A service server includes a product database configured to extract a label describing product details, map the label to a product, and store the label; a query processing unit configured to receive a query to request recommended product information related to a search target object from a user device, recognize the search target object from the received query, and obtain a query label from the recognized search target object; a feature label list providing unit configured to search the product database for one or more candidate recommended products, generate a feature label list based on feature labels selected among feature labels tagged on the one or more candidate recommended products, and provide the feature label list to the user device; and a product recommendation module configured to search the product database for a recommended product, and provide the recommended product information to the user device.

Systems and methods for distributing photo filters based on the location of the object in the image are described. A photo filter publication system detects that a client device in communication with the system has captured an image, identifies an object in the image, identifies a location of the object in the image, identifies an image overlay associated with the identified location and having object criteria satisfied by the identified object, and provides the identified image overlay to the client device.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.

Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.

Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.

In one aspect, a computerized method for predicting of a description and an icon for a field name. The method includes identifying a field name to provide the description and the icon; predicting the description of the field name with a machine learning (ML) process by: providing a set of field names and matching descriptions; in a training phase, using the set of field names and matching descriptions as a labelled dataset to train a ML model, the ML model comprises a sequence ML model; using the sequence ML model to predict a plurality of description tokens, wherein an initial input to the sequence ML model comprises the field name, wherein the field name is tokenized and passed through an embedding layer that is given as an input to the sequence model, wherein the sequence model predicts a first description token of the description, wherein the first description token is then fed back to the sequence ML model to predict a set of subsequent description tokens of the plurality of description tokens; implementing an icon prediction by: predicting an icon matching the field name by using the field name as a keyword in an image database search and then automatically selecting an icon from one or more query results.

In one aspect, a computerized method for predicting of a description and an icon for a field name. The method includes identifying a field name to provide the description and the icon; predicting the description of the field name with a machine learning (ML) process by: providing a set of field names and matching descriptions; in a training phase, using the set of field names and matching descriptions as a labelled dataset to train a ML model, the ML model comprises a sequence ML model; using the sequence ML model to predict a plurality of description tokens, wherein an initial input to the sequence ML model comprises the field name, wherein the field name is tokenized and passed through an embedding layer that is given as an input to the sequence model, wherein the sequence model predicts a first description token of the description, wherein the first description token is then fed back to the sequence ML model to predict a set of subsequent description tokens of the plurality of description tokens; implementing an icon prediction by: predicting an icon matching the field name by using the field name as a keyword in an image database search and then automatically selecting an icon from one or more query results.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.