ADOBE had 14 patents in future of work during Q1 2024. ADOBE Inc’s patent filed in Q1 2024 describes systems and methods that utilize machine learning models integrated with content editing tools to prevent inadvertent disclosure of sensitive data. The technology identifies entities associated with private information in unstructured text data, computes a privacy score based on connections between these entities, and updates the interface to indicate potential exposure of private information. This innovation aims to enhance data privacy and security during content editing processes. GlobalData’s report on ADOBE gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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ADOBE grant share with future of work as a theme is 35% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Machine learning modeling for protection against online disclosure of sensitive data (Patent ID: US20240046399A1)
The patent filed by ADOBE Inc. describes systems and methods that utilize machine learning models in conjunction with content editing tools to prevent or reduce the inadvertent disclosure of sensitive data. By applying a trained machine learning model to unstructured text data received through an interface, entities linked to private information are identified, and a privacy score is computed based on the connections between these entities. The interface is then updated to include an indicator that highlights a specific portion of the text data, allowing users to modify this target portion to alter the potential exposure of private information indicated by the privacy score. The system also includes components for real-time detection of modifications, processing of images or videos associated with text data, and training the machine learning model using neural networks.
The computer-implemented method and computing system outlined in the patent focus on identifying entities associated with private information, computing privacy scores based on these entities, and updating graphical interfaces to reflect potential exposure of private data. The system includes various subsystems for natural language processing, scoring, reporting, content retrieval, and media processing to ensure comprehensive data protection measures. Additionally, the patent details the use of neural networks for training the machine learning model, enabling accurate identification of entities contributing to privacy risks. Overall, the invention aims to enhance data security by leveraging machine learning algorithms and real-time monitoring to prevent unauthorized disclosure of sensitive information.
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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.
