Abstract: A demonstration serving system and associated methods are provided for creating an interactive demonstration presentation for an envisioned software product. The interactive demonstration presentation for the envisioned software product is a digital artifact that is presented during a presentation to present and demonstrate features of the envisioned software product and how the envisioned software product is to function and operate in response to user interaction with a graphical user interface (GUI) such that the interactive demonstration presentation mimics features, look, and feel of the envisioned software product once it is fully developed without creating custom-made Hypertext Markup Language (HTML), JavaScript and Cascading Style Sheets (CSS) of a real software product.

Abstract: Machine learning systems and associated methods are provided. A processor comprising at least one neural network can process a captured input image to translate the captured input image into an interactive demonstration presentation for an envisioned software product. The processing can include: automatically recognizing features within the captured input image; extracting the recognized features from the captured input image at the machine learning processor; processing each of the extracted features to determine a corresponding element in a library trained via a machine learning algorithm; and automatically replacing the extracted features from the captured input image with the one or more corresponding files or components to transform the captured input image into the interactive demonstration presentation.

Abstract: A system is provided that can execute one or more scripts to cause a discrepancy resolution processor to be executed on a first exported image file (having a raster-based image format such as PNG), and a second exported image file (having a vector-based image format such as SVG) to programmatically compare, fix, and flag discrepancies or differences between the first exported image file and the second exported image file.

Abstract: A dynamic Scalable Vector Graphics (SVG) system and associated methods are provided for rendering an interactive software-product demonstration presentation for an envisioned software product within a webpage. A script can be executed to digest an exported SVG file by: querying for target objects having particular attributes named in a target format, and/or particular tag names. For each of the target objects, methods are called to modify that target object based on a first found tag name, a first found attribute, and/or contents of the first found attribute. The called methods can be used to manipulate attributes and/or contents of that target object to modify it and generate a manipulated object that includes the outputs of each called method (e.g., appropriate contents to be inserted into the SVG file). The manipulated objects with the appropriate contents can then be inserted into the SVG file to generate a final SVG file.

Abstract: A dynamic Scalable Vector Graphics (SVG) system and associated methods are provided for rendering an interactive software-product demonstration presentation for an envisioned software product within a webpage. A script can be executed to digest an exported SVG file by: querying for target objects having particular attributes named in a target format, and/or particular tag names. For each of the target objects, methods are called to modify that target object based on a first found tag name, a first found attribute, and/or contents of the first found attribute. The called methods can be used to manipulate attributes and/or contents of that target object to modify it and generate a manipulated object that includes the outputs of each called method (e.g., appropriate contents to be inserted into the SVG file). The manipulated objects with the appropriate contents can then be inserted into the SVG file to generate a final SVG file.

Abstract: Machine learning systems and associated methods are provided. A processor comprising at least one neural network can process a captured input image to translate the captured input image into an interactive demonstration presentation for an envisioned software product. The processing can include: automatically recognizing features within the captured input image; extracting the recognized features from the captured input image at the machine learning processor; processing each of the extracted features to determine a corresponding element in a library trained via a machine learning algorithm; and automatically replacing the extracted features from the captured input image with the one or more corresponding files or components to transform the captured input image into the interactive demonstration presentation.

Abstract: Systems and methods are provided for creating and editing interactive demonstration presentations for an envisioned software product. Images can be placed on a webpage of the interactive demonstration presentation to cover portions of that webpage so that new functionality is displayable on that webpage. For example, a demonstration controller can inject and define one or more rapid components into the webpage. Each rapid component corresponds to a unique area of concern to be modified within the webpage and stores data required to edit and display that rapid component on the webpage.

Abstract: A system is provided that can execute one or more scripts to cause a discrepancy resolution processor to be executed on a first exported image file (having a raster-based image format such as PNG), and a second exported image file (having a vector-based image format such as SVG) to programmatically compare, fix, and flag discrepancies or differences between the first exported image file and the second exported image file.

Abstract: A demonstration serving system and associated methods are provided for creating an interactive demonstration presentation for an envisioned software product. The interactive demonstration presentation for the envisioned software product is a digital artifact that is presented during a presentation to present and demonstrate features of the envisioned software product and how the envisioned software product is to function and operate in response to user interaction with a graphical user interface (GUI) such that the interactive demonstration presentation mimics features, look, and feel of the envisioned software product once it is fully developed without creating custom-made Hypertext Markup Language (HTML), JavaScript and Cascading Style Sheets (CSS) of a real software product.