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

Abstract

In the present invention, a learning device (100) has: an acquisition unit (120) that acquires training data (10) including partial shape information (11), which is information indicating a set of elements in the shape of a machining site, and PMI (12), which is information indicating machining content; an extraction unit (130) that extracts the partial shape information (11) and the PMI (12) from within the training data; and a generation unit (150) that uses the partial shape information (11) and the PMI (12) to generate a trained model.

Description

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

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

[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).

[0003] Japanese Patent Application Laid-Open No. 2022-105917

[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 problem is how to judge the accuracy of PMI comprehensively.

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

[0006] According to one aspect of the present disclosure, there is provided an information processing device, the information processing device including: an acquisition unit that acquires learning data including partial shape information that is information indicating a set of elements of the shape of a processing location and processing content information that is information indicating processing content, 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.

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

[0008] 1 is a diagram illustrating an information processing system according to a first embodiment; FIG. 2 is a diagram illustrating hardware included in a learning device according to the first embodiment; FIG. 3 is a block diagram illustrating functions of the learning device according to the first embodiment; FIG. 4 is a diagram illustrating a specific example of partial shape information and PMI according to the first embodiment; FIG. 5 is a diagram illustrating an example of a conversion table according to the first embodiment; (A) and (B) are diagrams illustrating specific examples of converted partial shape information and converted PMI according to the first embodiment; FIG. 6 is a diagram illustrating a specific example of integration processing according to the first embodiment; FIG. 7 is a flowchart illustrating an example of processing performed by the learning device according to the first embodiment; FIG. 8 is a block diagram illustrating functions of an estimation device according to the first embodiment; FIG. 9 is a flowchart illustrating an example of processing performed by the estimation device according to the first embodiment; FIG. 10 is a block diagram illustrating functions of a learning device according to a second embodiment; FIG. 11 is a flowchart illustrating an example of processing performed by the learning device according to the second embodiment; FIG. 12 is a block diagram illustrating functions of the estimation device according to the second embodiment; FIG. 13 is a flowchart illustrating an example of processing performed by the estimation device according to the second embodiment;

[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 showing an information processing system according to embodiment 1. 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 information processing devices. 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. Fig. 2 is a diagram showing the 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 memory device 102 is a main memory device of the learning device 100. For example, the volatile memory device 102 is a RAM (Random Access Memory). The non-volatile memory device 103 is an auxiliary memory device of the learning device 100. For example, the non-volatile memory device 103 is a HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[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. Fig. 3 is a block diagram showing the functions of the learning device of embodiment 1. The learning device 100 has a storage unit 110, an acquisition unit 120, an extraction unit 130, a conversion unit 140, and a generation unit 150. The 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 acquisition unit 120, extraction unit 130, conversion unit 140, and generation unit 150 may be realized by a processing circuit. Also, some or all of the acquisition unit 120, extraction unit 130, conversion unit 140, and generation unit 150 may be realized as program modules 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 learning data 10 and a conversion table, which will be described later.

[0020] The acquisition unit 120 acquires the learning data 10. For example, the acquisition unit 120 acquires the learning data 10 from the storage unit 110. Alternatively, for example, the acquisition unit 120 acquires the learning data 10 from an external device. Note that the external device is a device that exists outside the learning 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 area. The partial shape information 11 may also be expressed as information indicating a set of elements of the shape of a part 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 referred to as machining content information. The PMI 12 is also correct answer data. That is, the information indicated by the PMI 12 is correct information regarding the processing content. In other words, the information indicated by the PMI 12 is correct information when the processing content is judged comprehensively. For example, when drilling a hole in a plate, the PMI 12 is information regarding 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 learning 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 a "cylindrical hole" shape.

[0023] 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, 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 using various expressions by the user. For example, the partial shape information 11 and PMI 12 are represented using colors. For example, the partial shape information 11 and PMI 12 are represented using text. For example, the partial shape information 11 and PMI 12 are represented using symbols. In this way, the partial shape information 11 and PMI 12 are described using various expressions by the user. 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 uses the conversion table 111 to convert the partial shape information 11 and the PMI 12 into a predetermined format. For example, the conversion unit 140 analyzes the partial shape information 11 and creates a tree structure representing the connection relationships between elements such as lines (LINE), surfaces (SURFACE), and curves (CURVE). The analysis process is similar to general analysis processes for CAD data. The tree structure is composed of multiple nodes. The multiple nodes represent elements such as LINE, SURFACE, and CURVE. The conversion unit 140 uses the conversion table 111 to convert elements such as LINE into a predetermined format. The conversion unit 140 traverses the tree structure, separating the elements of each node with spaces and arranging them. This generates data in a sentence format. After arranging the elements of each node, the conversion unit 140 may convert them into a predetermined format. The method for traversing the tree structure may be depth-first search, breadth-first search, or the like. Specific examples of the converted partial shape information 11 and PMI 12 are shown below.

[0029] 6A and 6B are diagrams showing specific examples of partial shape information after conversion and PMI after conversion according to embodiment 1. Fig. 6A shows an example of partial shape information after conversion, and Fig. 6B 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). Furthermore, 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] The trained model outputs information indicating whether a certain PMI is correct when the PMI is input, or outputs the correct PMI when a certain PMI is input. Note that whether the PMI is correct or not means whether the information is correct when 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 showing an example of processing executed by the learning device of embodiment 1. (Step S11) The acquisition unit 120 acquires learning data 10. (Step S12) The extraction unit 130 extracts partial shape information 11 and PMI 12 from the learning data 10. (Step S13) The conversion unit 140 converts the partial shape information 11 and PMI 12 into a predetermined format. (Step S14) The generation unit 150 trains a learning model using the converted partial shape information and the converted PMI. (Step S15) The generation unit 150 determines whether learning has ended. For example, if the acquisition unit 120 acquires an end instruction input by the user, learning ends. If learning has ended, the learning model becomes a trained model. If learning has not ended, the processing 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 the PMI 12, 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 determining 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 determined.

[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. FIG. 9 is a block diagram showing the functions of the estimation device of embodiment 1. The estimation device 200 has 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 also be called 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 acquisition unit 220, extraction unit 230, conversion unit 240, estimation unit 250, comparison unit 260, and output unit 270 may be realized by a processing circuit included in the estimation device 200. Furthermore, some or all of the acquisition unit 220, extraction unit 230, conversion unit 240, estimation unit 250, comparison unit 260, and 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 is also 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 a trained model from the storage unit 210 or an external device. 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 learned model. The method of using the conversion table 111 and the conversion learned 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 to output 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 learned model to output information indicating whether the PMI 22 is correct or not, or output the correct PMI.

[0048] If 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 uses 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 using a flowchart. FIG. 10 is a flowchart showing an example of the processing executed by the estimation device of embodiment 1. In the following description, it is assumed that the trained model outputs a correct PMI. (Step S21) The acquisition unit 220 acquires CAD data 20. (Step S22) The extraction unit 230 extracts partial shape information 21 and 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 outputs the correct PMI using the converted partial shape information, the converted PMI, and the trained model. (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] According to 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. Furthermore, 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 to 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 this information. By outputting this information, the designer becomes aware that the PMI 22 is not included in the CAD data 20.

[0056] Second Embodiment Next, a second embodiment will be described. In the second embodiment, differences from the first embodiment will be mainly described. In the second embodiment, descriptions of commonalities with the first embodiment will be omitted. <Learning Phase>

[0057] 11 is a block diagram showing the functions of the learning device according to the second embodiment. The learning device 100 further includes a conversion unit 160. The conversion unit 160 may be referred to as a second conversion unit. Part or all of the conversion unit 160 may be implemented by a processing circuit. Alternatively, part or all of the conversion unit 160 may be implemented as a program module executed by the processor 101.

[0058] In the second embodiment, the format of the training data 10 is assumed to be 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. Note that in step S12, the extraction unit 130 extracts partial shape information 11 and PMI 12 from the converted learning 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> Fig. 13 is a block diagram showing the functions of the estimation device according to the second embodiment. The estimation device 200 further includes a conversion unit 280. The conversion unit 280 may be referred to as a second conversion unit. A part or all of the conversion unit 280 may be implemented by a processing circuit included in the estimation device 200. Alternatively, a part or all of the conversion unit 280 may be implemented as a program module executed by a processor included in the estimation device 200.

[0062] In the second embodiment, the format of the CAD data 20 is assumed to be 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 estimating device 200 will be described using a flowchart. FIG. 14 is a flowchart showing an example of the processing executed by the estimating 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 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 partial shape information 21 and 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, embodiment 3 will be described. In embodiment 3, differences from embodiment 1 will be mainly described. Furthermore, in embodiment 3, descriptions 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 processing 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 22a are executed. Therefore, steps S21b and 22a will be described in Fig. 15. Description of processing other than steps S21b and 22a will be omitted.

[0068] (Step S21b) The acquisition unit 220 acquires estimation target information from the storage unit 210 or an external device. The estimation target information indicates shape elements 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 partial shape information 21 indicating shape elements related to the PMI of the estimation target and the PMI 22 of the estimation target from the CAD data 20.

[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.

[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 Storage unit, 111 Conversion table, 120 Acquisition unit, 130 Extraction unit, 140 Conversion unit, 150 Generation unit, 160 Conversion unit, 200 Estimation device, 210 Storage 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 acquisition unit that acquires learning data including partial shape information, which is information indicating a set of elements of the shape of a processing location, and processing content information, which is information indicating processing content; 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 learned model using the partial shape information and the processing content information. An information processing apparatus having the above components.

2. The information processing apparatus according to claim 1, further comprising 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 for generating the learned model is the converted partial shape information. When the processing content information is converted, the processing content information used for generating the learned model is the converted processing content information.

3. The information processing apparatus according to claim 1 or 2, further comprising a second conversion unit. The format of the learning data is a binary format. The second conversion unit converts the format of the learning data into an ASCII format. The extraction unit extracts the partial shape information and the processing content information from the converted learning data.

4. 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 location, and processing content information, which is information indicating processing content, and a learned model; an extraction unit that extracts the partial shape information and the processing content information from the design data; and an estimation unit that outputs information indicating whether the processing content information is correct or outputs the correct processing content information using the partial shape information, the processing content information, and the learned model. An information processing apparatus having the above components.

5. The information processing apparatus according to claim 4, further comprising 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 learned model is the converted partial shape information. When the processing content information is converted, the processing content information input to the learned model is the converted processing content information.

6. When the correct machining content information is output, a comparison unit that compares the machining content information or the converted machining content information with the correct machining content information, and an output unit that outputs a comparison result. The information processing apparatus according to claim 4 or 5, further comprising:

7. Further comprising a second conversion unit, wherein the format of the design data is a binary format, the second conversion unit converts the format of the design data into an ASCII format, and the extraction unit extracts the partial shape information and the machining content information from the converted design data. The information processing apparatus according to any one of claims 4 to 6.

8. The acquisition unit acquires element information of a shape related to the machining content information of the estimation target and estimation target information indicating the machining content information of the estimation target, and the extraction unit uses the estimation target information to obtain the partial shape information indicating the element information of the shape related to the machining content information of the estimation target and the machining content information of the estimation target from the design data. The information processing apparatus according to any one of claims 4 to 7.

9. Further comprising an output unit, wherein the design data does not include the machining content information, the estimation unit inputs the partial shape information or the converted partial shape information into the learned model, and the output unit outputs information indicating that the machining content information output from the learned model is not included in the design data. The information processing apparatus according to claim 4.

10. An information processing method, wherein an information processing apparatus acquires learning data including partial shape information, which is information indicating a set of elements of the shape of a machining location, and machining content information, which is information indicating machining content, extracts the partial shape information and the machining content information from the learning data, and generates a learned model using the partial shape information and the machining content information.

11. An information processing method, wherein an information processing apparatus acquires design data including partial shape information which is information indicating a set of elements of the shape of a processing location, and processing content information which is information indicating processing content, and a learned model, extracts the partial shape information and the processing content information from the design data, and outputs information indicating whether the processing content information is correct or outputs the correct processing content information by using the partial shape information, the processing content information, and the learned model.

12. An information processing program for causing an information processing apparatus to execute a process of acquiring learning data including partial shape information which is information indicating a set of elements of the shape of a processing location, and processing content information which is information indicating processing content, extracting the partial shape information and the processing content information from the learning data, and generating a learned model by using the partial shape information and the processing content information.

13. An information processing program for causing an information processing apparatus to execute a process of acquiring design data including partial shape information which is information indicating a set of elements of the shape of a processing location, and processing content information which is information indicating processing content, and a learned model, extracting the partial shape information and the processing content information from the design data, and outputting information indicating whether the processing content information is correct or outputting the correct processing content information by using the partial shape information, the processing content information, and the learned model.

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