A web-based item listing platform provides item listings that users can create or search. Item listings can be generated using structured information extracted while capturing an item listing video of the item. During creation of the item listing video, input prompts are provided to the user that cause a mobile device to provide an input request, such as taking an image of a specific feature of the item or providing some other item description information. During the item listing video, image recognition models may also be employed to determine other item description information, such as the color, the brand, and the like. The item listing can be generated from the item listing video by populating a set of structured data elements associated with an item description type. Each structured data element is populated with the item description information corresponding to the associated item description type.

The technology disclosed relates to artificial intelligence based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive prioritization. Certain embodiments utilize systems, methods, and computer program products that perform predictive prioritization using a combination of supervised machine learning models and unsupervised machine learning models that are in turn used to generate target features for a resultant prioritization machine learning model.

A method and an electronic device are disclosed. The method includes obtaining an image, obtaining information of the image, obtaining content information of content included in the image, obtaining related information which relates to the image based on at least one of the information of the image and the content information, and classifying the image into at least one category based on a plurality of defined information/data elements and a relation among the information/data elements and metadata of the image.

A method and an electronic device are disclosed. The method includes obtaining an image, obtaining information of the image, obtaining content information of content included in the image, obtaining related information which relates to the image based on at least one of the information of the image and the content information, and classifying the image into at least one category based on a plurality of defined information/data elements and a relation among the information/data elements and metadata of the image.

Implementations relate to generating theme-based videos. In some implementations, a computer-implemented method to automatically generate a theme-based video includes obtaining image criteria for a theme from a theme definition, querying an image database to obtain a plurality of images that meet the image criteria, determining that a count of the plurality of images satisfies a threshold based on the theme definition, and generating the theme-based video that includes one or more of the plurality of images.

Implementations relate to generating theme-based videos. In some implementations, a computer-implemented method to automatically generate a theme-based video includes obtaining image criteria for a theme from a theme definition, querying an image database to obtain a plurality of images that meet the image criteria, determining that a count of the plurality of images satisfies a threshold based on the theme definition, and generating the theme-based video that includes one or more of the plurality of images.

To provide an inventory management server, an inventory management system, an inventory management program, and an inventory management method which are capable of carrying out inventory management such as warehousing processing and delivery processing easily and inexpensively by using an image for identifying merchandise together with an image of the merchandise. Provided is an inventory management server 4 for carrying out inventory management using images of merchandise, the inventory management server 4 including: a warehousing data acquisition unit 61 which acquires warehousing data from a user terminal, the warehousing data including a merchandise basic image obtained by photographing merchandise to be warehoused, an identification information image obtained by photographing identification information on the merchandise, a warehoused quantity of the merchandise, and a warehousing date of the merchandise; and an inventory data storage unit 52 which stores the identification information image and the warehoused quantity and warehousing date of the merchandise as inventory data in association with the acquired merchandise basic image.

An automated queue is provided including processing circuitry configured to receive an image including a face of a customer, receive an indication of a selected service, enter the customer in virtual queue based on the indication of the selected service, and in response to the customer reaching the front of the virtual queue, causing a transmission of the selected service and at least a portion of the image including the face of the customer to an agent user interface.

A first type of data regarding an unidentified product is collected. A first type of analysis on the first type of data is performed. A second type of data regarding an unidentified product is collected. A second type of analysis on the second type of data is performed. Based upon the first type of analysis on the first type of data and the second type of analysis on the second type of data, product identification for the unidentified product is performed. Based on the product identification, an identity of the unidentified product is output. A user is enabled to perform a business interaction with a product matching the identity of the unidentified product.