a user operates their own user terminal 12 to move a plot point P of the two-dimensional map data shown in FIG. 2(A) on the two-dimensional map data. The user operates their own user terminal 12 to move a plot point already existing on the two-dimensional map data, thereby specifying a plot point different from the already existing plot point. The plot point different from the already existing plot point is unknown data for which no spectral data exists. The unknown data is also data on the two-dimensional map data specified by the user.

[0033] For example, the user moves plot point P shown in Figure 2(A) to plot point PU1, which corresponds to unknown, unmeasured spectral data. By moving plot point P on the two-dimensional map data to plot point PU1, the value of pc1 shown in Figure 2(B) ("1.75" in Figure 2(B)) changes to the value of pc1 shown in Figure 2(D) ("2.16" in Figure 2(D)).

[0034] Also, for example, the user moves plot point P shown in Fig. 2 to unknown plot point PU2. By moving plot point P on the two-dimensional map data to plot point PU2, the value of pc1 shown in Fig. 2(A) ("1.75" in Fig. 2(A)) changes to the value of pc1 shown in Fig. 2(C) ("0.41" in Fig. 2(C)).

[0035] A change in the PC value also changes the waveform of the spectral data shown on the right side of Figure 2. Note that the horizontal axis of the spectral data graph represents, for example, frequency, and the vertical axis represents the spectral value. For example, a change in the value of pc1 causes the spectral data WA to change to the spectral data WB shown in Figure 2(C). A change in the value of pc1 also causes the spectral data WA to change to the spectral data WC shown in Figure 2(D).

[0036] In this manner, the server 14 of this embodiment converts unknown data corresponding to plot points different from plot points already present on the two-dimensional map data (specifically, points designated by the user as described above) into spectral data. The server 14 then outputs the converted spectral data as shown on the right side of Figures 2(B), (C), and (D). This makes it possible to generate spectral data for unknown materials whose spectral data has not yet been measured.

[0037] For example, as shown in Figure 2(A), if the higher the pc1 value of the spectral data, the higher the performance of the material, the user can check pseudo-spectral data of the high-performance material by specifying plot point PU1. Therefore, if the user specifies a plot point that is expected to be a high-performance material, spectral data corresponding to that plot point will be generated, allowing the user to know what kind of spectrum will achieve high performance.

[0038] The PC value displayed as a bar in Fig. 2 may be adjustable. For example, the waveform of the spectrum data may be changed by changing the PC value as indicated by arrows D1 and D2 in Fig. 2. In this case, the waveform of the spectrum data changes in response to the adjustment of the principal component value (PC value) displayed as a bar, allowing the user to understand the meaning of the principal component value (PC value).

[0039] The identifying unit 146 identifies unknown data representing plot points that are different from plot points that already exist on the two-dimensional map data generated by the generating unit 144. Specifically, the identifying unit 146 identifies unknown data representing plot points designated by the user as described above (for example, PC values corresponding to the plot points).

[0040] The conversion unit 148 converts the unknown data identified by the identification unit 146 into spectral data. Note that the conversion from unknown data to spectral data can be realized by a known method.

[0041] The output unit 150 outputs the spectrum data and each PC value converted by the conversion unit 148. The data output from the output unit 150 is transmitted to the user terminal 12.

[0042] The user operates the user terminal 12 and checks the information displayed on the display unit 122 of the user terminal 12. For example, the user refers to the two-dimensional map data and related information as shown in Fig. 2 to check the shape of the spectrum data corresponding to the unknown data specified by the user (for example, pseudo spectrum data corresponding to a high-performance material).

[0043] The user terminal 12 and the server 14 can be realized, for example, by a computer 50 as shown in Fig. 3. The computer 50 that realizes the user terminal 12 and the server 14 includes a CPU 51, a memory 52 as a temporary storage area, and a non-volatile storage unit 53. The computer 50 also includes an input / output interface (I / F) 54 to which input / output devices (not shown) are connected, and a read / write (R / W) unit 55 that controls reading and writing of data from and to a recording medium 59. The computer also includes a network I / F 56 that is connected to a network such as the Internet. The CPU 51, memory 52, storage unit 53, input / output I / F 54, R / W unit 55, and network I / F 56 are connected to one another via a bus 57.

[0044] The storage unit 53 can be realized by a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The storage unit 53 as a storage medium stores a program for causing the computer to function. The CPU 51 reads the program from the storage unit 53, loads it into the memory 52, and sequentially executes the processes contained in the program.

[0045] Next, the operation of the information processing system 10 according to the embodiment will be described.

[0046] For example, the user inputs a plurality of pieces of spectral data, which are data obtained by performing predetermined measurement processing on a plurality of materials, into the user terminal 12 that the user operates.

[0047] The control unit 120 of the user terminal 12 receives a plurality of pieces of spectrum data in response to an operation by the user, and then transmits the plurality of pieces of spectrum data to the server 14 in response to the operation by the user.

[0048] When a plurality of pieces of spectral data are transmitted from the user terminal 12 to the server 14, the acquisition unit 140 of the server 14 acquires the plurality of pieces of spectral data transmitted from the user terminal 12. Then, the acquisition unit 140 stores the plurality of pieces of spectral data in the data storage unit 141.

[0049] When the server 14 receives a predetermined instruction signal transmitted from the user terminal 12, it executes an information processing routine shown in FIG.

[0050] In step S100, the setting unit 142 of the server 14 reads out each of the plurality of spectral data stored in the data storage unit 141, and sets each of the plurality of spectral data as vector data.

[0051] In step S102, the generation unit 144 of the server 14 applies principal component analysis, which is an example of a dimension reduction technique, to the plurality of vector data. Then, based on the results of applying the principal component analysis, the generation unit 144 of the server 14 generates two-dimensional map data in which each of the plurality of vector data is projected into two dimensions as a plurality of plot points. The generation unit 144 of the server 14 transmits the two-dimensional map data and related information as shown in FIG. 2 to the user terminal 12.

[0052] The control unit 120 of the user terminal 12 receives the 2D map data and related information transmitted from the user terminal 12 in step S102. The user operates their own user terminal 12 to display the 2D map data and related information shown in FIG. 2 on the display unit 122. The user then operates their own user terminal 12 to move plot points already present on the 2D map data, thereby specifying plot points different from the already existing plot points. Plot points different from the already existing plot points are unknown data for which no spectral data exists. The unknown data specified by the user is transmitted from the user terminal 12 to the server 14.

[0053] In step S104, the identification unit 146 of the server 14 identifies the unknown data transmitted from the user terminal 12. Specifically, the identification unit 146 identifies the unknown data representing the plot point designated by the user as described above (for example, the PC value corresponding to the plot point).

[0054] In step S106, the conversion unit 148 of the server 14 converts the unknown data identified in step S104 into spectral data.

[0055] In step S108, the output unit 150 of the server 14 transmits the spectral data of the unknown data obtained in step S106 and other information (for example, the two-dimensional map data as shown in FIG. 2(A) and the PC values in bar format on the left side of FIGS. 2(B), 2(C), and 2(D)) to the user terminal 12.

[0056] The user operating the user terminal 12 checks the data output from the server 14 and confirms what the spectral data of the material he or she desires is like.

[0057] As described above, the server of the information processing system according to the embodiment sets each of the spectral data measured from each of the plurality of materials as vector data. The server applies a dimension reduction technique to the plurality of vector data to generate two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points. The server identifies unknown data representing plot points different from existing plot points on the two-dimensional map data. The server converts the unknown data into spectral data. The server then outputs the converted spectral data. This allows spectral data for materials for which spectral data has not been measured to be generated. Specifically, when a material having a desired performance value is anticipated, the spectral data of the unknown material can be obtained without actually measuring the spectral data of the unknown material expected to have that performance value. The user can also recognize what kind of spectral data the material having the desired performance value would be. The server can also obtain spectral data for the unknown material corresponding to the plot points specified by the user.

[0058] Furthermore, although the processing performed by the computer 50 in each of the above embodiments has been described as software processing performed by executing a program, this is not limited to this. For example, the processing may be performed by hardware such as a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array). Alternatively, the processing may be a combination of both software and hardware. Furthermore, if the processing is software, the program may be stored in various storage media and distributed.

[0059] Furthermore, the present invention is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit of the present invention.

[0060] For example, the spectral data in the above embodiment may be spectral data generated from an image of a material. For example, the spectral data may be data generated by frequency analyzing an image obtained by capturing an image of the material using an electron microscope or the like. In this case, the conversion unit 148 of the server 14 of the information processing system 10 converts the unknown data into spectral data and generates an image of the unknown material from the converted spectral data. The output unit 150 then outputs the converted spectral data and the generated image of the material.

[0061] FIG. 5 is a diagram illustrating an example of generating an image of an unknown material from converted spectral data. As shown in the upper part of FIG. 5, consider the case where a user moves a plot point P already present on two-dimensional map data to a plot point PU corresponding to unknown data. In this case, the spectral data generated from an image IM1 of an actually captured material corresponds to plot point P, and the unknown data for which an image has not been captured corresponds to plot point PU. Image IM1 shown in the middle part of FIG. 5 is an image of an actually captured material, and image IM2 is an image of an unknown material. Image IM2 is an example of an image generated by the conversion unit 148 of the server 14 of the information processing system 10.

[0062] Also, the PC values in bar format are shown on the left side of the lower part of Fig. 5, and the spectral data generated from the actual image IM (solid line in Fig. 5) and the spectral data corresponding to the unknown data (dashed line in Fig. 5) are shown on the right side of the lower part of Fig. 5. The horizontal axis of the spectral data graph represents, for example, frequency, and the vertical axis represents the spectral value.

[0063] In this case, the server 14 of the information processing system 10 identifies the plot point PU corresponding to the unknown data and calculates the spectral data (dashed line in FIG. 5) corresponding to the unknown data. As described above, the conversion from the PC value corresponding to the unknown data to the spectral data can be uniquely determined. On the other hand, the conversion from the spectral data to the unknown image IM2 cannot be uniquely determined.

[0064] Therefore, when performing the conversion process from spectral data to an image, the server 14 of the information processing system 10 randomly generates multiple image candidates based on the image IM1 and selects an image candidate that is closest to the spectral data from the image candidates. For example, the server 14 generates multiple image candidates by randomly changing the pixel values that make up the image IM1 corresponding to the plot point P specified by the user. The server 14 then performs frequency analysis on each of the multiple image candidates to generate spectral data for each of the multiple image candidates. The server 14 then selects an image candidate for which the difference between the spectral data for each of the multiple image candidates and the spectral data corresponding to the plot point PU corresponding to the unknown data is small (or equal to or less than a threshold) as the image IM2 corresponding to the unknown data.

[0065] Alternatively, for example, the server 14 of the information processing system 10 may prepare multiple pieces of training data in which spectral data and images are associated with each other, generate a trained model based on the multiple pieces of training data, and generate an image using the trained model. In this case, the spectral data indicated by the solid line in FIG. 5 and image IM1 are set in advance as training data. Furthermore, for example, a known machine learning model for image generation is used as the trained model. The trained model is generated by training the machine learning model using, for example, a deep learning algorithm. For example, the server 14 of the information processing system 10 inputs the spectral data indicated by the dashed line in FIG. 5 into the trained model to generate image IM2 corresponding to the unknown spectral data.

[0066] This allows users to predict a material with a desired performance value and obtain an image of the unknown material without actually capturing an image of the material. Furthermore, users can recognize what kind of image the material with the desired performance value looks like. Furthermore, by generating an image from spectral data, users can understand the relationship between the PC value of the spectral data and the image.

[0067] For example, as shown in FIG. 6, only a portion of the spectral data that is correlated with the performance of the material may be converted into an image. Consider a case where the PC values obtained by the principal component analysis of FIG. 6 are positively correlated with a certain performance of the material. For example, only a partial region RX1 of the spectral data Sx, which corresponds to a portion of the high-dimensional vector data PC values with high values, may be converted into an image IMX. Alternatively, only a partial region RY1 of the spectral data Sx, which corresponds to a portion of the high-dimensional vector data PC values with low values, may be converted into an image. By generating an image from a portion of the spectral data, the user can also understand the relationship between the portion of the spectral data and the image. This allows the user to understand what kind of image will achieve the desired performance.

[0068] Furthermore, the output unit 150 may display each dimension value (e.g., PC value of principal component analysis) corresponding to the unknown data as a bar, and the conversion unit 148 may convert the PC value into spectral data in response to a user operation (e.g., operation of the bar) on each dimension value (e.g., PC value of principal component analysis) displayed as a bar. This allows the spectral data of the unknown material to be displayed in response to a user operation on each dimension value (e.g., PC value of principal component analysis) of the dimension reduction result displayed on the display unit 122 of the user terminal 12. Furthermore, the user can understand the meaning of each dimension value (e.g., PC value of principal component analysis) of the dimension reduction result.

[0069] Alternatively, the conversion unit 148 may generate spectral data corresponding to the average of plot points already present in the two-dimensional map data in response to a user operation (e.g., operating a bar), and the output unit 150 may output the spectral data corresponding to the average. This allows the average of multiple spectral data already displayed in the two-dimensional map data to be displayed in response to a user operation.

[0070] Furthermore, the conversion unit 148 may perform a regression analysis between the performance values of the material (e.g., mechanical strength, surface reflectance, transparency, etc.) and the values of each dimension (e.g., PC values of the principal component analysis) obtained by applying principal component analysis, which is an example of a dimension reduction technique. In this case, the output unit 150 may further output the regression analysis results obtained by the conversion unit 148. This makes it possible to obtain the regression analysis results between the performance values of the material and the values of each dimension (e.g., PC values of the principal component analysis) of the dimension reduction results.

[0071] Furthermore, each plot point on the two-dimensional map data may be assigned a color (e.g., the higher the performance value, the redder the color) corresponding to the performance value of the material (e.g., mechanical strength, surface reflectance, transparency, etc.). By assigning a color corresponding to the performance value of the material to the plot point, the user can specify the plot point using the color as a clue to obtain spectral data of the material with the desired performance value. [Explanation of symbols]

[0072] 10 Information Processing Systems 12 User terminal 14 Servers 50 Computers 53 Storage section 140 Acquisition Department 141 Data storage unit 142 Setting section 144 Generation part 146 Specific part 148 Conversion Unit 150 Output section

Claims

1. a setting unit that sets each of the spectrum data measured from each of the plurality of materials as vector data; a generation unit that generates two-dimensional map data by applying a dimension reduction technique to the plurality of vector data to project each of the plurality of vector data onto two dimensions as a plurality of plot points; an identifying unit that identifies unknown data representing plot points different from plot points already existing on the two-dimensional map data; a conversion unit that converts the unknown data into spectral data; an output unit that outputs the converted spectral data; Equipped with the spectral data is data generated by frequency analysis of an image of a material; the conversion unit converts the unknown data into the spectral data and generates an image of the unknown material from the converted spectral data; the output unit outputs the converted spectral data and the generated image of the material. Information processing device.

2. the unknown data is data on the two-dimensional map data designated by a user; the conversion unit converts the unknown data designated by a user into spectral data; the output unit causes a display unit to display the converted spectral data. The information processing device according to claim 1 .

3. Each of the spectral data measured from each of the plurality of materials is set as vector data; generating two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points by applying a dimension reduction technique to the plurality of vector data; Identifying unknown data representing plot points different from plot points already present on the two-dimensional map data; converting the unknown data into spectral data; outputting the transformed spectral data; An information processing method in which a process is executed by a computer, the spectral data is data generated by frequency analysis of an image of a material; converting the unknown data into spectral data includes converting the unknown data into the spectral data and generating an image of the unknown material from the converted spectral data; outputting the transformed spectral data and the generated image of the material; Information processing methods.

4. Each of the spectral data measured from each of the plurality of materials is set as vector data; generating two-dimensional map data in which each of the plurality of vector data is projected onto two dimensions as a plurality of plot points by applying a dimension reduction technique to the plurality of vector data; Identifying unknown data representing plot points different from plot points already present on the two-dimensional map data; converting the unknown data into spectral data; outputting the transformed spectral data; An information processing program for causing a computer to execute a process, the spectral data is data generated by frequency analysis of an image of a material; converting the unknown data into spectral data includes converting the unknown data into the spectral data and generating an image of the unknown material from the converted spectral data; outputting the transformed spectral data and the generated image of the material; Information processing program.

Images