Williams Sonoma. has filed a patent for a machine learning image processing system that can detect and modify attributes in images. The system is trained on specific scene types and can generate modified images based on the detected attributes. GlobalData’s report on Williams Sonoma gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Williams Sonoma, was a key innovation area identified from patents. Williams Sonoma's grant share as of January 2024 was 81%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning based image processing for object detection
The patent application (Publication Number: US20240005456A1) describes a method for modifying images using a machine learning framework trained on a set of training images labeled with attributes associated with a prescribed scene type. The method involves detecting attributes of an input image and generating an output image with modified attributes, such as aesthetics, style, objects, lighting, and noise. The output image can be labeled with corresponding attributes and may include different aesthetics, restyled versions, object replacements, relit versions, or denoised versions of the input image. The method can be applied to various scene types, geometries, materials, textures, camera characteristics, and noise statistics, and can produce photographs, photorealistic renderings, or video frames.
Furthermore, the patent application also includes a system and a computer program product for implementing the method. The system comprises a processor configured to detect attributes of an input image and generate a modified output image using the machine learning framework, while the computer program product includes instructions for performing the same process. The machine learning framework can be a deep neural network, a convolutional neural network, or a combination of both, enabling the modification of images based on learned attributes associated with specific scene types. Overall, the method, system, and computer program product provide a comprehensive approach to image modification using machine learning techniques trained on labeled training images with prescribed scene types.
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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.
