Williams Sonoma has been granted a patent for techniques involving machine learning-based image attribute determination. The method involves determining unknown attributes of an image with a prescribed environment using a machine learning framework trained on image data sets simulating the environment. GlobalData’s report on Williams Sonoma 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 Williams Sonoma, was a key innovation area identified from patents. Williams Sonoma's grant share as of May 2024 was 70%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning based image attribute determination
A recently granted patent (Publication Number: US12002149B2) discloses a method, system, and computer program product for determining unknown attributes of an image using a machine learning-based framework. The method involves receiving an image with unknown attributes in a prescribed environment and utilizing a machine learning model trained on image data sets that simulate the prescribed environment to determine these unknown attributes. The attributes could include pixel location in three-dimensional space, pixel xyz coordinates, pixel depth, and pixel surface normal vectors, among others. The prescribed environment, which could be a model environment or an actual physical environment, provides known information such as structure, geometry, camera details, and lighting information to aid in attribute determination.
The system and computer program product described in the patent also follow a similar approach, with a processor receiving the image and employing the machine learning framework to determine the unknown attributes. The attributes are inferred from training image data sets that closely resemble the prescribed environment, with various attributes learned by the machine learning model derived from associated metadata. The use of neural networks, including convolutional neural networks, enhances the accuracy of attribute determination for still images or frames of video sequences. Overall, the patent presents a comprehensive approach to leveraging machine learning for attribute determination in images, providing a valuable tool for applications requiring precise understanding of image attributes within specific environments.
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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.
