Abstract: A computer-based method to identify test user intent during evaluation of user interaction with a digital asset is provided comprising: receiving test user interaction data; producing a vector based upon the received data; using a machine learning model to project the vector to a first non-custom label embedding vector space; using the machine learning model to project the vector to a second custom label embedding vector space; determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the first embedding vector space for a label nearest to the vector and a nearest neighbor search within the second embedding vector space for a nearest neighbor to the vector, receiving identification of a custom label; and adjusting the second embedding vector space based upon the custom label.

Abstract: A search engine server includes a communication interface through which to receive a multi-modal query from a browser of a client device, the multi-modal query including at least a first image of an item. A processing device, coupled to the communication interface, is to: execute a neural network (NN) regressor model on the first image to identify a plurality of second items that are similar to and compatible with the item depicted in the first image, wherein a set of images correspond to the plurality of second items; generate structured text that explains, within one of a phrase or a sentence, why the set of images are relevant to the item; and return, to the browser of the client device via the communication interface, a set of search results comprising the set of images and the structured text.

Abstract: A computer-based method to identify test user intent during evaluation of user interaction with a digital asset is provided comprising: receiving test user interaction data; producing a vector based upon the received data; using a machine learning model to project the vector to a first non-custom label embedding vector space; using the machine learning model to project the vector to a second custom label embedding vector space; determining a starting label associated with the vector based at least in part upon a nearest neighbor search within the first embedding vector space for a label nearest to the vector and a nearest neighbor search within the second embedding vector space for a nearest neighbor to the vector, receiving identification of a custom label; and adjusting the second embedding vector space based upon the custom label.

Abstract: A digital experience management system comprising: the displaying on an electronic display screen a plurality of sequentially ordered node indicators, each node indicator having a respective sequence position; the system storing multiple session information structures, each respective session information structure including multiple sequentially ordered clusters of user experience information, each cluster of user experience having a sequence position that matches a sequence position of a node indicator; responding to a user selection of a respective node indicator, by displaying on the electronic display screen, each respective cluster of user experience information having a sequence position that matches a sequence position of the respective selected node indicator.

Abstract: A search engine server includes a communication interface through which to receive a multi-modal query from a browser of a client device, the multi-modal query including at least a first image of an item. A processing device, coupled to the communication interface, is to: execute a neural network (NN) regressor model on the first image to identify a plurality of second items that are similar to and compatible with the item depicted in the first image, wherein a set of images correspond to the plurality of second items; generate structured text that explains, within one of a phrase or a sentence, why the set of images are relevant to the item; and return, to the browser of the client device via the communication interface, a set of search results comprising the set of images and the structured text.

Abstract: A system and method for extracting and searching for design includes identifying design examples using a query system hosted on a server having one or more processors and memory. The system and method include receiving a first query, searching a design repository based on the first query to determine a result set having one or more first design examples, returning the first design examples, determining one or more first similarities among design properties in second design examples selected from the first design examples, and using the first similarities as a basis for one or more second queries. The design repository stores domain object model (DOM) trees, DOM elements, design properties, and snapshots. In some embodiments, the system and method further include adding one or more of the first design examples to a trendset and determining one or more second similarities of design properties in third design examples in the trendset.