The present disclosure relates to a method for expanding an image database for evaluation of eyewear compatibility. In particular, the present disclosure relates to a method, comprising receiving a user image, receiving a frame image, processing the received frame image by setting, as transparent, pixels of the received frame image except for an anterior face of the frame, defining, within the processed frame image, a left boundary and a right boundary of the anterior face of the frame, the left boundary and the right boundary corresponding to the left eye and the right eye, respectively, receiving a filter image, processing the received filter image by setting, as transparent, pixels in the received filter image outside the frame based on the left boundary and the right boundary, merging the processed frame image and the processed filter image, and overlaying the merged image onto the received user image.

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.

The disclosed technology addresses the need in the art for a service that can ingest a social network export and restore the integrity of threads within the social network export. The present technology can unite images in the social network export with the caption from the initial post, and with any comments within the thread. Likewise, images in the social network export can be enhanced to include metadata that reflects when the image was posted and any other contextual information that the social network provides in export file.

The disclosed technology addresses the need in the art for a service that can ingest a social network export and restore the integrity of threads within the social network export. The present technology can unite images in the social network export with the caption from the initial post, and with any comments within the thread. Likewise, images in the social network export can be enhanced to include metadata that reflects when the image was posted and any other contextual information that the social network provides in export file.

In implementations of search input generation for an image search, a computing device can capture image data of an environment scene that includes multiple objects. The computing device implements a search input module that can detect the multiple objects in the image data, and initiate a display of a selectable indication for each of the multiple objects. The search input module can then determine a subject object from the detected multiple objects, and generate the subject object as the search input for the image search.

In implementations of search input generation for an image search, a computing device can capture image data of an environment scene that includes multiple objects. The computing device implements a search input module that can detect the multiple objects in the image data, and initiate a display of a selectable indication for each of the multiple objects. The search input module can then determine a subject object from the detected multiple objects, and generate the subject object as the search input for the image search.

Provided are a display apparatus and a person recognition and presentation method. The display apparatus includes a display and a controller that is in communication with the display. The controller is configured to: associated information of a display interface of the display and generate a scenario image for recognition in response to a user command; obtain facial feature information for recognition in the scenario image; obtain similar facial feature information when a matching confidence level of pre-stored facial feature information in a database with the facial feature information for recognition does not exceed a preset confidence level; obtain average-person recognition data; generate a sharing control uniquely matching with the facial feature information for recognition; and control the display to present the average-person recognition data and the sharing control on a current display interface.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.