Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives.This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration.Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work.We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem.We demonstrate our method by computing an FCG on 500K patents, which we release for further research.A user study indicates that participants exposed to MUSE's inspirations generated more creative ideas, both in terms of absolute number (up to 19% increase over participants not given inspirations) and ratio (75%, compared to 49% for no inspirations).

We present an algorithm for producing a seamless animated loop from a single image. The algorithm detects periodic structures, such as the windows of a building or the steps of a staircase, and generates a non-trivial displacement vector field that maps each segment of the structure onto a neighboring segment along a user- or auto-selected main direction of motion. This displacement field is used, together with suitable temporal and spatial smoothing, to warp the image and produce the frames of a continuous animation loop. Our cinemagraphs are created in under a second on a mobile device. Over 140,000 users downloaded our app and exported over 350,000 cinemagraphs. Moreover, we conducted two user studies that show that users prefer our method for creating surreal and structured cinemagraphs compared to more manual approaches and compared to previous methods.