The present disclosure relates to a personalized fashion generation system that synthesizes user-customized images using deep learning techniques based on visually-aware user preferences. In particular, the personalized fashion generation system employs an image generative adversarial neural network and a personalized preference network to synthesize new fashion items that are individually customized for a user. Additionally, the personalized fashion generation system can modify existing fashion items to tailor the fashion items to a user's tastes and preferences.

An automated system/method for identifying and enabling viewer selection/purchase of products or services associated with digital content presented on a display device. Products within the digital content are identified and existing product placement data is ascertained. For products that do not include such data, other methodologies, with the assistance of third-party servers, are employed to assess identity and purchase availability. Viewer input designate products to assess or products can be automatically assessed. Viewers initiate purchase of identified products via the display device or other electronic devices controlled by viewers, such as via viewers' smart phones. Various processes for identifying products include use of AI processing, access to data on third-party servers, crowd sourcing and other methodologies. Various techniques for selecting products for purchases are employed including employing 3D codes (e.g., QR codes) alongside presented products to enable other portable electronic devices to facilitate purchase. Other features are described.

The disclosed systems, components, methods, and processing steps are directed to determining user-item fit characteristics of an item for a user body part by accessing a three-dimensional (3D) reconstructed model of the user body part, accessing information about one or more 3D reference models of the item, the information for each 3D reference model including respective dimensional measurement, spatial, and geometrical attributes, performing a 3D matching process based on the 3D reconstructed model and the accessed information of the one or more 3D reference models to determine a best-fitting 3D reference model from the one or more 3D reference models, integrating the best-fitting 3D reference model with the 3D reconstructed model to provide a 3D best fit representation and displaying the 3D best fit representation along with visual indications of user-item fit characteristics.

Techniques and methods are disclosed for providing improved and consistent navigation within a mobile shopping application. A default graphical user interface may be provided at a display of a user device. In response to a navigation request at the default graphical user interface, the application can enter a store mode for a separate specialty store within the application. While in the store mode, the application can present a subsequent graphical user interface that overlays the default graphical user interface. The subsequent graphical user interface can be configured using data conforming to a graphical interface specification. The graphical interface specification may also include rules for determining which navigation requests properly enter and leave the store mode.

A recommendation engine utilizes deep learning methods, including machine learned neural network models, to identify user group clusters of users having a common purchase history when determining item options, such as an item feature, for a user associated with a search query at a search engine. The determined item options are presented to the user at a search results page or as an item listing as a preselection of selectable options for item options of an item option category, thereby identifying and providing a specific item variation. The search results page can be condensed by excluding items having a same set of item option categories or a same identified item option, thereby providing a search results page or item listing that allows other contextually relevant items to be provided and identified by the user.

A system may monitor an appendage gesture with a smart device, extrapolate a gesture direction, select an item on a display based on the gesture direction, and display the item on a second display on the smart device.

According to one embodiment, a method, computer system, and computer program product for identifying commercialization opportunities for digital twin resources captured on a sensor is provided. The present invention may include receiving digital content pertaining to a physical asset captured by the sensor; responsive to determining that no digital twin resources within a digital twin content store associated with the physical asset exceed a threshold level of similarity to the digital content, uploading the digital content to the digital twin content store based on a user response to one or more prompts.

Prescreened electronic programs or products that are automatically determined for a specific potential entity based on characteristics and/or geographic location, then can be automatically ranked based on calculated expected values of respective programs or products. The ranked programs or products are digitally/electronically presented in an interactive graphical user interface to the specific entity for digital selection and application.

A method and system for automatically ordering and fulfilling architecture, design or construction physical product and/or product sample requests with bar codes is presented. Physical product request bar codes are added to plural 3D modeling programs, including Building Information Modeling (BIM) programs, and digital and paper copies of product swatches, product cards, product sheets, product pages, product catalogs, and/or or standards books, and/or other product information sources and/or directly to physical products and/or product samples with a laser. When the bar codes are activated, architecture, design or construction physical products and/or product samples are automatically added to a shopping cart for electronic purchase and/or requested without charge.

The disclosed context-based personalization system personalizes enterprise applications. The disclosed application collects and stores user events and user affinity signals during user sessions. By analyzing the captured user events and user affinity signals, the disclosed system can predict the user's preferences and customize settings, selections and options associated with the enterprise application based on the user's preferences.