Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating image layers and determining layer labels utilizing a machine learning approach. For example, the disclosed systems utilize an image segmentation machine learning model to segment the digital image and identify individual objects depicted within the digital image. Additionally, in some embodiments, the disclosed systems determine object classifications for the depicted objects by utilizing an object classification machine learning model. In some cases, the disclosed systems further generate image layers for the digital image by generating a separate layer for each identified object (or for groups of similar objects). In certain embodiments, the disclosed systems also determine layer labels for the image layers according to the object classifications of the respective objects depicted in each of the image layers.

Abstract: Techniques for template generation from image content include extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

Abstract: Embodiments are disclosed for multichannel content recommendation. The method may include receiving an input collection comprising a plurality of images. The method may include extracting a set of feature channels from each of the images. The method may include generating, by a trained machine learning model, an intent channel of the input collection from the set of feature channels. The method may include retrieving, from a content library, a plurality of search result images that include a channel that matches the intent channel. The method may include generating a recommended set of images based on the intent channel and the set of feature channels.

Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image.

Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image.

Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating image layers and determining layer labels utilizing a machine learning approach. For example, the disclosed systems utilize an image segmentation machine learning model to segment the digital image and identify individual objects depicted within the digital image. Additionally, in some embodiments, the disclosed systems determine object classifications for the depicted objects by utilizing an object classification machine learning model. In some cases, the disclosed systems further generate image layers for the digital image by generating a separate layer for each identified object (or for groups of similar objects). In certain embodiments, the disclosed systems also determine layer labels for the image layers according to the object classifications of the respective objects depicted in each of the image layers.

Abstract: A method and system for dynamically visualizing a user experience, can involve tracking data indicative of an interaction and a journey taken by a customer and an agent, and selectively rendering a visualization of the interaction and the journey taken by the customer and the agent to aid in a faster resolution of an issue experienced by the customer. In addition, deviant states and routes can be identified within the data indicative of interactions and journeys taken by the customer and the agent.

Abstract: A multi-degree-of-freedom electromagnetic machine includes a spherical structure, a first coil, a second coil, a non-magnetic structure, and a Halbach array. The spherical structure has a first axis of symmetry, a second axis of symmetry, and a third axis of symmetry, and the first, second, and third axes of symmetry are disposed perpendicular to each other. The first coil is wound on the spherical structure about the first axis of symmetry, and the second coil is wound on the spherical structure about the second axis of symmetry. The non-magnetic structure is spaced apart from, and at least partially surrounds, the spherical structure. The Halbach array is mounted on the non-magnetic structure and includes N-magnets, where N is a multiple of 4. The spherical structure and non-magnetic structure are mounted to allow relative rotation between the non-magnetic structure and the spherical structure.

Abstract: A multi-degree-of-freedom electromagnetic machine includes a spherical structure, a first coil, a second coil, a non-magnetic structure, and a Halbach array. The spherical structure has a first axis of symmetry, a second axis of symmetry, and a third axis of symmetry, and the first, second, and third axes of symmetry are disposed perpendicular to each other. The first coil is wound on the spherical structure about the first axis of symmetry, and the second coil is wound on the spherical structure about the second axis of symmetry. The non-magnetic structure is spaced apart from, and at least partially surrounds, the spherical structure. The Halbach array is mounted on the non-magnetic structure and includes N-magnets, where N is a multiple of 4. The spherical structure and non-magnetic structure are mounted to allow relative rotation between the non-magnetic structure and the spherical structure.

Abstract: A method of selecting an intervention includes, based on user content submitted to a platform, determining whether the content includes objectionable content. Based on the objectionable content, a content persona classification is associated with the objectionable content. Based on the content persona classification, an intervention is selected. The objectionable content may include, for example, content that has one or more characterizations as: subjective, misaligned, trigger, misguided, expressive, non-owner, offensive, prohibited, trigger product, trigger description, expired/invalid, and/or otherwise objectionable.

Abstract: A method and system for dynamically visualizing a user experience, can involve tracking data indicative of an interaction and a journey taken by a customer and an agent, and selectively rendering a visualization of the interaction and the journey taken by the customer and the agent to aid in a faster resolution of an issue experienced by the customer. In addition, deviant states and routes can be identified within the data indicative of interactions and journeys taken by the customer and the agent.