Design assistance system

Abstract

Provided is a design assistance system suitable for understanding a plurality of elements to be successively edited and an editing order thereof.　A drawing creation assistance device 100: acquires a created or edited element ID; estimates, from the acquired element ID, a plurality of pieces of element order information having different editing elements or editing orders by using a trained model that was trained on the basis of training data that includes the created or edited element ID, a plurality of editing elements consecutively edited following the editing element of the created or edited element ID, and element order information relating to the editing order thereof; and displays any of a plurality of element orders on the basis of the estimated plurality of pieces of element order information. Thereby, a plurality of editing elements to be consecutively edited and the editing order thereof can be understood.

Description

Design Support System

[0001] The present invention relates to a system for supporting design, and more particularly to a design support system suitable for grasping a plurality of continuously edited elements and their editing order.

[0002] Conventionally, as a technique for supporting design using AI (Artificial Intelligence), for example, the technique described in Patent Document 1 is known.

[0003] The technique described in Patent Document 1 assigns a reward R to a combination of a state S determined depending on the execution of a design activity and an action A that is an activity selectable under the state, and constructs a learned model by maximizing the value. Then, using the learned model, the action to be taken next from the current state is inferred.

[0004] Japanese Patent Application Laid-Open No. 2022-56238

[0005] In architectural design, it is sometimes necessary to continuously edit a plurality of related elements for a certain unit. For example, in the design of a toilet, the design of a toilet bowl and a washbasin may be continued. However, in the technique described in Patent Document 1, since it infers the action to be taken next from the current state, there is a problem that it is impossible to grasp a plurality of continuously edited elements and their editing order.

[0006] Therefore, the present invention has been made by paying attention to such an unsolved problem of the conventional technique, and an object thereof is to provide a design support system suitable for grasping a plurality of continuously edited elements and their editing order.

[0007] [Invention 1] To achieve the above objective, the design support system of Invention 1 comprises: element information acquisition means for acquiring element information relating to created or edited elements in design information; estimation means for estimating a plurality of element sequence information with different elements or editing sequences from the element information acquired by the element information acquisition means, using a trained model trained on training data including element information relating to created or edited elements, and element sequence information relating to a plurality of elements edited consecutively after the element and their editing sequence (hereinafter, "a plurality of elements and their editing sequence" is referred to as "element sequence"); and output means for outputting one or more element sequences from the plurality of element sequences based on the plurality of element sequence information estimated by the estimation means.

[0008] In this configuration, element information is acquired by the element information acquisition means, and multiple element order information is estimated from the acquired element information by the estimation means using a trained model. Then, one or more element orders are output by the output means based on the estimated multiple element order information.

[0009] Here, a trained model is defined as one that has been trained on training data containing at least element information and element order information, and also includes models that have been trained on training data containing element information, element order information, and other information.

[0010] Furthermore, the element information acquisition means may, for example, input element information from an input device, acquire or receive element information from an external terminal, read element information from a storage device or storage medium, or generate or calculate element information through information processing. Therefore, acquisition includes at least input, acquisition, reception, reading (including retrieval), generation, and calculation. The concept of acquisition remains the same hereafter.

[0011] Furthermore, element information can consist of the element itself, as information for identifying the element (e.g., name, number, ID, code, URL or other link information), or as feature information relating to the element's overview, statistics, or other characteristics. Element information can also consist of characters, numbers, figures, codes, symbols, images, sounds, or other information. Additionally, element information can consist of keywords related to the element (e.g., one or more keywords indicating part of the element's name).

[0012] Furthermore, element sequence information can consist of, for example, the element sequence itself, or it can be composed of information for identifying elements and editing sequences (e.g., name, number, ID, code, URL, or other link information), or it can be composed of feature information relating to the outline, statistics, or other characteristics of elements and editing sequences. Element sequence information can also consist of, for example, characters, numbers, figures, codes, symbols, images, sounds, or other information. Additionally, element sequence information can consist of keywords relating to elements and editing sequences (e.g., one or more keywords indicating part of the names of elements and editing sequences).

[0013] Furthermore, the output means can output information by methods such as display, printing, audio output, writing to storage devices or media, transmitting to other equipment, devices, terminals or other devices, vibration, heat generation, or other means. Therefore, output includes at least display, printing, audio output, writing, transmission, vibration, and heat generation. The same concept of output applies hereafter.

[0014] Furthermore, this system may be implemented as a single device, apparatus, terminal, or other device, or as a network system in which multiple devices, apparatus, terminals, or other devices are connected in a communicative manner. In the latter case, each component may belong to any of the multiple devices, as long as it is connected in a communicative manner.

[0015] [Invention 2] Furthermore, in the design support system of Invention 2, the output means outputs the element sequence with the largest number of elements among the plurality of element sequences, compared to the design support system of Invention 1.

[0016] With this configuration, the output method will output the element order with the largest number of elements.

[0017] [Invention 3] Furthermore, the design support system of Invention 3 includes, in the design support system of Invention 1, a identification means for identifying element sequences whose occurrence frequency in the design information is predetermined or greater as related groups, and the output means outputs element sequences from among the plurality of element sequences that have related groups identified by the identification means.

[0018] In this configuration, the identification means identifies element sequences that appear more than a predetermined number of times as related groups, and the output means outputs the element sequences that constitute the identified related groups.

[0019] [Invention 4] Furthermore, in the design support system of Invention 4, the output means outputs an element sequence that is the same as the related group identified by the identification means among the plurality of element sequences.

[0020] With this configuration, the output means will output elements in the same order as the related groups.

[0021] [Invention 5] Furthermore, the design support system of Invention 5 includes, in the design support system of Invention 1, a identification means for identifying element sequences whose occurrence frequency in the design information is predetermined or higher as related groups, and the output means outputs the portion of the related group from among the plurality of element sequences, including the related group identified by the identification means and other elements.

[0022] In this configuration, a specific means identifies element sequences that appear more than a predetermined number of times as related groups, and an output means outputs a portion of the identified related group from the element sequence including other elements.

[0023] [Invention 6] Furthermore, the design support system of Invention 6 comprises, in the design support system of any one of Inventions 1 to 5, a second element information acquisition means for acquiring element information relating to elements created or edited based on the element order output by the output means, and a second estimation means for estimating a plurality of element order information with different elements or editing orders from the element information acquired by the second element information acquisition means using the trained model.

[0024] In this configuration, element information is acquired by the second element information acquisition means, and multiple element order information is estimated from the acquired element information by the second estimation means using a trained model.

[0025] [Invention 7] Furthermore, the design support system of Invention 7 is a design support system of any one of Inventions 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; an input means for inputting a request to an AI model that includes the element information acquired by the second element information acquisition means and element order information relating to a plurality of elements to be edited consecutively after the element relating to the element information and their editing order, wherein the request includes a request to generate a plurality of such element order information with different elements or editing orders; and an acquisition means for acquiring the plurality of element order information output from the AI ​​model in response to the request.

[0026] In this configuration, element information is acquired by the second element information acquisition means, a request including the acquired element information and a request to generate multiple element sequence information is input to the AI ​​model by the input means, and the multiple element sequence information output from the AI ​​model is acquired by the acquisition means in response to the request.

[0027] [Invention 8] Furthermore, the design support system of Invention 8 is a design support system of any one of Inventions 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; and a search means for searching a plurality of element order information that corresponds to the element information acquired by the second element information acquisition means, but which has different elements or editing orders, from a storage means that stores element order information relating to a plurality of elements that are edited consecutively after a created or edited element and their editing order in association with the element information relating to the created or edited element.

[0028] In this configuration, element information is acquired by the second element information acquisition means, and multiple element sequence information corresponding to the acquired element information is retrieved from the storage means by the search means.

[0029] [Invention 9] Furthermore, the design support system of Invention 9 is a design support system of any one of Inventions 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to elements created or edited based on the element order output by the output means; and an output rule changing means for changing the output rules of the output means based on the element order output by the output means and the element information acquired by the second element information acquisition means.

[0030] In this configuration, element information is acquired by the second element information acquisition means, and the output rule changing means changes the output rules of the output means based on the output element order and the acquired element information.

[0031] [Invention 10] Furthermore, the design support system of Invention 10 is a design support system of any one of Inventions 1 to 5, wherein the trained model is trained to maximize the evaluation value based on training data which includes element information relating to created or edited elements, element sequence information relating to the sequence of elements edited consecutively after the element, and evaluation values ​​relating to the editing of the plurality of elements.

[0032] Here, a trained model is one that has been trained on training data that includes at least element information, element order information, and evaluation values, and also includes models that have been trained on training data that includes element information, element order information, evaluation values, and other information.

[0033] [Invention 11] Furthermore, the design support system of Invention 11 is the design support system of Invention 10, wherein the trained model includes a first trained model trained to maximize the evaluation value based on training data including the element information, the element sequence information, and the evaluation value based on a first index indicating an index of the value of the evaluation value, and a second trained model trained to maximize the evaluation value based on training data including the element information, the element sequence information, and the evaluation value based on a second index different from the first index, and comprises an index information acquisition means for acquiring index information relating to the first index or the second index, and a trained model selection means for selecting either the first trained model or the second trained model based on the index information acquired by the index information acquisition means, and the estimation means estimates the plurality of element sequence information using the trained model selected by the trained model selection means.

[0034] In this configuration, index information is acquired by the index information acquisition means, and a trained model is selected by the trained model selection means based on the acquired index information. Then, the estimation means estimates multiple element order information using the selected trained model.

[0035] Here, the first trained model only needs to be one that has been trained on training data that includes at least element information, element order information, and evaluation values ​​based on the first indicator, and also includes models that have been trained on training data that includes element information, element order information, evaluation values ​​based on the first indicator, and other information.

[0036] Furthermore, the second trained model only needs to be one that has been trained on training data that includes at least element information, element order information, and evaluation values ​​based on the second indicator, and also includes models that have been trained on training data that includes element information, element order information, evaluation values ​​based on the second indicator, and other information.

[0037] [Invention 12] Further, in the design support system of Invention 12, in the design support system of any one of Inventions 1 to 5, the plurality of elements are elements that require human judgment for their setting or change, and are elements that affect the setting or change with respect to other elements.

[0038] [Invention 13] Further, in the design support system of Invention 13, in the design support system of any one of Inventions 1 to 5, the design information is design information for performing the design of a building.

[0039] As described above, according to the design support system of Invention 1, since one or more element orders are output, it is possible to grasp a plurality of continuously edited elements and their editing order.

[0040] Further, according to the design support system of Invention 2, since the element order with the largest number of elements is output, it is possible to perform editing while imagining a large editing unit.

[0041] Further, according to the design support system of Invention 3 or 5, since an element order having a related group is output, it is possible to perform editing while imagining the related group.

[0042] Further, according to the design support systems of Inventions 6 to 8, since a plurality of element order information can be obtained for the result of creation or editing based on the output element order, it is possible to grasp a plurality of continuously edited elements and their editing order.

[0043] Further, according to the design support system of Invention 9, the output rule can be changed according to the output result of the output means and the result of subsequent creation or editing.

[0044] Further, according to the design support system of Invention 10, it is possible to grasp the element order with a high evaluation value.

[0045] Further, according to the design support system of Invention 11, it is possible to grasp the element order with a high evaluation value based on the first index or the second index.

[0046] Furthermore, according to the design support system of Invention 12, it is possible to grasp the element order considering the relationship between elements that require human judgment for their setting or change and other elements affected by the setting or change thereof.

[0047] It is a diagram showing the hardware configuration of the drawing creation support device 100. It is a diagram showing the structure of CAD data of the floor plan. It is a diagram showing the structure of the edit history data. It is a flowchart showing the learning data generation process. It is a diagram showing the structure of the learning data. It is a flowchart showing the learned model generation process. It is a block diagram showing the process of generating and using the learned model. It is a flowchart showing the element order information estimation process. It is a floor plan assuming the entry of a piano. It is a diagram showing the structure of the edit history data. It is a diagram showing the structure of the learning data. It is a flowchart showing the element order information estimation process. It is a block diagram showing the configuration of the network system according to the present embodiment. It is a functional block diagram of the generation AI server 120. It is a flowchart showing the learning data registration process. It is a flowchart showing the element order information acquisition process. It is a flowchart showing the element order information acquisition process.

[0048] 〔First Embodiment〕 Hereinafter, the first embodiment of the present invention will be described. FIGS. 1 to 9 are diagrams showing this embodiment.

[0049] 〔Configuration of the Present Embodiment〕 First, the configuration of the present embodiment will be described. FIG. 1 is a diagram showing the hardware configuration of the drawing creation support device 100.

[0050] As shown in Figure 1, the drawing creation support device 100 consists of a CPU (Central Processing Unit) 30 that controls calculations and the entire system based on a control program, a ROM (Read Only Memory) 32 that stores the control program for the CPU 30 in a predetermined area, a RAM (Random Access Memory) 34 for storing data read from the ROM 32 and other memory, as well as calculation results necessary for the calculation process of the CPU 30, and an I / F (Interface) 38 that mediates data input and output to external devices. These components are connected to each other and enable data exchange via a bus 39, which is a signal line for data transfer.

[0051] The I / F38 is connected to an external device, which includes an input device 40 consisting of a keyboard and mouse that can input data as a human interface, a storage device 42 that stores data and tables as files, and a display device 44 that displays a screen based on an image signal.

[0052] The storage device 42 has CAD (Computer-Aided Design) software and BIM (Building Information Modeling) software (hereinafter collectively referred to as "CAD software") installed on it. CAD software is software that assists in the creation of drawings according to the designer's operations. When the CAD software is requested to be started, the CPU 30 starts the program for the CAD software stored in a predetermined area of ​​the ROM 32 and executes processing according to that program. The designer can start the CAD software and create cross-section drawings, floor plan details, and other architectural drawings.

[0053] Next, the data structure of the storage device 42 will be described. The storage device 42 stores CAD data of cross-section drawings, detailed floor plans, and other architectural drawings.

[0054] Figure 2 shows the structure of the CAD data for a cross-section drawing. As shown in Figure 2, the CAD data for a cross-section drawing is data that constitutes a drawing that details the cross-section of a building, and is composed of data that includes one or more createable or editable elements (hereinafter referred to as "editable elements"). The CAD data for a cross-section drawing is created by the designer using CAD software. The designer creates the cross-section drawing by creating, setting, changing or deleting (hereinafter referred to as "editing") the editable elements in the CAD software. In the example in Figure 2, the editable elements for the floor, walls, and ceiling of the area labeled "internal corridor" and the editable elements for the floor, walls, and ceiling of the area labeled "vestibule" are respectively placed.

[0055] The same applies to CAD data for floor plans and other architectural drawings, which are composed of data containing one or more editing elements.

[0056] The storage device 42 stores editing history data for each CAD data, showing the history of editing elements. The CAD data reflects the final editing results related to the editing history data.

[0057] Figure 3 shows the structure of the editing history data. As shown in Figure 3, the editing history data includes, for each edited editing element, element information 400 related to that editing element and the editing time 402 required to edit that editing element, in the order of editing. The element information 400 includes an element ID for identifying the editing element, the area to be edited by the editing element, and the editing element itself. The editing time 402 can be calculated, for example, by subtracting the editing start time from the editing end time.

[0058] In the example in Figure 3, the first related group of editing elements for the toilet—"toilet bowl," "handwashing counter," "mirror," and "towel rack"—are edited in that order consecutively. These editing elements are assigned element IDs "52," "53," "54," and "55," and the editing times required are 35, 38, 70, and 94 minutes, respectively.

[0059] Furthermore, a second related group is shown, indicating that the editing elements of the toilet—"ventilation fan," "lighting," "storage," and "outlet"—were edited in that order consecutively. These editing elements were assigned element IDs "56," "57," "58," and "59," and the editing times were 94, 48, 42, and 74 minutes, respectively.

[0060] Furthermore, a third related group is shown, indicating that the closet editing elements "shelf board," "hanger pipe," "drawer," and "basket" are edited in that order consecutively. These editing elements are assigned element IDs "60," "61," "62," and "63," and the editing times required are 96, 74, 84, and 22 minutes, respectively.

[0061] Furthermore, a fourth related group is shown, indicating that the kitchen editing elements "flooring," "wall covering," "countertop," and "sink" are edited in that order consecutively. These editing elements are assigned element IDs "64," "65," "66," and "67," and the editing times were 42, 44, 35, and 32 minutes, respectively.

[0062] Since the editing history data is used to create training data, the storage device 42 stores a large amount of editing history data that has been created in the past.

[0063] [Operation of this embodiment] Next, the operation of this embodiment will be described. [Training data generation process] Figure 4 is a flowchart of the training data generation process.

[0064] The training data generation process is a process that generates training data, and when it is executed on the CPU 30, it proceeds to step S100, as shown in Figure 4.

[0065] In step S100, the unprocessed editing history data is retrieved from the storage device 42, the process moves to step S102, the variable n is set to "2", and the process moves to step S104.

[0066] In steps S104 to S108, the element IDs of created or edited editing elements (hereinafter abbreviated as "created or edited element IDs"), the n editing elements (the number indicated by the value of variable n) that were edited consecutively after that editing element, their editing order, and their editing times are obtained from the editing history data acquired in step S100. Hereafter, multiple editing elements and their editing order may be referred to as "element order". Using Figure 3 as an example, we will explain the case where the value of variable n is "2". In the editing history data in Figure 3, each row is arranged in editing order.

[0067] When the second row is targeted, the element IDs "01" to "51" of the previously edited elements are obtained as created or edited element IDs. Since the value of variable n is "2", "toilet" and "handwashing counter" are obtained as edited elements, and "35" and "38" are obtained as editing times.

[0068] When the third row is targeted, the element IDs "01" to "52" of the previously edited elements are obtained as created or edited element IDs. Since the value of variable n is "2", "Handwashing Counter" and "Mirror" are obtained as edited elements, and "38" and "70" are obtained as editing times.

[0069] Next, the process moves to step S110, where the evaluation value is calculated by multiplying the sum of the editing times obtained in step S108 by "-1". In the example in the second line above, editing times of "35" and "38" are obtained, so the evaluation value is calculated as (35 + 38) × -1 = -73. The reason for multiplying by "-1" is to set a higher evaluation value for shorter editing times.

[0070] Next, the process moves to step S112, where the element IDs and element order obtained in steps S104 to S108, along with the evaluation value calculated in step S110, are associated and registered as learning data. Then, the process moves to step S114, where "1" is added to the value of variable n, and finally, the process moves to step S116.

[0071] In step S116, it is determined whether the value of variable n is greater than "4". If it is determined to be less than or equal to "4" (NO), the process proceeds to step S104. Then, steps S104 to S114 are repeated until the value of variable n becomes "4".

[0072] Using Figure 3 as an example, the process from steps S104 to S110 will be explained for the case where the value of variable n is "3".

[0073] When the second row is targeted, the element IDs "01" to "51" of the previously edited elements are obtained as created or edited element IDs. Since the value of variable n is "3", "toilet", "handwashing counter", and "mirror" are obtained as edited elements, and "35", "38", and "70" are obtained as editing times. The evaluation value is calculated as (35 + 38 + 70) × -1 = -143.

[0074] When the third row is targeted, the element IDs of previously edited elements, "01" to "52", are obtained as created or edited element IDs. Since the value of variable n is "3", "handwashing counter", "mirror", and "towel rack" are obtained as edited elements, and "38", "70", and "94" are obtained as editing times. The evaluation value is calculated as (38 + 70 + 94) × -1 = -202.

[0075] Furthermore, using Figure 3 as an example, the process of steps S104 to S110 will be explained for the case where the value of variable n is "4".

[0076] When the second row is targeted, the element IDs "01" to "51" of previously edited elements are obtained as created or edited element IDs. Since the value of variable n is "4", "toilet", "handwashing counter", "mirror", and "towel rack" are obtained as edited elements, and "35", "38", "70", and "94" are obtained as editing times. The evaluation value is calculated as (35 + 38 + 70 + 94) × -1 = -237.

[0077] When the third row is targeted, the element IDs "01" to "52" of the previously edited elements are obtained as created or edited element IDs. Since the value of variable n is "4", "handwashing counter", "mirror", "towel rack", and "ventilation fan" are obtained as edited elements, and "38", "70", "94", and "94" are obtained as editing times. The evaluation value is calculated as (38 + 70 + 94 + 94) × -1 = -296.

[0078] On the other hand, if it is determined in step S116 that the value of variable n is greater than "4" (YES), the process proceeds to step S118 to determine whether the processing in steps S100 to S116 has been completed for all edit history data. If it is determined that the processing has been completed for all edit history data (YES), the process proceeds to step S120.

[0079] In step S120, the learning data in which the element ID and other information were registered in step S112 is stored in the storage device 42.

[0080] Figure 5 shows the structure of the training data. As shown in Figure 5, each row of the training data contains the created or edited element ID 410, element order information 412, and evaluation value 414. The element order information 412 includes the area and element order that will be edited for the edited element.

[0081] The second row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the first row of Figure 3, along with their editing order and evaluation value. The third row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the second row of Figure 3, along with their editing order and evaluation value. The fourth row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the third row of Figure 3, along with their editing order and evaluation value. Furthermore, the sixth row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the fifth row of Figure 3, along with their editing order and evaluation value. The seventh row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the sixth row of Figure 3, along with their editing order and evaluation value. The eighth row of Figure 5 shows two editing elements that were edited consecutively after the editing element in the seventh row of Figure 3, along with their editing order and evaluation value.

[0082] Furthermore, the 10th row of Figure 5 shows the three editing elements that were edited consecutively after the editing element in the 1st row of Figure 3, along with their editing order and evaluation value. The 11th row of Figure 5 shows the three editing elements that were edited consecutively after the editing element in the 2nd row of Figure 3, along with their editing order and evaluation value. Additionally, the 13th row of Figure 5 shows the three editing elements that were edited consecutively after the editing element in the 5th row of Figure 3, along with their editing order and evaluation value. The 14th row of Figure 5 shows the three editing elements that were edited consecutively after the editing element in the 6th row of Figure 3, along with their editing order and evaluation value.

[0083] Furthermore, row 16 of Figure 5 shows the four editing elements that were edited consecutively after the editing element in row 1 of Figure 3, along with their editing order and evaluation values. Similarly, row 18 of Figure 5 shows the four editing elements that were edited consecutively after the editing element in row 5 of Figure 3, along with their editing order and evaluation values.

[0084] [Trained Model Generation Process] Figure 6 is a flowchart showing the trained model generation process.

[0085] Figure 7 is a block diagram showing the process of generating and using a trained model. The trained model generation process is a process performed to generate a trained model, and when executed on the CPU 30, it proceeds to step S200, as shown in Figure 6, to execute the training data analysis process. In the training data analysis process, training data is read from the storage device 42, and the created or edited element ID 410, element sequence information 412, and evaluation value 414 are extracted from the read training data.

[0086] Next, the process moves to step S202. In step S202, as shown in Figure 7, a training dataset is generated based on the information extracted in step S200. The process then moves to step S204, where the generated training dataset is input into the training program, and the training program generates a trained model. The training program includes pre-training parameters and hyperparameters, and performs training based on the input training dataset and hyperparameters, updating the pre-training parameters. As a training method, for example, reinforcement learning can be employed. In reinforcement learning, evaluation values ​​are assigned to combinations of created or edited element IDs and element order information relating to multiple edited elements that are edited consecutively after that edited element, and training is performed to maximize the evaluation values. Finally, a trained model is output as the training result.

[0087] The trained model is trained to maximize the evaluation value 414 based on the created or edited element ID 410, element order information 412, and evaluation value 414. The trained model comprises trained parameters, which are updated from pre-training parameters, and an inference program. The inference program takes the created or edited element ID as input, estimates the element order information from the input created or edited element ID based on the trained parameters, and outputs the estimated element order information. Note that the relationship between the input element ID and the output element order information is determined by the AI's training, so while it shows a similar trend to the content of past training data, there is an ambiguity that prevents it from being an exact match. However, this ambiguity can be reduced by increasing the amount of training data and the training accuracy.

[0088] Next, the process moves to step S206, where the trained model generated in step S204 is stored in the storage device 42, and the series of processes ends.

[0089] [Element Order Information Estimation Process] Figure 8 is a flowchart showing the element order information estimation process.

[0090] The element sequence information estimation process is performed in response to requests from designers or other users, and when it is executed on the CPU 30, it first proceeds to step S300, as shown in Figure 8.

[0091] In step S300, the element IDs of created or edited elements are obtained from the CAD data currently being edited, and the process proceeds to step S302.

[0092] In step S302, the trained model in the storage device 42 is used to estimate multiple element order information with different edited elements or editing orders from the element ID obtained in step S300. The estimation is performed by inputting the created or edited element ID into the trained model and obtaining the element order information output from the trained model. The trained model may be configured to obtain multiple outputs from one input, or it may be configured to obtain multiple outputs by repeating the input and output of one multiple times.

[0093] Next, the process moves to step S304, where, based on the multiple element order information estimated in step S302, one of the multiple element orders is displayed on the display device 44 using one of the multiple display rules. Which display rule to use may be set, for example, by the designer or other user, or by a predetermined algorithm.

[0094] The first display rule is to display the element order with the most editable elements among the multiple estimated element orders.

[0095] The second display rule is to display element sequences that have related groups among the multiple estimated element sequences. For example, if the multiple element sequences estimated from the current editing state are (1) A, B, (2) A, B, C, (3) A, B, C, D, (4) A, B, C, D, E, (5) A, B, C, D, E, F, and A to D are related groups, then according to the second display rule, one of (3) to (5) will be displayed. Whether or not a group is related can be determined, for example, by identifying two or more editing elements and their editing sequences from A to F that appear more than a predetermined number of times in the training data. The same applies to the third and fourth display rules below.

[0096] The third display rule is to display the same element order as a related group among the multiple estimated element orders. For example, if the multiple element orders estimated from the current editing state are (1) to (5) above, according to the third display rule, (3) will be displayed.

[0097] The fourth display rule is to display related groups and related groups from among the estimated multiple element orders, including other editing elements. For example, if the multiple element orders estimated from the current editing state are (1), (2), and (5) above, according to the fourth display rule, A to D will be displayed starting from (5).

[0098] Next, the process moves to step S306. If the designer creates or edits an editable element based on the displayed element order, the created or edited element IDs, including the element ID of that editable element, are obtained from the currently edited CAD data, and the process moves to step S308.

[0099] In step S308, based on the element order displayed in steps S304 and S314 and the element ID obtained in step S306, it is determined whether the edited elements created or edited by the designer differ from the edited elements or editing order displayed in steps S304 and S314. If it is determined that the editing result differs from the estimated result (YES), the process proceeds to step S310.

[0100] In step S310, the display rules used for display in steps S304 and S314 are changed. For example, the display rules are changed to the optimal ones so that the editing results match the estimated results. The timing of changing the display rules does not have to be after each creation or edit, but may be after multiple creations or edits.

[0101] Next, the process moves to step S312, where, similar to the process in step S302, the trained model in the storage device 42 is used to estimate multiple element order information from the element IDs obtained in step S306, and then the process moves to step S314.

[0102] In step S314, similar to the process in step S304, one of the multiple element order information estimated in step S312 is displayed on the display device 44, and the process proceeds to step S316.

[0103] In step S316, it is determined whether the designer has finished editing. If it is determined that editing is complete (YES), the series of processes is terminated.

[0104] On the other hand, if it is determined in step S316 that the designer has not finished editing (NO), the process proceeds to step S306.

[0105] On the other hand, if it is determined in step S308 that the editing result matches the estimated result (NO), the process proceeds to step S312.

[0106] [When considering piano delivery] Next, we will explain the procedures when considering piano delivery.

[0107] Figure 9 is a cross-section drawing that assumes the delivery of a piano. If the designer wants to install a piano with a width of 120 mm in the cross-section drawing of Figure 9 using CAD software, they need to shorten the width of the toilet to prevent interference between the piano and the toilet wall when delivering the piano. However, changing the width of the toilet necessitates editing other editing elements of the toilet. Therefore, if the designer requests the estimation of the order of elements to be edited for the toilet after changing the width, the system will go through steps S300 to S304, retrieve the IDs of created or edited elements from the currently edited CAD data, and estimate and display the element order from the retrieved element IDs. For example, if the element order information for row 16 of Figure 5 is estimated, "toilet bowl → hand washing counter → mirror → towel rack" will be displayed. If these editing elements are already included in the CAD data as shown in Figure 9, these editing elements will be highlighted (for example, displayed in a specific color or pattern) and connected by arrows to display the editing order. If these editing elements are not included in the CAD data, they will be displayed as ghost images (for example, ghost images of the editing elements displayed as wireframes) and connected by arrows to indicate the editing order.

[0108] Then, when the designer creates or edits the "toilet" element based on the estimated and displayed element order, the next element order is estimated and displayed via steps S306 to S314. If the designer's editing result differs from the estimated result, the display rule is changed via step S310.

[0109] [Effects of this embodiment] Next, the effects of this embodiment will be described. In this embodiment, a created or edited element ID is obtained, and using a trained model, multiple element sequence information is estimated from the obtained element ID, and one of the multiple element sequences is displayed based on the estimated multiple element sequence information.

[0110] This allows you to understand multiple editing elements being edited sequentially and their editing order.

[0111] Furthermore, in this embodiment, the element order with the largest number of editable elements among the multiple estimated element orders is displayed.

[0112] This allows designers to perform editing while visualizing larger editing units. Furthermore, in this embodiment, element sequences that appear more than a predetermined number of times in the training data are identified as related groups, and the element sequences that contain related groups among the multiple estimated element sequences are displayed.

[0113] This allows designers to edit while visualizing related groups of elements.

[0114] Furthermore, in this embodiment, element sequences that appear more than a predetermined number of times in the training data are identified as related groups, and element sequences identical to those of the related groups among the multiple estimated element sequences are displayed.

[0115] This allows designers to edit while visualizing related groups of elements.

[0116] Furthermore, in this embodiment, element sequences that appear more than a predetermined number of times in the training data are identified as related groups, and from among the multiple estimated element sequences, the portion containing related groups and other editing elements is displayed.

[0117] This allows designers to edit while visualizing related groups of elements.

[0118] Furthermore, in this embodiment, based on the displayed element order, the created or edited element IDs, including the element IDs of edited elements created or edited by the designer, are obtained. Using a trained model, multiple element order information is estimated from the obtained element IDs, and one of the multiple element orders is displayed based on the estimated multiple element order information.

[0119] This allows the element order to be estimated and displayed for the result of creation or editing based on the displayed element order, making it possible to understand multiple editing elements being edited consecutively and their editing order.

[0120] Furthermore, in this embodiment, the element IDs of created or edited elements, including the element IDs of edited elements created or edited by the designer, are obtained based on the displayed element order, and the display rules are changed based on the displayed element order and the obtained element IDs.

[0121] This allows you to change the display rules according to the result of the element order display and the result of subsequent creation or editing.

[0122] Furthermore, in this embodiment, the trained model is trained to maximize the evaluation value based on training data that includes created or edited element IDs, element order information relating to a plurality of edited elements that are edited consecutively after that edited element and their editing order, and evaluation values ​​relating to the editing of the plurality of edited elements.

[0123] This makes it possible to identify the element order with the highest evaluation value. Furthermore, in this embodiment, an evaluation value is assigned to the editing of multiple edited elements for each combination of an already created or edited element ID and element order information relating to the multiple edited elements that were edited consecutively after that edited element, and a trained model is generated by training to maximize the evaluation value.

[0124] This makes it possible to obtain a trained model that yields an element order with a high evaluation value. In this embodiment, step S300 corresponds to the element information acquisition means of Invention 1, step S302 corresponds to the estimation means of Invention 1, steps S304 and S314 correspond to the output means of Inventions 1 to 6 or 9, or the identification means of Inventions 3 to 5. Furthermore, step S306 corresponds to the second element information acquisition means of Invention 6 or 9, steps S308 and S310 correspond to the output rule changing means of Invention 9, step S312 corresponds to the second estimation means of Invention 6, and the CAD data corresponds to the design information of Inventions 3, 5 or 13.

[0125] Furthermore, in this embodiment, the created or edited element IDs correspond to the element information of inventions 1, 6, 9, or 10.

[0126] [Second Embodiment] Next, a second embodiment of the present invention will be described. Figures 10 to 12 show this embodiment. Figures 3, 5 and 9 will also be referenced.

[0127] This embodiment differs from the first embodiment in that it performs estimation using a first pre-trained model and a second pre-trained model, each trained using evaluation values ​​based on different indicators. Below, only the parts that differ from the first embodiment will be described, and the overlapping parts will be omitted.

[0128] [Configuration of this embodiment] First, the configuration of this embodiment will be described. Figure 10 is a diagram showing the structure of the editing history data.

[0129] The storage device 42 stores, for each CAD data, the editing history data shown in Figure 3, as well as the editing history data shown in Figure 10.

[0130] As shown in Figure 10, the editing history data includes, for each edited editing element, element information 404 related to that editing element and the number of editing items 406 required to edit that editing element, in the order of editing.

[0131] In the example in Figure 10, the first related group of editing elements for the toilet, "toilet bowl," "handwashing counter," "mirror," and "towel rack," are edited in that order consecutively. These editing elements are assigned element IDs "52," "53," "54," and "55," and the number of editable items is 8, 5, 6, and 9, respectively.

[0132] Furthermore, a second related group is shown, indicating that the editing elements for the toilet—"ventilation fan," "lighting," "storage," and "outlet"—are edited in that order consecutively. These editing elements are assigned element IDs "56," "57," "58," and "59," with the number of editable items being 3, 7, 8, and 6 respectively.

[0133] Furthermore, a third related group is shown, indicating that the editing elements of the closet—"shelves," "hanger pipes," "drawers," and "baskets"—are being edited in that order consecutively. These editing elements are assigned element IDs "60," "61," "62," and "63," with the number of editable items being 8, 7, 2, and 4 respectively.

[0134] Furthermore, a fourth related group is shown, indicating that the kitchen editing elements "flooring," "wall covering," "countertop," and "sink" are edited in that order consecutively. These editing elements are assigned element IDs "64," "65," "66," and "67," with the number of editable items being 7, 4, 3, and 8 respectively.

[0135] [Operation of this embodiment] Next, the operation of this embodiment will be described. [Training data generation process] In the training data generation process, training data is generated based on the editing history data in Figure 10, similar to the training data generation in Figure 5.

[0136] Figure 11 shows the structure of the training data. As shown in Figure 11, each row of the training data contains a created or edited element ID 410, element order information 412, and evaluation value 414.

[0137] The second row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the first row of Figure 10, along with their editing order and evaluation value. The third row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the second row of Figure 10, along with their editing order and evaluation value. The fourth row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the third row of Figure 10, along with their editing order and evaluation value. Furthermore, the sixth row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the fifth row of Figure 10, along with their editing order and evaluation value. The seventh row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the sixth row of Figure 10, along with their editing order and evaluation value. The eighth row of Figure 11 shows two editing elements that were edited consecutively after the editing element in the seventh row of Figure 10, along with their editing order and evaluation value.

[0138] Furthermore, the 10th row of Figure 11 shows the three editing elements that were edited consecutively after the editing element in the 1st row of Figure 10, along with their editing order and evaluation value. The 11th row of Figure 11 shows the three editing elements that were edited consecutively after the editing element in the 2nd row of Figure 10, along with their editing order and evaluation value. Additionally, the 13th row of Figure 11 shows the three editing elements that were edited consecutively after the editing element in the 5th row of Figure 10, along with their editing order and evaluation value. The 14th row of Figure 11 shows the three editing elements that were edited consecutively after the editing element in the 6th row of Figure 10, along with their editing order and evaluation value.

[0139] Furthermore, row 16 of Figure 11 shows the four editing elements that were edited consecutively after the editing element in row 1 of Figure 10, along with their editing order and evaluation values, and row 18 of Figure 11 shows the four editing elements that were edited consecutively after the editing element in row 5 of Figure 10, along with their editing order and evaluation values.

[0140] [Trained Model Generation Process] In the trained model generation process, a first trained model is generated by performing training based on the training data in Figure 5, following steps S200 to S204. The first trained model is the same as the trained model in the first embodiment described above.

[0141] In the trained model generation process, following steps S200 to S204, a second trained model is generated by training based on the training data in Figure 11, similar to the generation of the first trained model. The second trained model is trained to maximize the evaluation value 420 based on the created or edited element ID 416, element order information 418, and evaluation value 420.

[0142] Then, the process moves to step S206, where the first trained model and the second trained model generated in step S204 are stored in the storage device 42, and the series of processes is completed.

[0143] [Element Order Information Estimation Process] Figure 12 is a flowchart showing the element order information estimation process.

[0144] The element sequence information estimation process is performed in response to requests from designers or other users. When executed on the CPU 30, it first proceeds to step S350, as shown in Figure 12.

[0145] In step S350, indicator information is obtained regarding the first indicator "editing time" or the second indicator "number of edited items," which indicate the value of the evaluation value. The process then proceeds to step S352, where the first trained model is selected if the indicator related to the obtained indicator information is the first indicator, and the second trained model is selected if the indicator related to the obtained indicator information is the second indicator.

[0146] Next, the process moves to step S354, where the IDs of created or edited elements are obtained from the CAD data currently being edited, and then the process moves to step S356.

[0147] In step S356, the selected trained model from the first and second trained models in the storage device 42 (hereinafter referred to as the "selected trained model") is used to estimate multiple element order information from the element IDs obtained in step S354. The estimation method is the same as the process in step S302 in the first embodiment described above.

[0148] Next, the process moves to step S358, where, similar to the process in step S304, one of the multiple element order information estimated in step S356 is displayed on the display device 44, and the process moves to step S360.

[0149] In step S360, if the designer creates or edits an editable element based on the displayed element order, the created or edited element IDs, including the element ID of that editable element, are obtained from the currently edited CAD data, and the process proceeds to step S362.

[0150] In step S362, based on the element order displayed in steps S358 and S368 and the element ID obtained in step S360, it is determined whether the edited element created or edited by the designer differs from the edited element or editing order displayed in steps S358 and S368. If it is determined that the editing result differs from the estimated result (YES), the process proceeds to step S364.

[0151] In step S364, similar to the process in step S310, the display rules used for display in steps S358 and S368 are changed, and the process proceeds to step S366.

[0152] In step S366, similar to the process in step S356, multiple element order information is estimated from the element IDs obtained in step S360 using the selected and trained model, and the process proceeds to step S368.

[0153] In step S368, similar to the process in step S358, one of the multiple element order information estimated in step S312 is displayed on the display device 44, and the process proceeds to step S370.

[0154] In step S370, it is determined whether the designer has finished editing. If it is determined that editing is complete (YES), the series of processes is terminated.

[0155] On the other hand, if it is determined in step S370 that the designer has not finished editing (NO), the process proceeds to step S360.

[0156] On the other hand, if it is determined in step S362 that the editing result matches the estimated result (NO), the process proceeds to step S366.

[0157] [When considering piano delivery] Next, we will explain the procedures when considering piano delivery.

[0158] If a designer wants to install a piano with a width of 120 mm in the cross-section drawing of Figure 9 using CAD software, they need to shorten the width of the toilet to prevent interference between the piano and the toilet wall when bringing the piano in. However, changing the width of the toilet necessitates editing other elements of the toilet. Therefore, the designer requests an estimation of the order in which elements should be edited for the toilet after changing its width. If the designer wants to obtain an element order that minimizes editing time, they select "editing time" as the indicator, and after steps S350 to S352, the first trained model is selected. Then, after steps S354 to S358, the IDs of created or edited elements are obtained from the CAD data currently being edited, and the element order is estimated and displayed from the obtained element IDs by the first trained model.

[0159] In contrast, if the designer wants to obtain an element order that minimizes the number of editing items, they select "number of editing items" as the indicator, and after steps S350 to S352, the second trained model is selected. Then, after steps S354 to S358, the IDs of created or edited elements are obtained from the CAD data currently being edited, and the second trained model estimates and displays the element order from the obtained element IDs.

[0160] [Effects of this embodiment] Next, the effects of this embodiment will be described. In this embodiment, index information relating to the first index or the second index is acquired, either the first trained model or the second trained model is selected based on the acquired index information, the created or edited element IDs are acquired, and multiple element sequence information is estimated from the acquired element IDs using the selected trained model.

[0161] This makes it possible to identify the order of elements that have high evaluation values ​​based on the first or second indicator.

[0162] In this embodiment, step S350 corresponds to the index information acquisition means of Invention 11, step S352 corresponds to the model selection means of Invention 11, steps S354 and S360 correspond to the element information acquisition means of Invention 1, and steps S356 and S366 correspond to the estimation means of Invention 1 or 11. Furthermore, steps S358 and S368 correspond to the output means of Invention 1.

[0163] [Third Embodiment] Next, a third embodiment of the present invention will be described. Figures 13 to 16 show this embodiment. Figures 3, 5 and 9 will also be referenced.

[0164] This embodiment differs from the first embodiment in that it utilizes a large language model. Below, only the differences from the first embodiment will be described, and the overlapping parts will be omitted.

[0165] [Configuration of this embodiment] First, the configuration of this embodiment will be described. Figure 13 is a block diagram showing the configuration of the network system according to this embodiment.

[0166] As shown in Figure 13, the Internet 199 is connected to a drawing creation support device 100 and a generation AI server 120 that generates response information using an AI (Artificial Intelligence) model in response to requests, enabling communication between the two.

[0167] [Generating AI Server 120] Next, the configuration of the generating AI server 120 will be described. Similar to the drawing creation support device 100, the generating AI server 120 has a hardware configuration similar to that of a general computer, with a CPU, ROM, RAM, and I / F connected by a bus, and is configured as, for example, a cloud server.

[0168] Figure 14 is a functional block diagram of the generation AI server 120. As shown in Figure 14, the generation AI server 120 is configured to include a plurality of AI models 50, an AI model control unit 52 that controls the AI ​​models 50, and a knowledge base 54 that registers data that the AI ​​models 50 refer to for inference.

[0169] AI Model 50 is an AI model trained on a large dataset and is a highly versatile model capable of performing various tasks. For example, AI Model 50 can employ a large-scale language model. A large-scale language model is a deep learning model that pre-trains a language model, which models human speech based on its probability of occurrence, on a vast amount of data. When a prompt is input, the large-scale language model statistically infers the probability of generating the next word from the sentence contained in the input prompt and outputs the inference result. For example, publicly known techniques described on the internet sites "https: / / chatgpt-lab.com / n / n418d3aa56f0b" and "https: / / agirobots.com / chatgpt-mechanism-and-problem / " can be used as large-scale language models. More specifically, for example, Titan Text G1 - Express, Titan Text G1 - Lite, Titan Image Generator G1, Titan Embeddings G1 - Text, Titan Embeddings Text V2, Titan Multimodal Embeddings G1, Claude, Claude Instant, Claude 3 Sonnet, Claude 3 Haiku, Claude 3 Opus, Jurassic-2 Mid, Jurassic-2 Ultra, Command, Command Light, Command R, Command R+, Embed English, Embed Multilingual, Llama 2 Chat 13B, Llama 2 Chat 70B, Llama 2 13B, Llama 2 70B, Llama 3 8b Instruct, Llama 3 70b Instruct, Mistral 7B Instruct, Mixtral 8X7B Instruct, Mistral Large, and Stable Diffusion XL can be adopted.

[0170] The AI ​​model control unit 52 selects one of the multiple AI models 50 to be used for inference in response to a selection request from the request processing unit 58. When a reference request is input from the request processing unit 58, the AI ​​model control unit 52 causes the selected AI model 50 (hereinafter referred to as the "selected AI model") to access data in the knowledge base 54 according to the input reference request. When a prompt is input from the request processing unit 58, the AI ​​model control unit 52 inputs the prompt to the selected AI model. Finally, when an execution request is input from the request processing unit 58, the AI ​​model control unit 52 causes the selected AI model to perform inference according to the input execution request, obtains the inference result from the selected AI model, and outputs the obtained inference result to the request processing unit 58.

[0171] The knowledge base 54 can register training data. The information registered in the knowledge base 54 is in a data format that the AI ​​model 50 can access (for example, vector data).

[0172] The generation AI server 120 is further configured to include a request receiving unit 56 that receives requests, a request processing unit 58 that processes the requests received by the request receiving unit 56, and a response information transmission unit 60 that transmits response information for the requests received by the request receiving unit 56 to the drawing creation support device 100.

[0173] The request receiving unit 56 receives a request from the drawing creation support device 100 for the generation of response information and outputs the received request to the request processing unit 58. The request includes (1) an element ID that has been created or edited, (2) a generation request to generate element sequence information relating to a plurality of editing elements to be edited consecutively after an edited element that has been created or edited, and the editing order thereof, (3) a selection request to select an AI model 50, and (4) a reference request to refer to the learning data of the knowledge base 54. (3) and (4) are not mandatory but are included as additional elements.

[0174] If the request received by the request receiving unit 56 includes a selection request or a reference request, the request processing unit 58 outputs the selection request or reference request to the AI ​​model control unit 52. It also generates a prompt to instruct the AI ​​model 50 based on the request received by the request receiving unit 56. For example, the prompt may request the AI ​​model 50 to generate element sequence information relating to multiple editing elements to be edited consecutively after a created or edited editing element, and their editing order, based on the created or edited element ID, provided that the evaluation value is above a predetermined value. The generated prompt and execution request are then output to the AI ​​model control unit 52, and if the AI ​​model control unit 52 inputs an inference result in response to the execution request, the input inference result is output to the response information transmission unit 60.

[0175] The response information transmission unit 60 transmits the response information, including the inference result input from the request processing unit 58, to the drawing creation support device 100.

[0176] The generation AI server 120 is further configured to include a request receiving unit 62 that receives requests and a learning data registration unit 64 that registers learning data in the knowledge base 54.

[0177] The request receiving unit 62 receives a request from the drawing creation support device 100 to register learning data, and outputs the received request to the learning data registration unit 64. The request includes (1) learning data.

[0178] The learning data registration unit 64 stores the learning data included in the request received by the request receiving unit 62 in storage (not shown) and converts it into a data format that the AI ​​model 50 can refer to (for example, vector data). Vector data can be generated by a technique (embedding) that converts data including characters, images, and sounds into numerical vectors. The converted learning data is then registered in the knowledge base 54. The AI ​​model control unit 52 makes the selected AI model refer to the learning data in response to a referencing request from the request processing unit 58.

[0179] [Operation of this embodiment] Next, the operation of this embodiment will be described. [Learning data registration process] Figure 15 is a flowchart of the learning data registration process.

[0180] The learning data registration process is performed in response to requests from designers and other users. When it is executed on the CPU 30, it first proceeds to step S400, as shown in Figure 15.

[0181] In step S400, the learning data shown in Figure 5 is acquired from the storage device 42, and the process proceeds to step S402.

[0182] In step S402, a request is sent to the generating AI server 120 requesting the registration of training data. The request includes (1) the training data acquired in step S400.

[0183] When the process in step S402 is completed, the series of processes ends. [Element Order Information Acquisition Process] Figure 16 is a flowchart of the element order information acquisition process.

[0184] The element sequence information acquisition process is performed in response to a request from the designer or other user, and when it is executed on the CPU 30, it first proceeds to step S500, as shown in Figure 16.

[0185] In step S500, the element IDs of created or edited elements are obtained from the CAD data currently being edited, and the process proceeds to step S502.

[0186] In step S502, a request is sent to the generation AI server 120 requesting the generation of answer information based on the element ID obtained in step S500. The request includes (1) the element ID obtained in step S500, (2) a generation request to generate multiple element sequence information relating to multiple editing elements to be edited consecutively after an already created or edited editing element and their editing order, wherein the editing elements or editing order differ, (3) a selection request to select a predetermined AI model 50, and (4) a reference request to refer to the learning data in the knowledge base 54.

[0187] Next, the process moves to step S504, where response information is received from the generating AI server 120. Then, the process moves to step S506, where, similar to the process in step S304, one of the multiple element sequences is displayed on the display device 44 based on the multiple element sequence information contained in the received response information, and the process moves to step S508.

[0188] In step S508, if the designer creates or edits an editable element based on the displayed element order, the created or edited element IDs, including the element ID of that editable element, are obtained from the currently edited CAD data, and the process proceeds to step S510.

[0189] In step S510, based on the element order displayed in steps S506 and S518 and the element ID obtained in step S508, it is determined whether the edited element created or edited by the designer differs from the edited element or editing order displayed in steps S506 and S518. If it is determined that the editing result differs from the inference result (YES), the process proceeds to step S512.

[0190] In step S512, similar to the process in step S310, the display rules used for display in steps S506 and S518 are changed, and the process proceeds to step S514.

[0191] In step S514, similar to the process in step S502, a request is sent to the generation AI server 120 requesting the generation of response information based on the element ID obtained in step S508, and the process proceeds to step S516.

[0192] In step S516, similar to the process in step S504, the system receives response information from the generating AI server 120, proceeds to step S518, and similar to the process in step S304, displays one of the multiple element sequences on the display device 44 based on the multiple element sequence information contained in the received response information, and proceeds to step S520.

[0193] In step S520, it is determined whether the designer has finished editing. If it is determined that editing is complete (YES), the series of processes is terminated.

[0194] On the other hand, if it is determined in step S520 that the designer has not finished editing (NO), the process proceeds to step S508.

[0195] On the other hand, if it is determined in step S510 that the editing result matches the inference result (NO), the process proceeds to step S514.

[0196] [When considering piano delivery] Next, we will explain the procedures when considering piano delivery.

[0197] If a designer wants to install a piano with a width of 120 mm in the cross-section drawing of Figure 9 using CAD software, they need to shorten the width of the toilet to prevent interference between the piano and the toilet wall when bringing the piano in. However, changing the width of the toilet necessitates editing other editing elements of the toilet. Therefore, after changing the width of the toilet, the designer requests inference of the order of elements to be edited for the toilet. After steps S500 to S506, created or edited element IDs are obtained from the CAD data currently being edited, and the element order is inferred and displayed from the obtained element IDs. In the inference, the learning data of the knowledge base 54 is referenced by the selected AI model. The method of displaying the element order is the same as in the first embodiment described above.

[0198] Then, when the designer creates or edits the editing element "toilet" based on the inferred and displayed element order, the next element order is inferred and displayed via steps S508 to S518. If the designer's editing result differs from the inferred result, the display rule is changed via step S512.

[0199] [Effects of this embodiment] Next, the effects of this embodiment will be described. In this embodiment, a request is input to the AI ​​model 50 that includes a generation request to generate multiple element sequence information that includes an already created or edited element ID, and which includes element sequence information relating to a plurality of edited elements to be edited consecutively after that edited element and their editing order, wherein the element sequence information has different edited elements or editing orders. In response to the request, the AI ​​model 50 outputs a plurality of element sequence information and displays one of the plurality of element sequences based on the obtained plurality of element sequence information.

[0200] This allows you to understand multiple editing elements being edited sequentially and their editing order.

[0201] Furthermore, in this embodiment, learning data including created or edited element IDs, multiple edited elements that are edited consecutively after the edited element, element order information relating to the editing order, and evaluation values ​​relating to the editing of the multiple edited elements is registered in the knowledge base 54, and in response to a request, the learning data in the knowledge base 54 is referenced to obtain the multiple element order information output from the AI ​​model 50.

[0202] This makes it possible to identify the element order with the highest evaluation value. Furthermore, in this embodiment, based on the displayed element order, the system obtains the element IDs of edited elements created or edited by the designer, including the element IDs of the edited elements, and inputs a request to the AI ​​model 50 that includes the obtained element IDs and a request to generate element order information relating to a plurality of edited elements that are edited consecutively after that edited element and their editing order, wherein the element order information is different in terms of the edited elements or editing order. In response to the request, the system obtains the plurality of element order information output from the AI ​​model 50 and displays one of the plurality of element orders based on the obtained plurality of element order information.

[0203] This allows the element order to be inferred and displayed for the result of creation or editing based on the displayed element order, making it possible to understand multiple editing elements being edited consecutively and their editing order.

[0204] [Fourth Embodiment] Next, a fourth embodiment of the present invention will be described. Figure 17 is a diagram showing this embodiment. Figures 5, 9, 11 and 15 will also be referenced.

[0205] This embodiment differs from the second and third embodiments in that it causes the AI ​​model 50 to refer to first training data including evaluation values ​​based on a first indicator or second training data including evaluation values ​​based on a second indicator. Below, only the parts that differ from the second and third embodiments will be described, and the overlapping parts will be omitted from the explanation.

[0206] [Operation of this Embodiment] Next, the operation of this embodiment will be described. [Learning Data Registration Process] The learning data registration process is a process that is executed in response to a request from a designer or other user. When it is executed on the CPU 30, it first proceeds to step S400, as shown in Figure 15.

[0207] In step S400, the learning data shown in Figure 5 and Figure 11 are acquired from the storage device 42, and the process proceeds to step S402.

[0208] In step S402, a request is sent to the generating AI server 120 requesting the registration of training data. The request includes (1) the training data shown in Figure 5 acquired in step S400 as the first training data, and (2) the training data shown in Figure 11 acquired in step S400 as the second training data.

[0209] When the process in step S402 is completed, the series of processes ends. [Element Order Information Acquisition Process] Figure 17 is a flowchart of the element order information acquisition process.

[0210] The element sequence information acquisition process is performed in response to a request from the designer or other user, and when it is executed on the CPU 30, it first proceeds to step S550, as shown in Figure 17.

[0211] In step S550, indicator information related to the first indicator "editing time" or the second indicator "number of edited items," which indicate the value of the evaluation value, is obtained. The process then moves to step S552, where the IDs of created or edited elements are obtained from the CAD data currently being edited, and the process moves to step S554.

[0212] In step S554, a request is sent to the generation AI server 120 requesting the generation of response information based on the index information obtained in step S550 and the element ID obtained in step S552. The request includes: (1) the element ID obtained in step S552; (2) a generation request to generate multiple element sequence information relating to multiple editing elements to be edited consecutively after an already created or edited editing element and their editing order, wherein the element sequence information is different in editing elements or editing order; (3) a selection request to select a predetermined AI model 50; and (4) a reference request to refer to the first learning data in the knowledge base 54 if the index related to the index information obtained in step S550 is the first index, or a reference request to refer to the second learning data in the knowledge base 54 if the index related to the index information obtained in step S550 is the second index.

[0213] Next, the process moves to step S556, where response information is received from the generating AI server 120. Then, the process moves to step S558, where, similar to the process in step S304, one of the multiple element sequences is displayed on the display device 44 based on the multiple element sequence information contained in the received response information, and the process moves to step S560.

[0214] In step S560, if the designer creates or edits an editable element based on the displayed element order, the created or edited element IDs, including the element ID of that editable element, are obtained from the currently edited CAD data, and the process proceeds to step S562.

[0215] In step S562, based on the element order displayed in steps S558 and S570 and the element ID obtained in step S508, it is determined whether the edited element created or edited by the designer differs from the edited element or editing order displayed in steps S558 and S570. If it is determined that the editing result differs from the inference result (YES), the process proceeds to step S564.

[0216] In step S564, similar to the process in step S310, the display rules used for display in steps S558 and S570 are changed, and the process proceeds to step S566.

[0217] In step S566, similar to the process in step S554, a request is sent to the generation AI server 120 requesting the generation of response information based on the indicator information obtained in step S550 and the element ID obtained in step S560, and the process proceeds to step S568.

[0218] In step S568, similar to the process in step S556, response information is received from the generating AI server 120, and the process moves to step S570, where, similar to the process in step S304, one of the multiple element sequences based on the multiple element sequence information contained in the received response information is displayed on the display device 44, and the process moves to step S572.

[0219] In step S572, it is determined whether the designer has finished editing. If it is determined that editing is complete (YES), the series of processes is terminated.

[0220] On the other hand, if it is determined in step S572 that the designer has not finished editing (NO), the process proceeds to step S560.

[0221] On the other hand, if it is determined in step S562 that the editing result matches the inference result (NO), the process proceeds to step S566.

[0222] [When considering piano delivery] Next, we will explain the procedures when considering piano delivery.

[0223] If a designer wants to install a piano with a width of 120 mm in the cross-section drawing of Figure 9 using CAD software, they need to shorten the width of the toilet to prevent interference between the piano and the toilet wall when bringing the piano in. However, changing the width of the toilet necessitates editing other editing elements of the toilet. Therefore, the designer requests inference on the order in which elements to be edited for the toilet after changing its width. If the designer wants to obtain an element order that minimizes editing time, they select "editing time" as the indicator. After steps S550 to S558, created or edited element IDs are obtained from the CAD data currently being edited, and element order information is inferred and displayed from the obtained element IDs. In the inference, the first training data of the knowledge base 54 is referenced by the selected AI model.

[0224] In contrast, if the designer wants to obtain an element order that minimizes the number of editable items, they can select "number of editable items" as the indicator. This will trigger steps S550 to S558, during which the IDs of created or edited elements will be retrieved from the currently edited CAD data. The element order information will then be inferred and displayed from the retrieved element IDs. In the inference process, the selected AI model will refer to the second training data in the knowledge base 54.

[0225] [Effects of this embodiment] Next, the effects of this embodiment will be described. In this embodiment, first learning data including evaluation values ​​based on the first indicator and second learning data including evaluation values ​​based on the second indicator are registered in the knowledge base 54, indicator information related to the first indicator or the second indicator is obtained, and a request is input to the AI ​​model 50 that includes a request to refer to either the first learning data or the second learning data in the knowledge base 54 based on the obtained indicator information.

[0226] This makes it possible to identify the order of elements that have high evaluation values ​​based on the first or second indicator.

[0227] [Modifications] In the first to fourth embodiments and their modifications described above, the display rules of the first to fourth were adopted. However, the invention is not limited to these, and any other display rules can be adopted, such as a display rule that shows the fewest number of editing elements, an element order greater than or equal to a predetermined value, less than or equal to a predetermined value, or an element order of a predetermined rank.

[0228] Furthermore, in the first to fourth embodiments and their variations described above, the element order is displayed according to one of the multiple display rules, but the system is not limited to this, and the element order can be displayed according to a predetermined display rule without providing multiple display rules.

[0229] Furthermore, while one element order was displayed in the first to fourth embodiments and their variations described above, the system is not limited to this, and multiple element orders can be displayed.

[0230] Furthermore, while the labeling rules were changed in the first to fourth embodiments and their variations described above, the system is not limited to these, and configurations that do not change the labeling rules can also be adopted.

[0231] Furthermore, in the first and second embodiments and their modifications described above, estimation and display were repeated multiple times, but the system is not limited to this, and a configuration in which estimation and display are performed only once can also be adopted. The same applies to the third and fourth embodiments and their modifications described above.

[0232] Furthermore, while reinforcement learning was employed as the learning method in the first and second embodiments and their variations described above, the method is not limited to this, and supervised learning, semi-supervised learning, unsupervised learning, deep learning, or any other learning method can be employed.

[0233] Furthermore, in the first to fourth embodiments and their variations described above, the training data is composed of element order information relating to the created or edited element ID, a plurality of edited elements that are edited consecutively after that edited element and their editing order, and evaluation values ​​relating to the editing of the plurality of edited elements. However, it is not limited to this, and can be composed without including evaluation values. In this case, the training data is composed of element order information relating to the created or edited element ID, a plurality of edited elements that are edited consecutively after that edited element and their editing order.

[0234] Furthermore, in the first and second embodiments and their variations described above, the trained model was one that had been trained based on created or edited element IDs, element order information, and evaluation values. However, it is not limited to this, and a model trained based on created or edited element IDs and element order information can also be used.

[0235] Furthermore, in the second embodiment and its modified form, the first trained model and the second trained model can be configured as a single trained model.

[0236] Furthermore, in the third and fourth embodiments and their variations, the AI ​​model 50 can be configured as the trained model in the first and second embodiments and their variations.

[0237] Furthermore, in the first to fourth embodiments and their variations described above, an element sequence with a predetermined or greater number of occurrences in the training data was identified. However, the invention is not limited to this, and an element sequence with a predetermined or greater number of occurrences in the editing history data can also be identified.

[0238] Furthermore, while the first to fourth embodiments and their variations described above handled a maximum of four editing elements and their editing order, the system is not limited to this, and can handle five or more editing elements and their editing order. There is no need to limit the number of editing elements; any number of editing elements is sufficient.

[0239] Furthermore, in the first to fourth embodiments and their variations described above, training data was generated by obtaining all combinations of 2 to 4 editing elements from the editing history data regarding the element order. However, the model is not limited to this, and training data can be generated by obtaining element orders that appear more than a predetermined number of times from the editing history data. In this case, a trained model that has been trained on multiple editing history data sets may be used to estimate the element orders with high occurrence frequencies. This makes it possible to learn or infer the element orders with high occurrence frequencies within the editing history data.

[0240] Furthermore, in the first to fourth embodiments and their variations described above, the editing history data is configured as separate data from the CAD data, but it is not limited to this and can be included in the CAD data and configured as an integrated entity.

[0241] Furthermore, in the third and fourth embodiments and their modifications described above, the AI ​​model 50 was made to refer to the learning data in the knowledge base 54. However, the AI ​​model 50 is not limited to this, and the learning data to be referenced in the knowledge base 54 can be included in the request. This allows the AI ​​model to be applied even in configurations that do not have a knowledge base 54. Specifically, for example, the following configuration can be adopted.

[0242] [Invention A1] An invention comprising: an element information acquisition means for acquiring element information relating to created or edited elements in design information; an input means for inputting a request to an AI model that includes element sequence information relating to a plurality of elements and their editing order that are edited consecutively after the element relating to the element information (hereinafter, in Inventions A1 to A5, "a plurality of elements and their editing order" is referred to as "element sequence"), which includes the element information acquired by the element information acquisition means and includes a request to generate a plurality of such element sequence information with different elements or editing orders; an acquisition means for acquiring the plurality of element sequence information output from the AI ​​model in response to the request; and an output means for outputting one or more of the plurality of such element sequences based on the plurality of element sequence information acquired by the acquisition means, wherein the request includes element information relating to created or edited elements, and reference information including element sequence information relating to a plurality of elements and their editing order that are edited consecutively after the element.

[0243] This allows you to understand multiple elements being edited sequentially and the order in which they are edited.

[0244] [Invention A2] In Invention A1, the request includes element information relating to an element that has been created or edited, element sequence information relating to a plurality of elements to be edited consecutively after the element and their editing order, and reference information including evaluation values ​​relating to the editing of the plurality of elements.

[0245] This makes it possible to understand the order of elements with high evaluation values. Embodiments of Inventions A1 and A2 will be described as variations of the third embodiment described above. In step S502, a request for the generation of response information is sent to the generation AI server 120. The request includes (1) the element ID obtained in step S500, (2) a generation request to generate element order information relating to a plurality of editing elements to be edited consecutively after an edited or created editing element and their editing order, (3) a selection request to select a predetermined AI model 50, and (4) the learning data obtained in step S400.

[0246] [Invention A3] Invention A2 is further provided with an indicator information acquisition means for acquiring indicator information relating to a first indicator that shows an indicator of the value of the evaluation value or a second indicator different from the first indicator, wherein the input means inputs the request to the AI ​​model, which includes either first reference information including the element information, the element sequence information and the evaluation value based on the first indicator, or second reference information including the element information, the element sequence information and the evaluation value based on the second indicator, based on the indicator information acquired by the indicator information acquisition means.

[0247] This makes it possible to identify the order of elements that have high evaluation values ​​based on the first or second indicator.

[0248] An embodiment of Invention A3 will be described as a modification of the fourth embodiment described above. In step S554, a request for the generation of response information is sent to the generation AI server 120. The request includes (1) an element ID obtained in step S552, (2) a generation request to generate element sequence information relating to a plurality of editing elements to be edited consecutively after an edited or created editing element and their editing order, (3) a selection request to select a predetermined AI model 50, and (4) first learning data obtained in step S400 if the index related to the index information obtained in step S550 is a first index, or second learning data obtained in step S400 if the index related to the index information obtained in step S550 is a second index.

[0249] [Invention A4] In Inventions A1 to A3, the plurality of elements are elements whose setting or modification requires human judgment, and whose setting or modification affects other elements.

[0250] [Invention A5] In Inventions A1 to A3, the design information is design information for designing buildings.

[0251] Furthermore, in the third and fourth embodiments and their modifications described above, the AI ​​model 50 was made to refer to information in the knowledge base 54. However, the AI ​​model 50 is not limited to this, and information to be referenced in the knowledge base 54 (for example, the training data in Figure 5 or Figure 11) can be obtained by web search or the like, and the AI ​​model 50 can be made to refer to the search results and perform inference. This configuration can be realized, for example, by RAG (Retrieval Augmented Generation).

[0252] Furthermore, while the first to fourth embodiments and their variations described above used a trained model or AI model 50, the system is not limited to these, and for example, the following configuration can be adopted.

[0253] [Invention B1] The invention comprises: an element information acquisition means for acquiring element information relating to created or edited elements in design information; a storage means for storing element sequence information relating to a plurality of elements and their editing order that are edited consecutively after a created or edited element (hereinafter, in Inventions B1 to B14, "a plurality of elements and their editing order" is referred to as "element sequence") in association with the element information relating to the created or edited element; a search means for searching for a plurality of element sequence information with different elements or editing orders that corresponds to the element information acquired by the element information acquisition means; and an output means for outputting one or more element sequences from the plurality of element sequence information retrieved by the search means.

[0254] This outputs one or more element sequences, allowing you to understand multiple elements being edited sequentially and their editing order.

[0255] [Invention B2] In Invention B1, the output means outputs the element sequence with the largest number of elements among the plurality of element sequences.

[0256] This allows editing to be performed while visualizing a large editing unit. [Invention B3] Invention B1 is further provided with a identifying means for identifying element sequences whose occurrence frequency in the design information is predetermined or greater as related groups, and the output means outputs element sequences from among the plurality of element sequences that have related groups identified by the identifying means.

[0257] This allows editing to be performed while visualizing related groups. [Invention B4] In Invention B3, the output means outputs the same element sequence as the related group identified by the identification means from among the plurality of element sequences.

[0258] [Invention B5] Invention B1 is further provided with a identifying means for identifying element sequences whose occurrence frequency in the design information is predetermined or greater as related groups, and the output means outputs the portion of the related group from among the plurality of element sequences, including the related group identified by the identifying means and other elements.

[0259] This allows editing to be performed while visualizing related groups. [Invention B6] Inventions B1 to B5 include a second element information acquisition means for acquiring element information relating to elements created or edited based on the element order output by the output means, and a second search means for searching the storage means for a plurality of element order information with different elements or editing orders that correspond to the element information acquired by the second element information acquisition means.

[0260] This allows you to obtain multiple element order information for the result created or edited based on the output element order, making it possible to understand multiple elements being edited sequentially and their editing order.

[0261] [Invention B7] Inventions B1 to B5 include a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means, and an estimation means for estimating a plurality of element order information with different elements or editing orders from the element information acquired by the second element information acquisition means, using a trained model trained on training data including element information relating to a created or edited element, and element order information relating to a plurality of elements edited consecutively after the element and their editing order.

[0262] This allows you to obtain multiple element order information for the result created or edited based on the output element order, making it possible to understand multiple elements being edited sequentially and their editing order.

[0263] [Invention B8] Inventions B1 to B5 include an input means for inputting a request to an AI model that includes element information acquired by the second element information acquisition means, and element sequence information relating to a plurality of elements to be edited consecutively after the element relating to the element information and the editing order thereof, wherein the request includes a request to generate a plurality of such element sequence information that differs in elements or editing order, and an acquisition means for acquiring the plurality of element sequence information output from the AI ​​model in response to the request.

[0264] This allows you to obtain multiple element order information for the result created or edited based on the output element order, making it possible to understand multiple elements being edited sequentially and their editing order.

[0265] [Invention B9] Inventions B1 to B5 include a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means, and an output rule changing means for changing the output rules of the output means based on the element order output by the output means and the element information acquired by the second element information acquisition means.

[0266] This allows the output rules to be changed according to the output results of the output device and the subsequent creation or editing results.

[0267] [Invention B10] In Invention B1, the storage means stores element sequence information relating to a plurality of elements to be edited consecutively after a created or edited element and the editing order thereof, in association with element information relating to the created or edited element and evaluation values ​​relating to the editing of the plurality of elements, and the search means searches for a plurality of element sequence information corresponding to the element information acquired by the element information acquisition means, the evaluation values ​​of which are equal to or greater than a predetermined value.

[0268] This makes it possible to identify element sequences with high evaluation values. Embodiments of Inventions B1 and B10 will be described as modifications of the first embodiment described above. The storage device 42 stores an element sequence information table having a data structure similar to the learning data in Figure 5. In step S302, multiple element sequence information corresponding to the element ID acquired in step S300 that has an evaluation value of or greater than a predetermined value is searched from the element sequence information table. In step S312, multiple element sequence information corresponding to the element ID acquired in step S306 that has an evaluation value of or greater than a predetermined value is searched from the element sequence information table.

[0269] [Invention B11] In Invention B10, the storage means includes a first storage means for storing the element sequence information in association with the evaluation value based on the element information and a first index indicating an index of the value of the evaluation value, and a second storage means for storing the element sequence information in association with the evaluation value based on the element information and a second index different from the first index, and comprises an index information acquisition means for acquiring index information relating to the first index or the second index, and a storage means selection means for selecting either the first storage means or the second storage means based on the index information acquired by the index information acquisition means, and the search means searches for a plurality of the element sequence information from the storage means selected by the storage means selection means.

[0270] This makes it possible to identify the order of elements that have high evaluation values ​​based on the first or second indicator.

[0271] An embodiment of Invention B11 will be described as a modification of the second embodiment described above. The storage device 42 stores a first element sequence information table having a data structure similar to the learning data in Figure 5, and a second element sequence information table having a data structure similar to the learning data in Figure 11. In step S352, if the index related to the index information acquired in step S350 is the first index, the first element sequence information table is selected, and if the index related to the acquired index information is the second index, the second element sequence information table is selected. In step S356, a plurality of element sequence information corresponding to the element ID acquired in step S354 with an evaluation value of a predetermined value or higher is searched from the element sequence information table selected in step S352. In step S366, a plurality of element sequence information corresponding to the element ID acquired in step S360 with an evaluation value of a predetermined value or higher is searched from the element sequence information table selected in step S352.

[0272] [Invention B12] In Invention B10, the storage means stores the element information, the element sequence information, the evaluation value based on a first index indicating an index of the value of the evaluation value, and index information relating to the first index in association with each other, and also stores the element information, the element sequence information, the evaluation value based on a second index different from the first index, and index information relating to the second index in association with each other, and includes an index information acquisition means for acquiring index information relating to the first index or the second index, and the search means searches for a plurality of the element sequence information corresponding to the element information acquired by the element information acquisition means and the index information acquired by the index information acquisition means, of which the evaluation value is greater than or equal to a predetermined value.

[0273] This makes it possible to identify the order of elements that have high evaluation values ​​based on the first or second indicator.

[0274] An embodiment of Invention B12 will be described as a modification of the second embodiment described above. The storage device 42 stores an element sequence information table in which, for each row, (1) created or edited element ID 410, element sequence information 412, evaluation value 414, and index information relating to the first index, or (2) created or edited element ID 416, element sequence information 418, evaluation value 420, and index information relating to the second index are registered. In step S356, a plurality of element sequence information corresponding to the element ID obtained in step S354 and the index information obtained in step S350, in which the evaluation value is greater than or equal to a predetermined value, is searched from the element sequence information table. In step S366, a plurality of element sequence information corresponding to the element ID obtained in step S360 and the index information obtained in step S350, in which the evaluation value is greater than or equal to a predetermined value, is searched from the element sequence information table.

[0275] [Invention B13] In Inventions B1 to B5, the plurality of elements are elements whose setting or modification requires human judgment, and whose setting or modification affects other elements.

[0276] [Invention B14] In Inventions B1 to B5, the design information is design information for designing buildings.

[0277] Furthermore, in inventions B1 to B14 and their modifications, storing element sequence information in association with element information, etc., includes, for example, (1) storing element sequence information and element information, etc., in direct association, such as registering them in the same record, and (2) storing them via one or more intermediate pieces of information, such as providing a table for registering element sequence information and intermediate information in association, and a table for registering element information, etc., and intermediate information in association. In other words, any data structure can be adopted as long as it is possible to trace element sequence information from element information, etc. Note that element sequence information only needs to be stored in the storage means in association with element information, etc., and it is not necessarily required to store element information, etc., in the storage means.

[0278] Furthermore, in inventions B1 to B14 and their modifications, the storage means stores element sequence information by any means and at any time. The element sequence information may be stored in advance, or it may not be stored in advance, but rather stored by external input or the like during the operation of the drawing creation support device 100.

[0279] Furthermore, in the third and fourth embodiments and their variations described above, the prompt is set to request the AI ​​model 50 to generate element sequence information relating to a plurality of editing elements to be edited consecutively after a created or edited editing element, and their editing order, based on the created or edited element ID, provided that the evaluation value is above a predetermined value. However, the prompt is not limited to this, and the prompt can be set to request the AI ​​model 50 to generate element sequence information relating to a plurality of editing elements to be edited consecutively after a created or edited editing element, and their editing order.

[0280] Furthermore, in the third and fourth embodiments and their modifications described above, the training data shown in Figure 5 or Figure 11 was registered in the knowledge base 54. However, the knowledge base 54 can also be used to register (1) editing history data, (2) reference information including element information relating to created or edited elements, and element order information relating to a plurality of elements to be edited consecutively after the element and their editing order, or (3) reference information including element information relating to created or edited elements, element order information relating to a plurality of elements to be edited consecutively after the element and their editing order, and evaluation values ​​relating to the editing of the plurality of elements. The reference information in (2) or (3) may include element order information estimated using the trained model in the first and second embodiments and their modifications described above.

[0281] Furthermore, in the third and fourth embodiments and their variations described above, vector data was registered in the knowledge base 54, but the invention is not limited to this, and data in any format can be registered.

[0282] Furthermore, while the first to fourth embodiments and their modifications described above employ a configuration that includes one or more of the following: estimation processing using a trained model, acquisition processing from the AI ​​model 50, and search processing using a table, the system is not limited to these, and a configuration that includes multiple of these processes can be adopted. Specifically, for example, the following configuration can be adopted.

[0283] The first configuration is one in which the processing in step S302 or step S312 is performed by one of the following: estimation processing, acquisition processing, or search processing. For the second and subsequent processing in step S312, it can be performed by the same or different processing as the processing in step S302 or the first processing in step S312.

[0284] The second configuration is one in which the processing in step S356 or step S366 is performed by one of the following: estimation processing, acquisition processing, or search processing. For the second and subsequent processing in step S366, the processing can be the same as or different from the processing in step S356 or the first processing in step S366.

[0285] The third configuration is one in which the processes in steps S502 and S504 or steps S514 and S516 are performed by one of the following: estimation, acquisition, or search. The second and subsequent processes in steps S514 and S516 can be performed by the same or different processes as the first processes in steps S502 and S504 or steps S514 and S516.

[0286] The fourth configuration is one in which the processing in steps S554 and S556 or the processing in steps S566 and S568 is performed by one of the following: estimation processing, acquisition processing, or search processing. For the second and subsequent processing in steps S566 and S568, the processing can be the same as or different from the processing in steps S554 and S556 or the first processing in steps S566 and S568.

[0287] The fifth configuration is a configuration that controls which process is prioritized. For example, one can adopt a configuration that (1) prioritizes the process with the lowest current load among multiple processes, (2) prioritizes the process whose result has been adopted by the designer to a high degree (referring to the number of times, percentage, or other degree of adoption), or (3) prioritizes the process which is used by the designer to a high degree (referring to the number of times, percentage, or other degree of use).

[0288] Furthermore, in the first to fourth embodiments and their variations described above, multiple editing elements and their editing order were learned or inferred. However, the "multiple editing elements" to be learned or inferred can be editing elements A whose setting or modification requires human judgment, and whose setting or modification affects other editing elements B. This makes it possible to grasp the element order considering the relationship between editing elements A and B. For editing elements that do not require human judgment, editing can be automated using, for example, the technology described in Japanese Patent Publication No. 7341580. Therefore, by targeting editing elements that are difficult to automate, the editing work can be made more efficient.

[0289] Furthermore, while the first and second embodiments and their modifications described above are implemented as a single device, the system is not limited to this and can also be implemented as a network system. As an example of a network system, some or all of the functions of the drawing creation support device 100 can be configured as a virtual server on a server that provides cloud computing services.

[0290] Furthermore, in the third and fourth embodiments and their modifications described above, the generating AI server 120 integrates the functions of the AI ​​model 50, AI model control unit 52, knowledge base 54, request receiving unit 56, request processing unit 58, response information transmission unit 60, request receiving unit 62, and learning data registration unit 64. However, it is not limited to this configuration, and some functions can be configured on separate servers or the like.

[0291] Furthermore, while the third and fourth embodiments and their modifications described above were implemented as a network system, the system is not limited to this and can also be implemented as a single device or application.

[0292] Furthermore, while the third and fourth embodiments and their modifications described above describe the case where the invention is applied to a network system consisting of the Internet 199, the invention is not limited to this, and may also be applied to a so-called intranet that communicates using the same method as the Internet 199. Of course, it is not limited to networks that communicate using the same method as the Internet 199, but can be applied to any network using any communication method.

[0293] Furthermore, in the first to fourth embodiments and their modifications described above, the drawing creation support device 100 is configured to utilize the storage device 42, but it is not limited to this, and can also be configured to utilize an external storage device such as a database server.

[0294] Furthermore, in the first to fourth embodiments and their modifications described above, the process shown in the flowcharts of Figures 4, 6, 8, 12, 15, 16, and 17 was described in the case of executing a program pre-stored in ROM 32. However, the invention is not limited to this, and the program showing these procedures may be read into RAM 34 from a storage medium in which the program is stored and then executed.

[0295] Here, "storage medium" refers to any storage medium that can be read by a computer, regardless of whether it is an electronic, magnetic, or optical reading medium, including semiconductor storage mediums such as RAM and ROM, magnetic storage mediums such as FD and HD, optical reading mediums such as CD, CDV, LD, and DVD, and magnetic / optical reading mediums such as MO.

[0296] Furthermore, the first to fourth embodiments and their variations described above are mutually applicable. Moreover, the present invention is applicable to other cases without departing from the spirit of the invention, not limited to the first to fourth embodiments and their variations. For example, the present invention can be applied to a wide range of design work, such as automobile design, mechanical design, and circuit design.

[0297] 100... Drawing creation support device, 30... CPU, 32... ROM, 34... RAM, 38... I / F, 39... Bus, 40... Input device, 42... Storage device, 44... Display device, 120... Generation AI server, 50... AI model, 52... AI model control unit, 54... Knowledge base, 56, 62... Request receiving unit, 58... Request processing unit, 60... Response information transmission unit, 64... Learning data registration unit, 199... Internet, 400, 404... Element information, 402... Editing time, 406... Number of edited items, 410, 416... Element ID, 412, 418... Element order information, 414, 420... Evaluation value

Claims

1. A design support system comprising: an element information acquisition means for acquiring element information relating to created or edited elements in design information; an estimation means for estimating a plurality of element sequence information with different elements or editing sequences from the element information acquired by the element information acquisition means, using a trained model trained on training data including element information relating to created or edited elements, and element sequence information relating to a plurality of elements edited consecutively after said element and their editing sequence (hereinafter, "a plurality of elements and their editing sequence" is referred to as "element sequence"); and an output means for outputting one or more element sequences from the plurality of element sequences based on the plurality of element sequence information estimated by the estimation means.

2. The design support system according to claim 1, characterized in that the output means outputs the element sequence with the largest number of elements among the plurality of element sequences.

3. The design support system according to claim 1, comprising a identification means for identifying element sequences whose occurrence frequency in the design information is predetermined or greater as related groups, wherein the output means outputs element sequences from among the plurality of element sequences that have related groups identified by the identification means.

4. The design support system according to claim 3, characterized in that the output means outputs an element sequence that is the same as a related group identified by the identification means from among the plurality of element sequences.

5. The design support system according to claim 1, comprising a identification means for identifying element sequences that appear a predetermined number of times or more in the design information as related groups, wherein the output means outputs the portion of the related groups identified by the identification means and other elements from among the plurality of element sequences.

6. A design support system according to any one of claims 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; and a second estimation means for estimating a plurality of element order information with different elements or editing orders from the element information acquired by the second element information acquisition means using the trained model.

7. A design support system according to any one of claims 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; an input means for inputting a request to an AI model that includes the element information acquired by the second element information acquisition means and element order information relating to a plurality of elements to be edited consecutively after the element relating to the element information and the editing order thereof, wherein the request is for generating a plurality of such element order information with different elements or editing orders; and an acquisition means for acquiring the plurality of element order information output from the AI ​​model in response to the request.

8. A design support system according to any one of claims 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; and a search means for searching a plurality of element order information, each with different elements or editing order, that corresponds to the element information acquired by the second element information acquisition means, from a storage means that stores element order information relating to a plurality of elements to be edited consecutively after a created or edited element and their editing order, in association with the element information relating to the created or edited element.

9. A design support system according to any one of claims 1 to 5, comprising: a second element information acquisition means for acquiring element information relating to an element created or edited based on the element order output by the output means; and an output rule changing means for changing the output rules of the output means based on the element order output by the output means and the element information acquired by the second element information acquisition means.

10. The design support system according to any one of claims 1 to 5, characterized in that the trained model is trained to maximize the evaluation value based on training data which includes element information relating to created or edited elements, element sequence information relating to the sequence of elements edited consecutively after the element, and evaluation values ​​relating to the editing of the plurality of elements.

11. The design support system according to claim 10, wherein the trained model includes a first trained model trained to maximize the evaluation value based on training data including the element information, the element sequence information, and the evaluation value based on a first index indicating an index of the value of the evaluation value, and a second trained model trained to maximize the evaluation value based on training data including the element information, the element sequence information, and the evaluation value based on a second index different from the first index, wherein the system comprises index information acquisition means for acquiring index information relating to the first index or the second index, and trained model selection means for selecting either the first trained model or the second trained model based on the index information acquired by the index information acquisition means, and the estimation means for estimating the plurality of element sequence information using the trained model selected by the trained model selection means.

12. A design support system according to any one of claims 1 to 5, wherein the plurality of elements are elements whose setting or modification requires human judgment and whose setting or modification affects other elements.

13. A design support system characterized in that, according to any one of claims 1 to 5, the design information is design information for designing buildings.

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