Information processing apparatus, 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 a control method thereof that achieves both analytical accuracy and efficiency when analyzing an object using multiple spectral components. The 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 upon receiving light reflected from the food to be analyzed (for example, [Abstract]). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-176951 A Summary of the Invention [Problem to be solved by the invention]

[0004] In general, when an analysis is performed on a certain material, it is necessary to measure the spectrum data of the material. When an analysis is performed on a material, various analyses are performed on the material using the measured spectrum 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 having a desired performance. In this case, if a material having the desired performance is unknown and its spectral data has not 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 obtain spectral data of materials whose 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 capable of generating spectral data for a material for which spectral data has not been measured. [Means for solving the problem]

[0009] The information processing device according to the first aspect is an information processing device including: a setting unit that sets each of the spectral data measured from each of a plurality of materials as vector data; a generating unit that 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 by applying a dimension reduction technique to the plurality of vector data; an identifying unit that identifies unknown data representing plot points different from plot points already existing 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. According to the information processing device according to the first aspect, it is possible to generate spectral data of a material whose spectral data has not been measured. Specifically, when a material having a performance value desired by a user is expected, it is possible to generate spectral data of an unknown material that is expected to have the performance value without actually measuring the spectral data of the unknown material. Spectral data for the unknown material can be obtained.

[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, and 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 performance value desired by a user is assumed, an image of the unknown material can be obtained without actually capturing an image of the unknown material expected to have the performance value. In addition, the user can recognize what kind of image the image of the material having the performance value desired by the user is. In the second aspect, a plurality of teacher data in which the spectral data and the image are associated with each other may be prepared, a trained model may be generated based on the plurality of teacher 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 is generated, for example, by training the machine learning model using a deep learning algorithm.

[0011] The unknown data of the information processing device according to the third aspect 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 the 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 each of a plurality of plot points, identifying unknown data representing plot points different from plot points already existing on the two-dimensional map data, converting the unknown data into spectral data, and outputting the converted spectral data. According to the information processing method according to the fourth aspect, it is possible to generate spectral data for materials whose spectral data has not been measured.

[0013] An information processing program according to a fifth aspect is an information processing program for causing a computer to execute a process of setting each of the 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 each of a plurality of plot points, identifying unknown data representing plot points different from plot points already existing on the two-dimensional map data, converting the unknown data into spectral data, and outputting the converted spectral data. According to the information processing program according to the fifth aspect, it is possible to generate spectral data for materials whose spectral data has not been measured. Effect of the Invention

[0014] As described above, the present disclosure has an advantage in that it is possible to generate spectral data for a material for which spectral data has not been measured. [Brief description 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. [Diagram 2] FIG. 2 is a diagram for explaining two-dimensional map data and spectrum data. [Diagram 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. [Diagram 5] FIG. 2 is a diagram for explaining generation of an image from spectral data. [Figure 6] FIG. 1 is a diagram for explaining a process of converting a portion of spectral data that is highly correlated with performance into an image. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[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 a 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 the present embodiment projects each of the spectrum data measured from each of the multiple materials onto two-dimensional map data. Note that one plot point on the two-dimensional map data corresponds to one spectrum data, and one spectrum data corresponds to one material. The spectrum data is, for example, data measured by various methods.

[0019] The information processing system 10 of the present embodiment then converts unknown data representing plot points that are different from plot points already present on the two-dimensional map data and that are specified by the user into spectral data. This makes it possible to generate spectral data for materials whose spectral data has not been measured. A specific description will be given 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 multiple spectral data as vector data. For example, when the spectral data is illustrated in a graph in which the horizontal axis represents frequency and the vertical axis represents spectral values, vector data having, as components, each of the spectral values ​​corresponding to each frequency is set.

[0028] The generating 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 generating 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 generating 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. The 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 FIG. 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. One plot point in FIG. 2(A) corresponds to one spectrum data. Also, as shown in FIG. 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 performance of the material is poor, and when the color of the plot point is black, the performance of the material is good. Therefore, the plot point located to the right in FIG. 2(A) corresponds to a material with higher performance. When the "Back" button in FIG. 2(A) is pressed, the screen transitions to another screen.

[0031] FIG. 2(B), (C), and (D) are diagrams showing an example of principal component values ​​displayed as bars and spectrum data corresponding to the principal component values. On the left side of FIG. 2(B), (C), and (D), 10 PC values ​​from pc1 to pc10 are displayed as bars. Also, on the right side of FIG. 2(B), (C), and (D), spectrum data corresponding to the PC values ​​on the left side are shown. Conversion from PC values ​​to spectrum data is uniquely determined. Note that the "0SET" button shown in FIG. 2(B), (C), and (D) is a button for resetting the 10 PC values ​​from pc1 to pc10 to zero. Also, the "RESET" button shown in FIG. 2(B), (C), and (D) is a button for resetting the 10 changed PC values ​​from 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. For this reason, for example, a user operates his / her 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 his / her 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 Fig. 2(A) to plot point PU1 corresponding to unknown spectrum data that has not been measured. By moving plot point P on the two-dimensional map data to plot point PU1, the value of pc1 shown in Fig. 2(B) ("1.75" in Fig. 2(B)) changes to the value of pc1 shown in Fig. 2(D) ("2.16" in Fig. 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 spectrum data shown on the right side of Figure 2. Note that the horizontal axis of the spectrum data graph represents, for example, frequency, and the vertical axis represents the spectrum value. For example, a change in the value of pc1 causes the spectrum data WA to change to the spectrum data WB shown in Figure 2(C). A change in the value of pc1 also causes the spectrum data WA to change to the spectrum data WC shown in Figure 2(D).

[0036] In this manner, the server 14 of the present embodiment converts unknown data corresponding to plot points different from plot points already existing 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 an unknown material whose spectral data has not been measured.

[0037] For example, as shown in Fig. 2(A), in the case where the higher the value of pc1 of the spectrum data is, the higher the performance of the material, the user can confirm pseudo spectrum 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, the spectrum data corresponding to that plot point is generated, so the user can 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 changing or adjusting the principal component value (PC value) displayed as a bar, so that the user can understand the meaning of the principal component value (PC value).

[0039] The identifying unit 146 identifies unknown data representing plot points different from plot points already existing 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 the 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 two-dimensional map data and related information as shown in Fig. 2, and checks 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 an input / output device or the like (not shown) is 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, the memory 52, the storage unit 53 3. The input / output I / F 54, the R / W unit 55, and the network I / F 56 are connected to each other 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, expands it in 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 spectrum data, which are data obtained by performing a predetermined measurement process 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 transmits the plurality of pieces of spectrum data to the server 14 in response to the operation by the user.

[0048] When the plurality 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 spectral data transmitted from the user terminal 12. Then, the acquisition unit 140 stores the plurality 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 multiple pieces of spectral data stored in the data storage unit 141, and sets each of the multiple pieces 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 result 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 each of 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 two-dimensional map data and related information transmitted from the user terminal 12 in step S102. The user operates his / her user terminal 12 to cause the display unit 122 to display the two-dimensional map data and related information shown in FIG. 2. The user then operates his / her user terminal 12 to move plot points already present on the two-dimensional map data to specify plot points different from the already existing plot points. The 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 specified 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 The collected unknown data is converted 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), (C), and (D)) to the user terminal 12.

[0056] A 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 multiple materials as vector data. The server generates two-dimensional map data in which each of the multiple vector data is projected into two dimensions as each of multiple plot points by applying a dimension reduction method to the multiple vector data. The server identifies unknown data representing plot points different from the plot points already existing on the two-dimensional map data. The server converts the unknown data into spectral data. Then, the server outputs the converted spectral data. This makes it possible to generate spectral data of a material whose spectral data has not been measured. Specifically, when a material having a performance value desired by the user is assumed, the spectral data of the unknown material that is expected to have the performance value can be obtained without actually measuring the spectral data of the unknown material. In addition, the user can recognize what kind of data the spectral data of the material having the performance value desired by the user is. In addition, the spectral data of the unknown material corresponding to the plot point specified by the user's operation can be obtained.

[0058] In addition, the processing performed by the computer 50 in each of the above embodiments has been described as software processing performed by executing a program, but 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), and an FPGA (Field-Programmable Gate Array). Alternatively, the processing may be a combination of both software and hardware. In addition, when the processing is performed by 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 and scope 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 imaging a material with 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. Then, the output unit 150 outputs the converted spectral data and the generated image of the material.

[0061] FIG. 5 is a diagram for explaining an example of generating an image of an unknown material from converted spectral data. As shown in the upper part of FIG. 5, consider a case where a user moves a plot point P already existing 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 the plot point P, and unknown data whose image has not been captured corresponds to the plot point PU. The image IM1 shown in the middle part of FIG. 5 is an image of an actually captured material, and the image IM2 is an image of an unknown material. The 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 spectrum data generated from the actual image IM (solid line in Fig. 5) and the spectrum 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 spectrum data graph represents, for example, frequency, and the vertical axis represents the spectrum 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 the server 14 of the information processing system 10 performs a conversion process from the spectral data to an image, for example, the server 14 randomly generates a plurality of image candidates based on the image IM1, and selects an image candidate that is close to the spectral data from the image candidates. For example, the server 14 generates a plurality of image candidates by randomly changing pixel values ​​constituting the image IM1 corresponding to the plot point P designated by the user. The server 14 then performs frequency analysis on each of the plurality of image candidates to generate spectral data for each of the plurality of image candidates. The server 14 then selects an image candidate having a small difference (or a difference equal to or smaller than a threshold) between the spectral data for each of the plurality of image candidates and the spectral data corresponding to the plot point PU corresponding to the unknown data, as the image IM2 corresponding to the unknown data.

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

[0066] This allows a user to obtain an image of an unknown material that is expected to have a desired performance value without actually capturing an image of the unknown material. The user can also recognize what kind of image the material that has the desired performance value looks like. By generating an image from the spectrum data, the user can also understand the relationship between the PC value of the spectrum data and the image.

[0067] For example, as shown in FIG. 6, only a portion of the spectral data that has a correlation with the performance of the material may be converted into an image. For example, consider a case where the PC value obtained by the principal component analysis in FIG. 6 is positively correlated with a certain performance of the material. For example, only a partial region RX1 of the spectral data Sx that corresponds to a portion having a high value among the PC values ​​that are high-dimensional vector data may be converted into an image IMX. Alternatively, only a partial region RY1 of the spectral data Sx that corresponds to a portion having a low value among the PC values ​​that are high-dimensional vector data 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 provide the desired performance.

[0068] Also, the output unit 150 may display the value of each dimension (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 a bar) on the value of each dimension (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 the value of each dimension (e.g., PC value of principal component analysis) of the dimension reduction result displayed on the display unit 122 of the user terminal 12. Also, the user can understand the meaning of the value of each dimension (e.g., PC value of principal component analysis) of the dimension reduction result.

[0069] Furthermore, the conversion unit 148 may generate spectral data corresponding to an 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 obtained by applying principal component analysis, which is an example of a dimension reduction technique (e.g., PC values ​​of principal component analysis). 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 of the dimension reduction results (e.g., PC values ​​of principal component analysis).

[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) according to the performance value of the material (e.g., mechanical strength, surface reflectance, transparency, etc.). By assigning a color according to the performance value of the material to the plot points, the user can specify a plot point using the color as a clue to obtain the spectral data of a material with a desired performance value. [Explanation of symbols]

[0072] 10. Information Processing Systems 12 User terminal 14 Server 50 Computer 53 Memory 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. Each of the spectral data measured from each of the plurality of materials is set as vector data; Applying a dimensionality reduction technique to the plurality of vector data; Displaying the values ​​of each dimension resulting from the application of the dimension reduction technique and the spectral data corresponding to each of the values ​​of each dimension; The displayed value of each of the dimensions is configured to be adjustable; A change in the value of each of the dimensions displayed causes a change in the waveform of the displayed spectrum data. Information processing device.

2. the dimensionality reduction technique is principal component analysis, The values ​​of each dimension are principal component values. The information processing device according to claim 1 .

3. The values ​​of each dimension are displayed as bars, By changing the length of the bar, the value of each dimension changes.

3. The information processing device according to claim 1 or 2.

4. Each of the spectral data measured from each of the plurality of materials is set as vector data; Applying a dimensionality reduction technique to the plurality of vector data; Displaying the values ​​of each dimension resulting from the application of the dimension reduction technique and the spectral data corresponding to each of the values ​​of each dimension; The displayed value of each of the dimensions is configured to be adjustable; A change in the value of each of the dimensions displayed causes a change in the waveform of the displayed spectrum data. An information processing method in which processing is performed by a computer.

5. Each of the spectral data measured from each of the plurality of materials is set as vector data; Applying a dimensionality reduction technique to the plurality of vector data; Displaying the values ​​of each dimension resulting from the application of the dimension reduction technique and the spectral data corresponding to each of the values ​​of each dimension; The displayed value of each of the dimensions is configured to be adjustable; A change in the value of each of the dimensions displayed causes a change in the waveform of the displayed spectrum data. An information processing program for causing a computer to execute processing.

Images