Methods and systems for detecting the content of model files

Abstract

This invention provides a computerized method and system for detecting modeling content within a model file. This method and system do not require rendering the model file. The method and system include loading the model file into a detection engine, and the model file includes software code. In this method and system, the detection engine scans the model file and detects descriptor terms within the software code. The method and system include: generating a description list of the model file based on multiple descriptor terms; and executing a transformation engine to examine the description list relative to a relational database, the transformation engine electronically generating file terms describing the modeling content within the model file based on input from the relational database. Thus, the method and system generate a content list of the model file based on the file terms, thereby associating the content list with the model file.

Description

[0001] Copyright Notice

[0002] This patent document contains copyrighted material. The copyright holder does not object to any fax copying of the patent document or patent disclosure when it appears in the patent archives or records of the Patent and Trademark Office, but retains all copyright in any way. Technical Field

[0003] This invention generally relates to multidimensional electronic model content, and more specifically, to the detection of model content within model files. Background Technology

[0004] In video games and other graphics generation systems, pre-defined multi-dimensional models are commonly used. These models are typically 3D models, pre-generated content uploaded and included in the graphics rendering engine. For example, a model could be a vehicle, allowing game developers to upload models and insert them into their games without having to create new models from scratch.

[0005] Existing marketplaces offer models available through purchase or free use. These models are typically created by third parties and then made available to the public.

[0006] Current technology has problems at several levels. For example, there are many file formats. If 3D printing and CAD software are involved, there may be hundreds of different file formats that require different rendering engines to view the model content.

[0007] Another issue is determining the model content encoded within the model file. Model content can represent third-party intellectual property and typically does not require third-party approval. For example, model content might represent an actual project protected by trademarks and / or trade dress. It is important for users to understand the model content before acquiring and using it.

[0008] Currently, model content creators provide written descriptions of the file content. For example, someone loading model files onto a commercial website might provide a list of descriptive terms such as car, sedan, race car, red, 20-inch wheels, spoiler, etc. This technology relies on the accuracy and completeness of human-inputted terms. This can lead to misleading, incomplete, or inconsistent nomenclature.

[0009] Another example of content is inspectors examining file metadata, and even manually opening model files to visually inspect the model content and provide written descriptions. Full rendering of all models can be a time-consuming process. Given the large number of file formats and model files, this is a cumbersome task, impractical in typical model file platforms. Furthermore, the reliance on manual input again depends on the completeness and accuracy of user-generated input.

[0010] Confusing, misleading, or even incomplete descriptions can complicate access to and use of model files. Users may struggle to find the best model files through the search interface. For example, many users search based on filenames, so multiple terms for the same project can complicate the search, such as whether to search for "tatami" or "sofa." Furthermore, users may purchase or import model content without realizing the potential abuse of third-party intellectual property.

[0011] The third issue arises with the proprietary content included within the model. Using the vehicle example above, the model could represent a well-known vehicle (e.g., (e.g., a convertible). However, the vehicle's manufacturer (such as Tata Motors) may not authorize the use of the model in subsequent graphics rendering environments. Furthermore, the model's use in rendering may exceed acceptable purposes. For example, if the rendering is of a video game where players use the car to evade law enforcement and engage in illegal activities, the manufacturer may object to the use of the vehicle's likeness.

[0012] Due to a lack of understanding of the model files' contents, tracking the ownership, distribution, and use of the proprietary content that has been modeled also presents problems. Therefore, it is important not only to understand what is in the model, but also to track model information for subsequent use.

[0013] Therefore, an improved method and system are needed to detect the modeling content within model files. Summary of the Invention

[0014] This invention provides a method and system for detecting modeling content within a model file. The model file represents multidimensional model data, such as a 3D model available in a graphics processing or rendering environment. The model file can have any suitable file extension and can be executed by modeling software to create, use, or manipulate model data. Common examples include computer-aided drawing software and video games or other graphics rendering engines.

[0015] The methods and systems utilize model files to detect modeled content without requiring executable modeling software to render the model files. The methods and systems involve loading the model files into a detection engine. The detection engine can be a software application that reads software code within the model files. However, while a rendering engine reads software code to render the model, the detection engine scans the software code in the model files and detects various descriptive terms within that code. For example, the detection engine might scrape the model files to extract each English word or phrase, which could include creator comments and annotation fields, as well as other items.

[0016] The system scans and generates a list of descriptions for the model files, based on detected descriptor terms. Descriptor terms can originate from user-generated content embedded within the model files, such as coding comments written by programmers into the software code. They can also come from automatically generated terms written into the code of model generation or model rendering software. Descriptor terms can be any other suitable terms, including, for example, filenames, model set information, etc.

[0017] The methods and systems may also use further techniques to find terms. For example, one technique may include rendering the contents of a model file and requesting user data input. In this example, the rendered content may be submitted to a third-party website to receive keyword descriptors. These further techniques can be used to supplement the descriptive list.

[0018] In this paper, the method and system refine the description list by executing a transformation engine. The transformation engine references the description list using a relational database that includes additional terms, such as supplementary terms, synonyms, or other related terms. The relational database can also insert hierarchical or other structures into the terms. Therefore, the relational database expands the description list by generating a transformation engine that describes the terminology of the documented modeling content.

[0019] In this paper, the method and system generate a content list of model files based on file terms. The content list can be the sum of all file terms, including the original descriptor terms. The content list can be used as searchable content. In another example, the content list can include a hierarchical structure of terms, ranging from general identifiers to sub-parts or specific levels. An example of a hierarchy could be: anatomy as the highest-level term, human as a sub-level term, and organs as second-level sub-level terms.

[0020] In this paper, the methods and systems involve associating a list of contents with a model file. This association can be performed in any number of appropriate ways, including modifying the model file itself or using a file identifier associated with the model file to maintain a reference database of the list of contents.

[0021] Methods and systems can operate using a model file repository. For example, methods and systems can operate simultaneously with uploading model files to the repository. In another example, methods and systems can scan an existing repository to find already loaded content.

[0022] Through the model file repository, a list of various model files provides additional accessibility via search functionality. For example, users with access to the repository can perform a more comprehensive search for the desired model files. The search function can access the database of the content list to reference related search model files.

[0023] The methods and systems can also operate using feedback from the content generator. For example, a model file creator can receive a list of content associated with the model file. The creator can generate additional terms or modify existing terms. These additional terms can then be processed by the methods and systems.

[0024] The methods and systems can also use machine learning to further examine and refine the content list relative to the relational database. For example, machine learning can include learning operations to determine additional relationships between terms. Machine learning can also include updating and improving the accuracy and completeness of the relational database and the content list.

[0025] The methods and systems used to detect modeling content also utilize this content for search and data tracking operations. Where the modeling content may include proprietary material, an associated list of content can be used to track the storage, sale, distribution, and use of model files and modeling content. The methods and systems can provide type detection by identifying intellectual property associated with or contained within model files. Based on associating the content list with model files, records can be maintained to determine who obtains and uses different model files, including ensuring that the model file repository does not improperly distribute content. Alternatively, if content is distributed, the recipient is informed of the appropriate rights and obligations associated with the model file.

[0026] Therefore, this method and system improve the monitoring of access to, tracking, and distribution of model files. Attached Figure Description

[0027] Figure 1 The computational system provided for detecting the modeling content of model files is shown;

[0028] Figure 2 It shows Figure 1 An expanded diagram of one embodiment of the model content detector;

[0029] Figure 3 A data block flowchart illustrating one embodiment of model file content detection is shown;

[0030] Figure 4 It shows Figure 1 An expanded diagram of one embodiment of the model database;

[0031] Figure 5 An embodiment of a system for detecting the content of extended model files is shown;

[0032] Figure 6 A flowchart illustrating the steps of an embodiment of a method for detecting modeling content in a model file is shown.

[0033] Figure 7A flowchart illustrating the steps of another embodiment of model file content detection is shown;

[0034] Figure 8 shows a representation of an exemplary model file;

[0035] Figure 9 The model file shown in Figure 8 is shown. Figure 3 An exemplary embodiment of the data block flowchart; and

[0036] Figures 10 to 12 The image shows a sample screenshot of a model file that is accessible within a hierarchical structure based on content detection.

[0037] A better understanding of the disclosed technology will be obtained from the following detailed description of preferred embodiments in conjunction with the accompanying drawings and claims. Detailed Implementation

[0038] As described in this paper, the methods and systems detect the modeling content within the model file.

[0039] Figure 1 A general computing environment 100 is illustrated, in which client device 102 accesses server 104 via network 106. Server 104 communicates with model database 108 and model content detector 110.

[0040] Client device 102 can be any suitable type of device used to connect to network 106 and access server 104. For example, client device 102 can be a computer used by a model developer to upload content. Client device 102 can be a client computer or computing device seeking to search for, purchase, or otherwise access content through server 104.

[0041] Server 104 can be one or more computing devices that operate to perform server-based content management operations. For example, server 104 may represent a network-side computing device that runs an access portal for model files or a cloud-based distributed computing environment. In one embodiment, server 104 may be a commercial online repository accessed via network 106 for uploading and downloading model files. Server 104 can also be a proprietary repository for content, such as a video game developer or graphics studio that maintains its own content database.

[0042] Network 106 is any suitable network, such as the publicly accessible Internet or a private network. Known communication protocols can be used to perform computer-to-computer access through network 106.

[0043] Model database 108 can be a single or distributed data repository for model files and related model content. Database 108 can be local to server 104 or across a distributed environment. Interaction between server 104 and database 108 can be performed using known data storage and retrieval technologies. Interaction may include storing model files, retrieving model files, searching model files, etc.

[0044] System 100 further includes a model content detector 110, which performs processing operations to detect modeled content. The detector 110 can be located within server 104 or in a separate processing environment, such as... Figure 1 As shown. For example, in one embodiment, the model content detector 110 may be an executable routine within server 104. In another embodiment, detector 110 may be independently executable and accessible via a network.

[0045] Figure 1 The system 100 runs to inspect the modeling content within the model file without needing to render the model file. The model file represents two-dimensional or three-dimensional digital content and is typically used in graphics rendering environments, but can also be used in further modeling environments, such as 3D printers.

[0046] Figure 2 A further exemplary display of an embodiment of the model content detector 110 is provided. Figure 2 The detector 110 includes a detection engine 120 and a conversion engine 122. The detection engine 120 can access a file type database 124, and the conversion engine 122 can access a relational database 126.

[0047] The detection engine 120 can be an executable application running on one or more processing devices. In response to executable instructions, the engine 120 scans incoming model files without requiring actual rendering of the files. Scanning the files includes examining the software code for the files, as well as any other associated data or content accessible to the detection engine 120. For example, a scan may include examining the filename. In contrast, rendering the model files may require an appropriate rendering application to construct the model.

[0048] The transformation engine 122 may be one or more processing devices that examine and transform the output of the detection engine, including reference to additional datasets and / or databases.

[0049] Compared to Figure 1 In one embodiment of system 100, a model file is loaded into detection engine 120. Client device 102 can upload the model file to server 104 via network 106. When the file is uploaded, server 104 can load the model file into engine 120 of detector 110.

[0050] The detection engine 120 responds to executable instructions, scans the software code of the model file, checks the actual letters and numbers in the model file, and does not formally render the model.

[0051] The detection engine 120 examines the software code to detect multiple descriptor terms within it. For example, the software code may include developer comments and annotations. The detection engine 120, scanning the software code, can perform content recognition operations to extract terms identified as descriptor terms. In this example, the model developer may include comments instructing the model file to render vehicles, including descriptions of the vehicle brand and model. These vehicle brand and model terms are descriptor terms. The engine 120 then combines these terms into a list of descriptors.

[0052] Since the detection engine 120 may not render model files, it does not require or use model file-specific rendering software. It should be understood that file type extensions typically specify rendering software for rendering model files, but the detection engine 120 may be unaware of the file type.

[0053] In one embodiment, file types can be beneficial for examining software code. Therefore, the file type database 124 may include additional information related to different model software code that can be used to improve the detection engine. Thus, while the detection engine 120 may not render the model itself, file type encoding features can be identified and used to further assist in the analysis of the software code. For example, file types may have specific formats for annotations or system-generated tags, which are easier to detect using file type-specific information.

[0054] In detector 110, conversion engine 122 refines the description list. In this embodiment, conversion engine 122 uses the description list to access relational database 126. Relational database 126 expands and refines the scope of terms within the model content of the tagged model file through its relational content. The relational database provides a deeper classification of terms.

[0055] In this embodiment, when accessing the relational database 126, the transformation engine 122 generates a list of contents for the model files. As described in further detail below, this list of contents is an expanded or refined list. The transformation engine 122 can then distribute the list of contents associated with the model files. Figure 1 In the example, the list of contents can be distributed to model database 108 and appropriately associated with the original model file.

[0056] Figure 3 An example of the computational process used to detect modeled content is shown. Figure 3Model file 130 is shown, containing software code. As described above, the rendering application executes the software code to generate the model. However, in this method and system, the software code is not rendered; instead, descriptive terms embedded in the software code are read.

[0057] Extract descriptive terms 132 from the software code. In this example, term 132 is a data structure or data field with multiple terms, such as a structured or unstructured list. Essentially, these terms are limited, depending on the software code to be included in the model file 130.

[0058] However, term 132 is then processed by conversion engine 122. Conversion engine 122 can then expand and refine the list using any suitable reference material, such as an embodiment of a relational database. In one example, the conversion engine may determine synonyms or alternative terms describing the model file. For example, software code may include the term "sedan," while the conversion engine includes synonyms such as "vehicle," "car," and "car." For example, software code may include "sedan" and "four-door," while the conversion engine includes the related term "car."

[0059] A refined content list 134 is generated from the descriptive terms 132 from the transformation engine 122. Similar to the descriptive terms 132, this list 134 can be a data structure or data field with multiple terms. The content list may also include reference identifiers that link the content list 108 to the model file 130.

[0060] Figure 4 It shows Figure 1 One embodiment of the model database 108. In this embodiment, database 108 includes two storage units: one for storing model files 140 and the other for storing a list of contents 142. It should be understood that these may be stored in shared storage or across different platforms. Various embodiments may include local storage and / or cloud-based storage for the model files and the list of contents, including local storage embodiments or distributed storage embodiments.

[0061] Content list 142 is associated with model file 140, allowing the management of modeling content using the model file. Content management may include accessibility to modeling content used for commercial transactions or other forms of distribution. For example, content list 142 may be searchable content, allowing queries to request specific modeling content via model file 130. In the example of a commercial repository, content list 142 allows for broader and enhanced search results in response to search requests.

[0062] Content management can also include accountability and tracking of modeled content. This accountability can be applied to commercial transactions or distribution formats. Model files may contain proprietary content, such as trademarks, trade dress, or other usage rights. Commercial databases can quickly identify model files with proprietary content.130

[0063] Brand management includes considering distribution and usage restrictions required by proprietary content owners. As an example, a model could include a car brand and model number; the car manufacturer might permit general use but prohibit embedding the model in video games or videos for illegal or unethical activities.

[0064] These usage and distribution restrictions are available for reference in the model file and are tracked based on the content list. Thus, any acquisition or distribution of the model file 130 can easily incorporate known usage and distribution restrictions by referring to the associated content list.

[0065] Another example of proprietary content could be a proprietary content owner seeking to verify that their proprietary content has not been sold or distributed in the model file. Having a list of content associated with the model file allows for this. Figure 1 Server 104 can quickly and efficiently examine its model file database 140. In this paper, the method and system allow server 104 ( Figure 1 Check the content under its management.

[0066] Figure 5 Further alternative embodiments are shown, using machine learning engine 150 to further expand or refine the content list 122. Similar to... Figure 2 This embodiment uses a transformation engine 122 that operates in conjunction with a relational database 126. The machine learning engine 150 includes further processing operations that extend onto the content list.

[0067] Machine learning operations can include any suitable or well-known machine learning techniques to expand or modify the content list. For example, machine learning can be based on a dataset used to determine second-order relationships between multiple terms or descriptors.

[0068] Figures 1 to 5 Describes one or more processing environments. Figure 6 A flowchart of a method for detecting modeled content is shown. This method can use the methods described above. Figures 1 to 5 It is executed in a processing environment.

[0069] exist Figure 6 In step 160, the model file is loaded into the detection engine. This loading step can be performed over the network or from a local repository. In one embodiment, Figure 7 Further embodiments of the loading operation are described.

[0070] Step 162 scans the model file to detect descriptor terms within the software code of the model file. While the rendering engine reads the software code to render the model, the scanning step checks the software code for detection purposes, rather than performing graphics rendering.

[0071] Step 164 generates a description list for the model file based on descriptor terms. The description list is formed based on the terms detected and extracted from the scan in step 162. The description list can also be generated from additional sources, as described below. Figure 7 A more detailed description. The description list can be a text file with multiple terms.

[0072] Step 166 references a relational database and uses a transformation engine to examine the description list. The description list may be limited in scope based on the detection steps, therefore the relational database uses relational knowledge consistent with known relational database techniques to reference additional terms.

[0073] Step 168 generates document terminology based on a relational database, describing the modeling content within the model file. The database uses a relational schema to expand the scope, breadth, and classification of the terminology. For example, the following... Figure 9 An exemplary embodiment of the extended descriptive terminology is shown.

[0074] Then, the description list is converted into a content list. Step 170 generates a content list for the model file based on the document terminology. The content list can also be a text file or a structured list.

[0075] In step 172, the content list is further associated with and referenced by the underlying model file. The model file and the associated content list can then be stored and accessed by any suitable search or distribution platform. Among other benefits, the association between the content list and the model facilitates search operations, business transactions, and brand management, including tracking the model file and its proprietary content.

[0076] Figure 7 A flowchart illustrating a further embodiment of model file content detection is provided. Model file interaction may include uploading to a repository or distribution server. For example, a website may manage a model file repository for purchase or download. Model file creators may upload files and offer them for purchase. Therefore, this method and system improve the understanding of model file content without requiring performance and visualization checks on the model itself.

[0077] Step 180 receives the model file uploaded by the user. In this example, the user may access a website or web location. The user may create an account or log in to an existing account. The user can then use any suitable known upload technology to upload the model file. Uploading may include formally writing the model file to a local storage location, or it may include linking to an external database.

[0078] Step 182 uses a conversion engine to generate a content list. This step can be combined with... Figure 6 Steps 162 to 170 are the same. Among them, Figure 6 The flowchart is an automation process. Figure 7 Provide interactivity or feedback.

[0079] Step 184 presents the content list to the user or a third party. Presentation includes interactive features for modifying or supplementing the content list. For example, the model creator may seek to include additional descriptive terms in the content list. A third party may represent the owner of proprietary rights encapsulated within the model file content; for example, the third party may add further information regarding constraints or usage restrictions.

[0080] Step 186 determines whether to add other terms to the content list. If it is determined to add them, step 188 updates the content list and the relational database. Afterward, or if there are no further changes, the method proceeds to step 190 to associate the content list with the model file. Thus, Figure 7 It facilitates user interaction to enhance the content list.

[0081] The above text describes the method and system from the aspects of operation and structural function. Figure 8 and... Figure 9 A representative embodiment of detecting modeling content is illustrated. Figure 8 shows a prior art model file 200, which is executed by a rendering engine 202 to generate a 3D model. In this example, the model file has model encoding, which can be read by the rendering engine 202 to create a vehicle model 204.

[0082] Figure 9 Similar to Figure 3 Here, the sample terminology relates to model file 200 in Figure 8. Model file 200 includes software code 220. As mentioned above, software code 220 can be read by a rendering engine (e.g., engine 202 in Figure 8) to render the model. This method and system do not need to render the model; instead, they examine the software code itself. Therefore, the method and system may be unaware of the model file type.

[0083] Scanning software code 220 detects descriptor terms 222. Using model file 200 of the rendered vehicle model (204 in Figure 8), detected terms may include: automobile, luxury car, sedan, rim, driver, grille, LED lights, etc. These terms can be obtained by scanning annotations, comments, or other parts.

[0084] Consistent with the techniques described above, descriptor terms are fed through a conversion engine 122. In this document, the conversion engine can expand terms into a content list 224. In this example, the term "car" expands to include Bentley shape, non-specific vehicle, and hood trim with generic sub-names. The term "luxury sedan" can be expanded to indicate a sloping hood, a lower grille, leather seats, etc.

[0085] Further refinement may include removing default or non-descriptive terms. For example, model developers may use extremely generic or overly broad terms that have little or no practical value in describing the model's content, so the generation of the document's terminology may include eliminating these terms.

[0086] The content list 224 may be generated independently by the transformation engine 122, or in other embodiments may include further feedback or modification techniques. For example, a machine learning engine may use additional feedback operations to expand the scope. In another example, creator or third-party feedback may be used to modify or expand the list.

[0087] The methods and systems can further generate and extend the classification of descriptive terms. For example, aggregation. Figure 4 The terms in content list 142 provide a global dataset that can be used to generate a list of descriptions. All terms in content list 142 are aggregated ( Figure 4 The terminology within the model file (140).

[0088] This expanded dataset allows for improved reading and matching of existing terms within new model files. For example, model developers may have a limited or unique set of terms, but aggregated terms, methods, and systems refine and expand the terminology more accurately than known and available terms.

[0089] The aggregation of terms can be refined through term distribution. For example, a model database might have 20,903 aircraft model files. The aggregated content list could be displayed as follows: for example, term distribution as wing 99%, seat 95%, wheel 92%, ... Boeing 26%, propeller 24%, model 737 1%, etc. This can be created using a conversion engine (e.g., ... Figure 3 Engine 122) is used to generate a list of contents (e.g. Figure 3 The categories of list 134).

[0090] Terminology and classification can also be used to categorize content, such as creating object trees. Including object trees provides an improved model for content classification. This, among other benefits, also improves content accessibility.

[0091] Figure 10The image shows a thumbnail of a sample frog model file. A user accessing the server or processing system to obtain model content can request one or more frog content files. Each of these files, represented by the thumbnail, is model content that, when rendered by appropriate rendering software, creates a multi-dimensional model of the appropriate frog. Figure 10 As seen in the screenshot, there are many examples of frogs, so further differences can be used to better quantify the model file, using the coded data within the model file as well as the relational terminology structure and hierarchy as described in this article.

[0092] Figure 11 The sample hierarchy is shown, i.e., the object tree of the files. This object tree uses subsets to describe the hierarchy of animals. The object tree shows the highest-level example as "animal," and the examples of the first-order subsets are amphibians, animal anatomy, birds, extinct animals, invertebrates, mammals, reptiles, marine life, and other animals not shown. In this example, the model file is described within the hierarchy; for example, in the amphibian example, it is "object-nature-animal-amphibians."

[0093] The object tree can be further segmented. Figure 12 The diagram illustrates subcategories of frogs within amphibians, such as bullfrogs, tree frogs, cartoon frogs, and frog eggs. This hierarchy creates further terminology and distinctions for the model content files. This hierarchical structure of terms can be assigned to model files as part of a content list generated by the aforementioned transformation engine based on descriptor terms detected within the model file software code. The assigned terms also allow for the categorization of image files, making the model file repository accessible to third parties.

[0094] This method and system improve the description of model files by examining the software code. The identification and processing of this information allows for a more robust description of the model files without the time-consuming manual rendering and visualization of each file.

[0095] In further embodiments, the method and system may include supplementing the content list based on user input (including visual feedback). For example, one embodiment may include rendering a model file to visually examine the model content. This can be done... Figure 1 This can be done on server 104, or via a separate processing environment. One or more users can visually inspect the model content and enter descriptive terms. For example, one or more users can manually enter one or more descriptive terms based on viewing the rendered model content, such as community or user input / feedback to add further descriptive content.

[0096] In another embodiment, visual inspections can be used to supplement or validate the generation of the description list. In the machine learning example, visual inspections and data inputs can be used to improve machine learning algorithms to enhance the accuracy and adequacy of the description list. Machine learning operations may involve processing descriptive terms, including identifying additional associations or connections between various terms. Machine learning may also include operations that visually inspect or graphically recognize rendered model content, such as using image recognition techniques to generate additional descriptive terms for broadening, expanding, or creating further associations.

[0097] Figures 1 to 12 These are conceptual diagrams used to illustrate the invention. It should be noted that the examples and figures above do not imply limiting the scope of the invention to a single embodiment, as other embodiments are possible by interchangeing some or all of the described or illustrated elements. Furthermore, where certain elements of the invention are partially or fully implemented using known components, only those portions of these known components necessary for understanding the invention are described, and detailed descriptions of other portions of these known components are omitted to avoid obscuring the invention. Moreover, unless explicitly stated otherwise, the applicant does not intend to assign any terminology in the specification or claims uncommon or special meanings. Furthermore, the invention includes current and future known equivalents of the known components mentioned herein by way of illustration.

[0098] The foregoing description of the specific embodiments fully discloses the general nature of the invention, enabling others to readily modify and / or adapt these specific embodiments to various applications by applying knowledge within the scope of the relevant art (including the content of documents cited herein and incorporated herein by reference) without requiring excessive experimentation and without departing from the general conception of the invention. Therefore, based on the teachings and guidance given herein, such adaptations and modifications are intended to fall within the meaning and scope of equivalents of the disclosed embodiments.

Claims

1. A computerized method for detecting modeling content within a model file, the method comprising: The model file is loaded into the detection engine, and the model file includes software code; The detection engine electronically scans the model file and detects multiple descriptor terms associated with the software code, wherein the scanning of the model file is not performed by executing the model file to render the modeling content; Based on the multiple descriptor terms, a description list for the model file is generated; A transformation engine is executed to examine the description list relative to a relational database. The transformation engine, based on input from the relational database, electronically generates file terms describing the modeling content within the model file by expanding the descriptor terms. Identify the degree of specific layering of the document terms in order to insert a hierarchy into the document terms; Based on the document terminology, generate a list of contents for the model file; Associate the content list with the model file; The visualization of the modeling content is transmitted to a third party; Receive keyword descriptors for the modeling content from the third party; The keyword descriptors linked to the model file are stored in a searchable database; Receive search requests for model file repositories with at least one search term; and When the search request includes the keyword descriptor according to the hierarchy, and the content list includes one or more items related to the at least one search item, the model file is retrieved in response to receiving the search request.

2. The method according to claim 1, further comprising: Receive the model file to include it in the model file repository; as well as The model file is loaded into the detection engine before it is included in the model file repository.

3. The method according to claim 1, further comprising: The model file is retrieved from the model file repository before being loaded into the detection engine; as well as Associate the list of contents with the model files within the model file repository.

4. The method according to claim 1, further comprising: Present the list of content to the content creator; Receive feedback from the creator of the content list; as well as The content list will be updated based on the creator's feedback.

5. The method of claim 4, further comprising: The relational database is updated based on the creator's feedback.

6. The method of claim 1, further comprising: Execute a machine learning software application to examine the list of contents relative to the relational database; as well as The relational database is updated based on the results of the machine learning software application.

7. The method according to claim 1, wherein, The step of associating the content list with the model file further includes: When the content list includes one or more items related to the at least one search item, the model file is retrieved in response to the search request.

8. The method according to claim 1, wherein, The step of associating the content list with the model file further includes: Generate tracking data for accessing and retrieving the model files from the model file repository.

9. The method according to claim 1, wherein, The plurality of descriptor terms are hierarchical terms, and the hierarchical terms have a hierarchical relationship defined on the hierarchical terms.

10. The method of claim 1, further comprising: The description list is supplemented with one or more visualization terms from the visualization inspection of the visualization display of the modeling content.

11. A system for detecting modeling content within a model file, the system executing and constructing the model file without using modeling software capable of executing to render the modeling content, the system comprising: Memory, which stores executable instructions; as well as The processing device operates in response to the executable instructions to: The model file is loaded into the detection engine, and the model file includes software code; The detection engine electronically scans the model file and detects multiple descriptor terms associated with the software code, wherein scanning the model file does not involve executing the model file to render the modeling content; Based on the descriptor terms, generate a description list for the model file; The transformation engine examines the description list relative to a relational database, and based on the input from the relational database, generates multiple file terms that describe the modeling content within the model file by expanding the descriptor terms; Identify the degree of specific layering of the document terms in order to insert a hierarchy into the document terms; Based on the document terminology, generate a list of contents for the model file; Associate the content list with the model file; The visualization of the modeling content is transmitted to a third party; Receive keyword descriptors for the modeling content from the third party; The keyword descriptors linked to the model file are stored in a searchable database; Receive search requests for model file repositories with at least one search term; and When the search request includes the keyword descriptor according to the hierarchy, and the content list includes one or more items related to the at least one search item, the model file is retrieved in response to receiving the search request.

12. The system of claim 11, wherein the processing device further operates in response to the executable instruction to: Receive the model file to include it in the model file repository; and The model file is loaded into the detection engine before it is included in the model file repository.

13. The system of claim 11, wherein the processing device further operates in response to the executable instruction to: Before loading the model file into the detection engine, the model file is retrieved from the model file repository; and Associate the list of contents with the model files within the model file repository.

14. The system of claim 11, wherein the processing device further operates in response to the executable instruction to: Present the list of content to the content creator; Receive feedback from the creator of the content list; and The content list will be updated based on the creator's feedback.

15. The system of claim 14, wherein the processing device further operates in response to the executable instruction to: The relational database is updated based on the creator's feedback.

16. The system of claim 11, wherein the processing device further operates in response to the executable instruction to: Execute a machine learning software application to examine the list of contents relative to the relational database; and The relational database is updated based on the results of the machine learning software application.

17. The system of claim 11, wherein the processing device, in response to the executable instructions, further operates upon associating the content list with the model file to: If the content list includes one or more items related to the at least one search term, the model file is retrieved in response to the search request.

18. The system of claim 11, wherein the processing device, in response to the executable instructions, further operates upon associating the content list with the model file to: Generate tracking data for accessing and retrieving the model files from the model file repository.

19. The system according to claim 11, wherein, The descriptor term is a hierarchical term, and the hierarchical term has a hierarchical relationship defined on the hierarchical term.

20. The system of claim 11, wherein the processing device further operates in response to the executable instruction to: Render the model file to create a visual display of the modeled content; and The description list is supplemented with one or more visualization terms from the visualization inspection of the visualization display of the modeling content.

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