Fugro has been granted a patent for a computer-implemented method to identify features of interest in data images. The method involves identifying data variations, features of interest, genuses, reclassifying data to eliminate background, and generating feature maps. Additionally, a machine learning method for automatic feature identification is included. GlobalData’s report on Fugro gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Fugro, Marine seismic data acquisition was a key innovation area identified from patents. Fugro's grant share as of May 2024 was 41%. Grant share is based on the ratio of number of grants to total number of patents.
Computer-implemented method for identifying features of interest in dataset
A recently granted patent (Publication Number: US11978249B2) discloses a computer-implemented method for identifying features of interest in a dataset. The method involves identifying data variations in a data image or set of datasets, determining features of interest based on these variations, and reclassifying rendered data to eliminate background data. By generating feature of interest datasets and converting them into polygons, the method calculates geometries and spatial parameters to determine the significance of each feature. Additionally, the method isolates features of interest based on size, assigns relative weights using a ranking system, and generates ranked feature of interest maps for each identified feature genus.
Furthermore, the patent describes the application of machine learning in identifying features of interest in a data image. The method includes a training phase where data variation characteristics are determined and stored in a database, along with the corresponding feature of interest genus. In the identification phase, features of interest are automatically identified in a data image based on stored characteristics, and the analysis phase involves reclassifying rendered data to eliminate background and converting features into polygons. The method also includes isolating features of interest based on size or significance, enhancing the accuracy of feature identification and mapping in datasets. Overall, the patent presents a comprehensive approach to efficiently identifying and analyzing features of interest in datasets using advanced computer-implemented methods and machine learning techniques.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
