Salesforce had 66 patents in artificial intelligence during Q1 2024. Salesforce Inc has filed patents for systems and methods related to multimodal layout generation for digital publications, ensuring trust in non-fungible tokens, providing insights pages in CRM applications, neural network based news reading tools, and reporting success in dialog sessions. These patents cover a range of technologies aimed at enhancing user experiences and data management processes. GlobalData’s report on Salesforce gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Salesforce grant share with artificial intelligence as a theme is 60% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Systems and methods for multimodal layout designs of digital publications (Patent ID: US20240104809A1)

The patent filed by Salesforce Inc. describes systems and methods for generating multimodal layouts for digital publications. The system receives inputs such as a background image, foreground texts, and foreground images, and generates feature representations of the background image. These inputs are then used by a layout generator with cross attention to the background image feature representations to create a layout with bounding box parameters for each input item. The resulting composite layout is displayed on a user interface. The method involves generating layout bounding box parameters for foreground elements based on attention weights from cross attention between image and text representations, ensuring the foreground elements do not overlap, and training various components such as a transformer decoder and conditional discriminator to optimize layout generation.

The system for generating layouts includes components such as an image encoder, text encoder, visual transformer encoder, and attention layers at the transformer decoder. The system generates variations of layout bounding box parameters to prevent overlap of foreground elements and utilizes techniques like sampling a vector based on a gaussian noise distribution. The method also involves training a conditional discriminator to predict layout origin, an auxiliary decoder to reconstruct text, and a conditional reconstructor to reconstruct text and image. These components work together to generate layouts by overlaying foreground elements over a background image based on the specified layout bounding box parameters, ensuring an optimized and visually appealing digital publication layout.

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