Colorado Patent of the Month – December 2023

Flyreel, Inc., a key component of an end-to-end property intelligence LexisNexis® solution, has patented a novel system set to revolutionize how we perceive and measure interior spaces. This inventive system for estimating the layout of interior spaces integrates advanced technology and artificial intelligence.

At its core, the system comprises a sophisticated interplay of components—a processor, memory housing specialized instructions, and a computing device equipped with an image sensor. The system’s prowess lies in its ability to receive both labeled panorama images, adorned with layout annotations representing boundary junctures within the interior space, and unlabeled panorama images, capturing different facets of the space.

Flyreel sets itself apart with the incorporation of a trained neural network model. This model, trained with a fusion of labeled and unlabeled data, outputs a meticulous reconstruction layout of at least a portion of the interior space. The true marvel, however, lies in the model’s ability to provide precise coordinates, pinpointing the locations of junctures within the reconstructed layout.

The system’s versatility extends further with the inclusion of a sensor suite, enhancing its capabilities by incorporating location data corresponding to juncture positions within the labeled panorama images. This integration with a coordinate system associated with the interior space ensures a comprehensive and accurate representation.

Flyreel’s commitment to excellence is evident in the nuanced selection process of the trained neural network model. Whether influenced by the complexity or type of room associated with the panorama images, the system showcases adaptability and intelligence. Notably, a stochastic neural network, with stochastic weights during training, ensures robustness and accuracy in the face of varied challenges.

In a world where traditional methods fall short, Flyreel’s system emerges as a beacon of progress, offering a transformative approach to interior space measurement. This leap in innovation not only addresses the limitations of conventional measuring tools but also opens new frontiers in the application of deep semi-supervised learning.

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