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

Abstract

The learning device (100) has an acquisition unit (120) that acquires learning data (10) including partial shape information (11), which is information indicating a set of elements of the shape of a processing location, and PMI (12), which is information indicating the processing content, an extraction unit (130) that extracts the partial shape information (11) and the PMI (12) from the learning data, and a generation unit (150) that generates a learned model using the partial shape information (11) and the PMI (12).

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] Machining is performed based on design data such as CAD (Computer-Aided Design) data. The design data also includes PMI (Product Manufacturing Information). Drawing inspection is performed on the design data. A technique related to drawing inspection has been proposed (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-105917 Summary of the Invention [Problem to be solved by the invention]

[0004] During drawing inspection, the accuracy of PMI is judged. One possible method for inspecting drawings is to use rule information that indicates the rules. However, there are cases where the accuracy of PMI needs to be judged comprehensively. When a comprehensive judgment is made, the method using rule information cannot be used. Therefore, the question is how to judge the accuracy of PMI comprehensively.

[0005] The purpose of this disclosure is to comprehensively assess the accuracy of PMI. [Means for solving the problem]

[0006] According to an aspect of the present disclosure, there is provided an information processing apparatus, the information processing apparatus, and a learning data including partial shape information that is information indicating a set of elements of the shape of a processing portion and processing content information that is information indicating processing content. and the training data in binary formatan acquisition unit that acquires the a second conversion unit that converts the format of the learning data into an ASCII format; The system includes an extraction unit that extracts the partial shape information and the processing content information from the learning data, and a generation unit that generates a trained model using the partial shape information and the processing content information. [Effects of the Invention]

[0007] According to the present disclosure, the accuracy of PMI can be comprehensively determined. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an information processing system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating hardware included in the learning device of the first embodiment. [Figure 3] 1 is a block diagram showing the functions of a learning device according to a first embodiment. [Figure 4] FIG. 10 is a diagram showing a specific example of partial shape information and PMI according to the first embodiment. [Figure 5] FIG. 3 is a diagram illustrating an example of a conversion table according to the first embodiment. [Figure 6] 10A and 10B are diagrams showing specific examples of partial shape information after conversion and PMI after conversion according to the first embodiment. [Figure 7] FIG. 10 is a diagram showing a specific example of the integration process according to the first embodiment. [Figure 8] 4 is a flowchart illustrating an example of processing executed by the learning device according to the first embodiment. [Figure 9] 1 is a block diagram showing functions of an estimation device according to a first embodiment. [Figure 10] 4 is a flowchart illustrating an example of processing executed by the estimation device according to the first embodiment. [Figure 11] FIG. 10 is a block diagram showing the functions of a learning device according to a second embodiment. [Figure 12] 10 is a flowchart illustrating an example of processing executed by the learning device according to the second embodiment. [Figure 13] FIG. 10 is a block diagram showing the functions of an estimation device according to a second embodiment. [Figure 14] 10 is a flowchart illustrating an example of processing executed by the estimation device according to the second embodiment. [Figure 15] 11 is a flowchart illustrating an example of processing executed by the estimation device according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments will be described with reference to the drawings. The following embodiments are merely examples, and various modifications are possible within the scope of the present disclosure.

[0010] Embodiment 1 FIG. 1 is a diagram illustrating an information processing system according to a first embodiment. The information processing system includes a learning device 100 and an estimation device 200. The learning device 100 and the estimation device 200 may be implemented by the same device. The learning device 100 and the estimation device 200 may be called an information processing device. The learning device 100 is a device that executes an information processing method. The information processing method may be called a learning method. The estimation device 200 is a device that executes the information processing method. The information processing method may be called an estimation method.

[0011] Next, the hardware included in the learning device 100 and the estimation device 200 will be described. 2 is a diagram showing hardware included in the learning device of Embodiment 1. The learning device 100 is a computer. The learning device 100 includes a processor 101, a volatile storage device 102, and a non-volatile storage device 103.

[0012] The processor 101 controls the entire learning device 100. For example, the processor 101 is a central processing unit (CPU) or a field programmable gate array (FPGA). The processor 101 may be a multiprocessor. The learning device 100 may also include a processing circuit.

[0013] The volatile storage device 102 is a main storage device of the learning device 100. For example, the volatile storage device 102 is a random access memory (RAM). The nonvolatile storage device 103 is an auxiliary storage device of the learning device 100. For example, the nonvolatile storage device 103 is a hard disk drive (HDD) or a solid state drive (SSD).

[0014] The estimation device 200 includes a processor, a volatile storage device, and a non-volatile storage device, similar to the learning device 100. The estimation device 200 may also include a processing circuit.

[0015] The learning phase and the utilization phase will be described below. In the learning phase, the learning device 100 will be described. In the utilization phase, the estimation device 200 will be described.

[0016] <Learning Phase> The functions of the learning device 100 will be described. 3 is a block diagram showing the functions of the learning device of embodiment 1. Learning device 100 includes a storage unit 110, an acquisition unit 120, an extraction unit 130, a conversion unit 140, and a generation unit 150. Note that conversion unit 140 may also be called a first conversion unit.

[0017] The storage unit 110 may be realized as a storage area secured in the volatile storage device 102 or the non-volatile storage device 103 .

[0018] Some or all of the acquiring unit 120, the extracting unit 130, the converting unit 140, and the generating unit 150 may be realized by a processing circuit. Also, some or all of the acquiring unit 120, the extracting unit 130, the converting unit 140, and the generating unit 150 may be realized as modules of a program executed by the processor 101. For example, the program executed by the processor 101 is also referred to as an information processing program or a learning program. For example, the information processing program or the learning program is recorded on a recording medium.

[0019] The storage unit 110 stores various information, and may store training data 10 and a conversion table, which will be described later.

[0020] The acquiring unit 120 acquires the training data 10. For example, the acquiring unit 120 acquires the training data 10 from the storage unit 110. Alternatively, for example, the acquiring unit 120 acquires the training data 10 from an external device. The external device is a device that exists outside the training device 100. For example, the external device is a cloud server, an external memory, or the like. The external device is not shown in the drawing.

[0021] The learning data 10 will be described. For example, the learning data 10 is CAD data. The learning data 10 includes partial shape information 11 and a PMI 12. The partial shape information 11 is information indicating a set of elements of the shape of a machining location. The partial shape information 11 may also be expressed as information indicating a set of elements of the shape of a location that appears due to machining. For example, when drilling a hole in a plate, the object to be machined is the plate, and the partial shape information 11 is information indicating a set of elements of the shape of a cylindrical hole. Specifically, the partial shape information 11 is LINE, CURVE, SURFACE, EDGE, etc., which represent a cylindrical hole (in other words, a cylinder). The partial shape information 11 may be represented by a line number of the learning data 10, as in a specific example shown later. The PMI 12 is information indicating the machining content. The PMI 12 is also called machining content information. The PMI 12 is correct data. That is, the information indicated by the PMI 12 is correct information regarding the machining content. In other words, the information indicated by the PMI 12 is correct when the processing content is judged comprehensively. For example, when drilling a hole in a plate, the PMI 12 is information about the size of the hole. Specific examples of the partial shape information 11 and the PMI 12 are shown below.

[0022] Fig. 4 is a diagram showing a specific example of partial shape information and PMI according to the first embodiment. Fig. 4 shows an example of training data 10. Fig. 4 also shows examples of partial shape information 11 and PMI 12. Note that the partial shape information 11 in Fig. 4 indicates the row number of an element with the shape of a "cylindrical hole."

[0023] Returning to FIG. 3, the extraction unit 130 will be described. The extraction unit 130 extracts the partial shape information 11 and the PMI 12 from the learning data 10. For example, the extraction unit 130 performs the extraction using a table for identifying the partial shape information 11 and the PMI 12.

[0024] The conversion unit 140 converts into a predetermined format at least one of the partial shape information 11 and the PMI 12. Specifically, the conversion unit 140 converts into a predetermined format the partial shape information 11 and the PMI 12 using a conversion table or a trained model (hereinafter referred to as a conversion trained model).

[0025] Here, the reason for performing the conversion will be explained. When a user (e.g., a designer) creates learning data 10 (e.g., CAD data), the partial shape information 11 and PMI 12 included in the learning data 10 are described by the user using various expressions. For example, the partial shape information 11 and PMI 12 are represented by color. For example, the partial shape information 11 and PMI 12 are represented by text. For example, the partial shape information 11 and PMI 12 are represented by symbols. In this way, the partial shape information 11 and PMI 12 are described by the user using various expressions. Therefore, the conversion unit 140 converts the partial shape information 11 and PMI 12 to unify the expression methods.

[0026] First, a case where the conversion unit 140 uses a conversion table will be described. The conversion table is acquired by the acquisition unit 120. For example, the acquisition unit 120 acquires the conversion table from the storage unit 110. The acquisition unit 120 may also acquire the conversion table from an external device. An example of the conversion table will be shown below.

[0027] FIG. 5 is a diagram showing an example of a conversion table according to the first embodiment. For example, the conversion table 111 is stored in the storage unit 110. The conversion table 111 may also be called conversion information. The conversion table 111 is information for converting the partial shape information 11 and the PMI 12 into a predetermined format. The conversion table 111 has fields for description content and conversion content. The description content is registered in the description content field. The conversion content is registered in the conversion content field.

[0028] The conversion unit 140 converts the partial shape information 11 and the PMI 12 into a predetermined format using the conversion table 111. For example, the conversion unit 140 analyzes the partial shape information 11 and creates a tree structure that represents the connection relationships between elements such as lines, surfaces, and curves. The analysis process is similar to general analysis process for CAD data. The tree structure is composed of multiple nodes. The multiple nodes represent elements such as lines, surfaces, and curves. The conversion unit 140 converts elements such as lines into a predetermined format using the conversion table 111. The conversion unit 140 traverses the tree structure and arranges the elements of each node, separating them with spaces. This generates data in a text format. The conversion unit 140 may convert the elements of each node into a predetermined format after arranging them. The tree structure may be traversed using a depth-first search, a breadth-first search, or the like. Specific examples of the converted partial shape information 11 and PMI 12 are shown below.

[0029] 6(A) and (B) are diagrams showing specific examples of partial shape information after conversion and PMI after conversion according to embodiment 1. Fig. 6(A) shows an example of partial shape information after conversion, and Fig. 6(B) shows an example of PMI after conversion.

[0030] Next, a case where the conversion unit 140 uses a conversion trained model will be described. The conversion trained model is acquired by the acquisition unit 120. For example, the acquisition unit 120 acquires the conversion trained model from the storage unit 110. The acquisition unit 120 may acquire the conversion trained model from an external device.

[0031] The conversion unit 140 converts the partial shape information 11 and the PMI 12 into a predetermined format using the conversion trained model. In particular, when the conversion unit 140 inputs the partial shape information 11 into the conversion trained model, the conversion trained model outputs the partial shape information 11 expressed in a predetermined format (i.e., converted partial shape information). Also, when the conversion unit 140 inputs the PMI 12 into the conversion trained model, the conversion trained model outputs the PMI 12 expressed in a predetermined format (i.e., converted PMI). In this way, the partial shape information 11 and the PMI 12 are converted into a predetermined format.

[0032] The generation unit 150 generates a trained model using the partial shape information and the PMI. Note that if the partial shape information is converted, the partial shape information used to generate the trained model is the converted partial shape information. If the PMI is converted, the PMI used to generate the trained model is the converted PMI.

[0033] When a certain PMI is input, the trained model outputs information indicating whether the PMI is correct, or when a certain PMI is input, it outputs the correct PMI. Note that whether the PMI is correct or not means whether it is correct as information for processing.

[0034] Furthermore, the generation unit 150 may convert the converted partial shape information and the converted PMI into an integrated state, and generate a trained model using the converted information. An example of the converted information is shown below.

[0035] Fig. 7 is a diagram showing a specific example of the integration process according to embodiment 1. Fig. 7 shows a converted state (i.e., converted information) obtained by integrating the converted partial shape information and the converted PMI.

[0036] Next, the processing executed by the learning device 100 will be described using a flowchart. FIG. 8 is a flowchart illustrating an example of processing executed by the learning device according to the first embodiment. (Step S11) The acquisition unit 120 acquires the training data 10. (Step S12) The extraction unit 130 extracts the partial shape information 11 and the PMI 12 from the training data 10. (Step S13) The conversion unit 140 converts the partial shape information 11 and the PMI 12 into a predetermined format. (Step S14) The generation unit 150 uses the converted partial shape information and the converted PMI to train a learning model. (Step S15) The generation unit 150 determines whether or not learning has ended. For example, when the acquisition unit 120 acquires an end instruction input by the user, learning ends. When learning ends, the learning model becomes a trained model. When learning has not ended, the process proceeds to step S11. The trained model is stored in the estimation device 200 or an external device. In this way, a trained model is generated. Since the trained model is generated using PMI12, which is the correct answer data, the learning method in the learning phase may be called supervised learning.

[0037] In the learning phase, the learning device 100 learns using the learning data to generate a trained model for comprehensively assessing the accuracy of the PMI. The learning device 100 prepares the trained model. Thus, by preparing the trained model, the accuracy of the PMI can be comprehensively assessed.

[0038] The above describes a case where the partial shape information 11 and the PMI 12 are converted. If the partial shape information 11 is already expressed in a predetermined format, the conversion unit 140 does not convert the partial shape information 11. Also, if the PMI 12 is already expressed in a predetermined format, the conversion unit 140 does not convert the PMI 12. If the partial shape information 11 and the PMI 12 are not converted, the generation unit 150 generates a trained model using the partial shape information 11 and the PMI 12.

[0039] Next, the utilization phase will be described. <Utilization phase> The functions of the estimation device 200 will be described. 9 is a block diagram showing functions of the estimation device according to the first embodiment. The estimation device 200 includes a storage unit 210, an acquisition unit 220, an extraction unit 230, a conversion unit 240, an estimation unit 250, a comparison unit 260, and an output unit 270. The conversion unit 240 may be referred to as a first conversion unit.

[0040] The storage unit 210 may be realized as a storage area secured in a volatile storage device or a non-volatile storage device included in the estimation device 200.

[0041] Some or all of the acquiring unit 220, the extracting unit 230, the converting unit 240, the estimating unit 250, the comparing unit 260, and the output unit 270 may be realized by a processing circuit included in the estimation device 200. Furthermore, some or all of the acquiring unit 220, the extracting unit 230, the converting unit 240, the estimating unit 250, the comparing unit 260, and the output unit 270 may be realized as program modules executed by a processor included in the estimation device 200. For example, the program executed by the processor may also be referred to as an information processing program or an estimation program. For example, the information processing program or the estimation program is recorded on a recording medium.

[0042] The storage unit 210 stores various information, and may store CAD data 20 and a conversion table 111, which will be described later.

[0043] The acquisition unit 220 acquires the CAD data 20. For example, the acquisition unit 220 acquires the CAD data 20 from the storage unit 210. Alternatively, for example, the acquisition unit 220 acquires the CAD data 20 from an external device. The CAD data 20 is also referred to as design data. The CAD data 20 includes partial shape information 21 and PMI 22. The acquisition unit 220 acquires the trained model from the storage unit 210 or an external device. Note that the trained model is a trained model generated by the learning device 100.

[0044] The extraction unit 230 extracts the partial shape information 21 and the PMI 22 from the CAD data 20. For example, the extraction unit 230 performs the extraction using a table for identifying the partial shape information 21 and the PMI 22.

[0045] The conversion unit 240 converts at least one of the partial shape information 21 and the PMI 22 into a predetermined format. Specifically, the conversion unit 240 converts the partial shape information 21 and the PMI 22 into a predetermined format using the conversion table 111 or a conversion trained model. The method of using the conversion table 111 and the conversion trained model is the same as described above, and therefore description thereof will be omitted.

[0046] The estimation unit 250 uses the partial shape information, the PMI, and the trained model to output information indicating whether the PMI 22 is correct or not, or outputs the correct PMI. Note that when the partial shape information is converted, the partial shape information input to the trained model is the converted partial shape information. When the PMI is converted, the PMI input to the trained model is the converted PMI. For example, when the estimation unit 250 inputs the converted partial shape information and the converted PMI to the trained model, the trained model outputs information indicating whether the PMI 22 is correct or not, or the correct PMI.

[0047] In addition, the estimation unit 250 may convert the converted partial shape information and the converted PMI into an integrated state, and use the converted information and the trained model to output information indicating whether the PMI 22 is correct or not, or output the correct PMI.

[0048] When the correct PMI is output, the comparison unit 260 compares the PMI 22 or the converted PMI with the correct PMI. For example, the comparison unit 260 performs the comparison using word2vec or BERT.

[0049] When the information indicating whether the PMI 22 is correct or not is output, the output unit 270 outputs the information. For example, the output unit 270 outputs the information to the display of the estimating device 200. By outputting the information, the designer can recognize whether the PMI 22 is correct or not.

[0050] When the comparison is performed, the output unit 270 outputs the comparison result. For example, the output unit 270 outputs the comparison result to a display of the estimation device 200. By outputting the comparison result, the designer can recognize whether the PMI 22 is excessive or insufficient.

[0051] Next, the processing executed by the estimation device 200 will be described with reference to a flowchart. 10 is a flowchart illustrating an example of processing executed by the estimation device according to Embodiment 1. In the following description, it is assumed that the trained model outputs a correct PMI. (Step S21) The acquisition unit 220 acquires the CAD data 20. (Step S22) The extraction unit 230 extracts the partial shape information 21 and the PMI 22 from the CAD data 20.

[0052] (Step S23) The conversion unit 240 converts the partial shape information 21 and the PMI 22 into a predetermined format. (Step S24) The estimation unit 250 uses the converted partial shape information, the converted PMI, and the trained model to output the correct PMI. (Step S25) The comparison unit 260 compares the PMI 22 or the converted PMI with the correct PMI. (Step S26) The output unit 270 outputs the comparison result.

[0053] In the utilization phase, the estimation device 200 can comprehensively determine the accuracy of the PMI by using the trained model.

[0054] The above describes a case where the partial shape information 21 and the PMI 22 are converted. If the partial shape information 21 is already expressed in a predetermined format, the conversion unit 140 does not convert the partial shape information 21. Also, if the PMI 22 is already expressed in a predetermined format, the conversion unit 140 does not convert the PMI 22. If the partial shape information 21 and the PMI 22 are not converted, the estimation unit 250 uses the partial shape information 21, the PMI 22, and the trained model to output information indicating whether the PMI 22 is correct or not, or output the correct PMI.

[0055] Here, the CAD data 20 may not include the PMI 22. When the PMI 22 is not included in the CAD data 20, the estimation unit 250 inputs the partial shape information 21 or the converted partial shape information to the trained model. Then, the trained model outputs information indicating that the PMI 22 is not included in the CAD data 20. The output unit 270 outputs the information. By outputting the information, the designer becomes aware that the PMI 22 is not included in the CAD data 20.

[0056] Embodiment 2 Next, a description will be given of embodiment 2. In embodiment 2, differences from embodiment 1 will be mainly described. Furthermore, in embodiment 2, description of matters common to embodiment 1 will be omitted. <Learning Phase>

[0057] 11 is a block diagram showing the functions of the learning device of Embodiment 2. Learning device 100 further includes a conversion unit 160. Conversion unit 160 may also be called a second conversion unit. A part or all of the conversion unit 160 may be realized by a processing circuit, or a part or all of the conversion unit 160 may be realized as a module of a program executed by the processor 101.

[0058] The format of the training data 10 in the second embodiment is assumed to be a binary format. If the training data 10 is not in ASCII format, the extraction unit 130 cannot perform extraction. Therefore, the conversion unit 160 converts the format of the training data 10 into ASCII format. In other words, the conversion unit 160 converts the training data 10 into a text file. For example, the conversion unit 160 converts the format of the training data 10 into ASCII format using a table for format conversion. By converting the format of the training data 10 into ASCII format, the extraction unit 130 can perform extraction.

[0059] Next, the processing executed by the learning device 100 will be described using a flowchart. Fig. 12 is a flowchart showing an example of processing executed by the learning device of embodiment 2. The processing in Fig. 12 differs from the processing in Fig. 8 in that step S11a is executed. Therefore, step S11a will be described in Fig. 12. Description of processing other than step S11a will be omitted. (Step S11a) The conversion unit 160 converts the format of the learning data 10 into ASCII format. In step S12, the extraction unit 130 extracts the partial shape information 11 and the PMI 12 from the converted training data 10.

[0060] According to the second embodiment, the learning device 100 can perform learning even when the format of the learning data 10 is a binary format.

[0061] <Utilization phase> 13 is a block diagram showing the functions of the estimation device according to the embodiment 2. The estimation device 200 further includes a conversion unit 280. The conversion unit 280 may be called a second conversion unit. A part or all of the conversion unit 280 may be realized by a processing circuit included in the estimating device 200. Alternatively, a part or all of the conversion unit 280 may be realized as a program module executed by a processor included in the estimating device 200.

[0062] The format of the CAD data 20 in the second embodiment is assumed to be a binary format. If the CAD data 20 is not in ASCII format, the extraction unit 230 cannot perform extraction. Therefore, the conversion unit 280 converts the format of the CAD data 20 into ASCII format. In other words, the conversion unit 280 converts the CAD data 20 into a text file. For example, the conversion unit 280 converts the format of the CAD data 20 into ASCII format using a table for format conversion. By converting the format of the CAD data 20 into ASCII format, the extraction unit 230 can perform extraction.

[0063] Next, the processing executed by the estimation device 200 will be described with reference to a flowchart. Fig. 14 is a flowchart showing an example of processing executed by the estimation device of embodiment 2. The processing in Fig. 14 differs from the processing in Fig. 10 in that step S21a is executed. Therefore, step S21a will be described in Fig. 14. Description of the processing other than step S21a will be omitted. (Step S21a) The conversion unit 280 converts the format of the CAD data 20 into ASCII format. In step S22, the extraction unit 230 extracts the partial shape information 21 and the PMI 22 from the converted CAD data 20.

[0064] According to the second embodiment, the estimating device 200 can perform estimation even when the CAD data 20 is in a binary format.

[0065] Embodiment 3 Next, a description will be given of embodiment 3. In embodiment 3, differences from embodiment 1 will be mainly described. Furthermore, in embodiment 3, description of matters common to embodiment 1 will be omitted.

[0066] <Utilization phase> In the first embodiment, a case has been described in which all of the partial shape information 21 and PMI 22 included in the CAD data 20 are extracted. In the third embodiment, a case will be described in which elements corresponding to the PMI to be estimated are extracted from the partial shape information 21.

[0067] The process executed by the estimation device 200 will be described using a flowchart. Fig. 15 is a flowchart showing an example of processing executed by the estimation device of embodiment 3. The processing in Fig. 15 differs from the processing in Fig. 10 in that steps S21b and S22a are executed. Therefore, steps S21b and S22a will be described in Fig. 15. Description of processing other than steps S21b and S22a will be omitted.

[0068] (Step S21b) The acquisition unit 220 acquires estimation target information from the storage unit 210 or an external device. Note that the estimation target information indicates a shape element related to the PMI of the estimation target and the PMI of the estimation target. (Step S22a) The extraction unit 230 uses the estimation target information to extract, from the CAD data 20, partial shape information 21 indicating shape elements related to the PMI of the estimation target, and the PMI 22 of the estimation target.

[0069] According to the third embodiment, the estimating device 200 does not extract all of the partial shape information 21 and PMI 22 included in the CAD data 20. Therefore, the time required for the conversion process (i.e., step S23) and the estimation process (i.e., step S24) is short. Note that if the conversion process is not performed, the time required for the estimation process is short. Therefore, the processing load on the estimating device 200 can be reduced.

[0070] The features of the above-described embodiments can be combined with each other as appropriate. [Explanation of symbols]

[0071] 10 learning data, 11 partial shape information, 12 PMI, 20 CAD data, 21 partial shape information, 22 PMI, 100 learning device, 101 processor, 102 volatile storage device, 103 non-volatile storage device, 110 memory unit, 111 conversion table, 120 acquisition unit, 130 extraction unit, 140 conversion unit, 150 generation unit, 160 conversion unit, 200 estimation device, 210 memory unit, 220 acquisition unit, 230 extraction unit, 240 conversion unit, 250 estimation unit, 260 comparison unit, 270 output unit, 280 conversion unit.

Claims

1. an acquiring unit that acquires learning data in a binary format, the learning data including partial shape information that is information indicating a set of elements of the shape of a processing portion and processing content information that is information indicating processing content; a second conversion unit that converts the format of the learning data into an ASCII format; an extraction unit that extracts the partial shape information and the processing content information from the converted learning data; A generation unit that generates a trained model using the partial shape information and the processing content information; An information processing device having the above.

2. a first conversion unit that converts at least one of the partial shape information and the processing content information into a predetermined format; When the partial shape information is converted, the partial shape information used to generate the trained model is the converted partial shape information, When the processing content information is converted, the processing content information used to generate the trained model is the converted processing content information. The information processing device according to claim 1 .

3. an acquisition unit that acquires design data including partial shape information, which is information indicating a set of elements of the shape of a processing portion, and processing content information, which is information indicating processing content, and a trained model; an extraction unit that extracts the partial shape information and the processing content information from the design data; an estimation unit that uses the partial shape information, the processing content information, and the learned model to output information indicating whether the processing content information is correct or not, or outputs correct processing content information; An information processing device having the above.

4. a first conversion unit that converts at least one of the partial shape information and the processing content information into a predetermined format; When the partial shape information is converted, the partial shape information input to the trained model is the converted partial shape information, When the processing content information is converted, the processing content information input to the trained model is the processing content information after conversion. The information processing device according to claim 3 .

5. a comparison unit that compares the processing content information or the converted processing content information with the correct processing content information when the correct processing content information is output; an output unit that outputs the comparison result; further comprising 5. The information processing device according to claim 3 or 4.

6. Further comprising a second conversion unit; the design data is in a binary format; the second conversion unit converts the format of the design data into an ASCII format; the extraction unit extracts the partial shape information and the processing content information from the converted design data.

5. The information processing device according to claim 3 or 4.

7. the acquisition unit acquires estimation target information indicating a shape element related to the processing content information of the estimation target and the processing content information of the estimation target; the extraction unit uses the estimation target information to extract, from the design data, the partial shape information indicating a shape element having a relationship with the processing content information of the estimation target, and the processing content information of the estimation target.

5. The information processing device according to claim 3 or 4.

8. further having an output section, The design data does not include the processing content information, the estimation unit inputs the partial shape information or the converted partial shape information into the trained model; The output unit outputs information indicating that the processing content information is not included in the design data, the information being output from the trained model. The information processing device according to claim 3 .

9. The information processing device The learning data includes partial shape information, which is information indicating a set of elements of the shape of the processing portion, and processing content information, which is information indicating the processing content, and the learning data is acquired in a binary format; converting the format of the learning data into ASCII format; extracting the partial shape information and the processing content information from the converted learning data; Generate a trained model using the partial shape information and the processing content information. Information processing methods.

10. The information processing device Acquire design data including partial shape information, which is information indicating a set of elements of the shape of a processing portion, and processing content information, which is information indicating processing content, and a trained model; extracting the partial shape information and the processing content information from the design data; Using the partial shape information, the processing content information, and the trained model, outputting information indicating whether the processing content information is correct or not, or outputting correct processing content information. Information processing methods.

11. In the information processing device, The learning data includes partial shape information, which is information indicating a set of elements of the shape of the processing portion, and processing content information, which is information indicating the processing content, and the learning data is acquired in a binary format; converting the format of the learning data into ASCII format; extracting the partial shape information and the processing content information from the converted learning data; Generate a trained model using the partial shape information and the processing content information. An information processing program that executes processing.

12. In the information processing device, Acquire design data including partial shape information, which is information indicating a set of elements of the shape of a processing portion, and processing content information, which is information indicating processing content, and a trained model; extracting the partial shape information and the processing content information from the design data; Using the partial shape information, the processing content information, and the trained model, outputting information indicating whether the processing content information is correct or not, or outputting correct processing content information. An information processing program that executes processing.

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