Wix.com Ltd.

A system for reconstructing a source website of a source modality into a new website of a target modality includes a Customer Management System (CuMS) and a deconstructor and constructor module. The Customer Management System stores a copy of the source website, which has a plurality of user interface (UI) entities. The deconstructor and constructor module, including an AI/ML Engine, iteratively deconstructs the website copy by identifying, recording a description for, and removing the last-added UI entity until the website is fully deconstructed, thereby creating an ordered list of UI entity descriptions. The module then processes this ordered list in a forward-chronological sequence. For the UI entity description, it generates and adds a corresponding new UI entity to the new website in the target modality.

A system for adaptive advertisement integration with webpages includes a chameleon ad handler, an artificial intelligence (AI) engine, and a site generation system. The chameleon ad handler analyzes visual properties of a target webpage to create a webpage style profile and reads the schema of a source chameleon advertisement object (CHA). The AI engine generates adaptation commands for the source CHA to create a display CHA visually aligned with the target webpage. Following a user interaction with the display CHA, the chameleon ad handler analyzes properties of the display CHA to create a display CHA style profile and instructs the AI engine. The AI engine then generates a blueprint for a personalized landing page according to the display CHA style profile. The site generation system constructs the personalized landing page according to the blueprint, creating a visually consistent user journey from advertisement to landing page.

A system to define features of online presences and match them to an entrepreneur's profile includes a high-performance metric analyzer, an online app summarizer, and a feature definer. The metric analyzer identifies high-performance online applications (HPOAs) by analyzing hosting platform data based on performance indicators. The app summarizer generates content summaries for these HPOAs, from which the feature definer defines specific business features. A matcher to match a profile of an entrepreneur to features of an HPOA includes an HPOA processor, an HPOA-based profiler, and an HPOA-based enterprise matcher. The processor uses this information to generate a feature database of the HPOAs. The profiler receives information about an entrepreneur's skills, hobbies, and interests to create a personal profile. Finally, the enterprise matcher matches the entrepreneur's profile with the set of features in the HPOA feature database to find alignments between the user and the high-performance application models.

A website building system (WBS) includes a processor implementing a machine learning feedback-based proposal module and a database storing at least the websites of a plurality of users of the WBS, and components of the websites. The module includes a plurality of per activity AI units and a feedback system. Each per activity AI unit supports one or more specific activity related to the WBS and provides at least one system suggestion to the users related to its specific activity. Each per activity AI unit includes at least one machine learning model suitable for the activity supported by its per activity AI unit. The feedback system provides a plurality of different kinds of feedback from the users and from rule engines for updating the machine learning models. The feedback system analyzes the feedback to determine which one of the at least one machine learning models to update.

A website building system (WBS) includes a processor implementing a machine learning feedback-based proposal module and a database storing at least the web sites of a plurality of users of the WBS, and components of the websites. The module includes a plurality of per activity AI units and a feedback system. Each per activity AI unit supports one or more specific activity related to the WBS and provides at least one system suggestion to the users related to its specific activity. Each per activity AI unit includes at least one machine learning model suitable for the activity supported by its per activity AI unit. The feedback system provides a plurality of different kinds of feedback from the users and from rule engines for updating the machine learning models. The feedback system analyzes the feedback to determine which one of the at least one machine learning models to update.

Embodiments provide for responsive optimization of websites. In some examples, a layout graph data structure is generated based on applying a trained layout model to extracted website elements from an input object including one or more website building tools. A webpage layout data structure is generated based on applying a trained structure optimization model to the layout graph data structure. An optimized webpage that is configured to render according to a plurality of screen parameters is generated based on applying a trained responsive optimization model to the webpage layout data structure and a plurality of screen parameters.

A method and system for a website building system (WBS) includes gathering, accumulating and analyzing information for updating an existing website for a designer of the WBS from answers to questionnaires and systems external and internal to the WBS, using at least artificial intelligence and machine learning techniques to make recommendations for the existing website according to the information; and optimizing and regenerating the existing website into an updated website according to the recommendations.

A website building system (WBS) includes a processor implementing a machine learning feedback-based proposal module and a database storing at least the web sites of a plurality of users of the WBS, and components of the websites. The module includes a plurality of per activity AI units and a feedback system. Each per activity AI unit supports one or more specific activity related to the WBS and provides at least one system suggestion to the users related to its specific activity. Each per activity AI unit includes at least one machine learning model suitable for the activity supported by its per activity AI unit. The feedback system provides a plurality of different kinds of feedback from the users and from rule engines for updating the machine learning models. The feedback system analyzes the feedback to determine which one of the at least one machine learning models to update.

A website building system (WBS) includes a processor implementing a machine learning feedback-based proposal module and a database storing at least the websites of a plurality of users of the WBS, and components of the websites. The module includes a plurality of per activity AI units and a feedback system. Each per activity AI unit supports one or more specific activity related to the WBS and provides at least one system suggestion to the users related to its specific activity. Each per activity AI unit includes at least one machine learning model suitable for the activity supported by its per activity AI unit. The feedback system provides a plurality of different kinds of feedback from the users for updating the machine learning models. The feedback system analyzes the feedback to determine which one of the at least one machine learning models to update.

Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.