Abstract: A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

Abstract: A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector comprising first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective substring distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary substring of the first binary substrings of the first binary vector and a respective corresponding second binary substring of the second binary substrings of the respective second binary vector for each of the second images.

Abstract: A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors to cause the one or more processors to perform: extracting one or more visual attributes of a requested product in a domain based on an image describing the requested product in an inquiry from a user; extracting one or more textual attributes of the requested product based on a text describing the requested product in the inquiry; determining projected requested products, in a product graph relating to the domain and dynamically generated by machine learning, based on the one or more visual attributes of the requested product and the one or more textual attributes of the requested product; determining one or more substitutes for the requested product based on scores of candidate products in the product graph, wherein: a greater weight is given to visual distances than textual distances in determining the scores; the visual distances and the text

Abstract: A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user and the order of the one or more substitutes to be transmitted to the user.

Abstract: A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user. The method comprises one or more of the steps including receiving an inquiry from a user, which comprises a requested product; extracting one or more attributes of the requested product based on the inquiry; determining a context of the inquiry; and determining one or more substitutes for the requested product from an inventory database based on a distance between the requested product and each substitute of the one or more substitutes, as projected in a product graph relating to a domain of the requested product and the context of the inquiry.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector of first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective sub string distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary sub string of the first binary sub strings of the first binary vector and a respective corresponding second binary sub string of the second binary sub strings of the respective second binary vector for each of the second images.

Abstract: A method including a collection expanding model for expanding a collection of items. The method can include upon receiving a user request from a user via a user device through a network, retrieving a target collection of one or more collections from a database. The target collection can comprise collection items, one or more collection styles, and one or more collection colors. The method also can include determining candidate items for the target collection based at least in part on the collection items of the target collection. The candidate items can comprise one or more complementary candidate items or one or more substitute candidate items. The method further can include removing a first candidate item of the candidate items from the candidate items when at least a dominant style of the first candidate item is not included in the one or more collection styles and/or when at least a dominant color of the first candidate item is not included in the one or more collection colors.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Abstract: A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user and the order of the one or more substitutes to be transmitted to the user.

Abstract: A system can include one or more processors and non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform a method for determining one or more substitutes for a product requested in an inquiry from a user. The method comprises one or more of the steps including receiving an inquiry from a user, which comprises a requested product; extracting one or more attributes of the requested product based on the inquiry; determining a context of the inquiry; and determining one or more substitutes for the requested product from an inventory database based on a distance between the requested product and each substitute of the one or more substitutes, as projected in a product graph relating to a domain of the requested product and the context of the inquiry.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.