ADOBE had 26 patents in ecommerce during Q4 2023. ADOBE Inc’s patents in Q4 2023 focus on innovative systems and methods for user representation generation, determining causal relationships in mixed datasets, product retrieval based on text descriptions, Nonsymmetric Determinantal Point Processes for compatible set recommendations, and generating actionable KPI-based customer segments. These patents showcase ADOBE‘s commitment to developing advanced technologies for data analysis and user engagement. GlobalData’s report on ADOBE gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

ADOBE grant share with ecommerce as a theme is 53% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Generating concise and common user representations for edge systems from event sequence data stored on hub systems (Patent ID: US20230419339A1)

The patent filed by ADOBE Inc. describes a system that includes a representation generator subsystem capable of generating a user representation by executing a user representation model and a task prediction model. This user representation is a vector of a predetermined size that represents user event sequence data, such as user interactions with the system, and is stored in a data store with a lower storage requirement than the original event sequence data. The system can utilize different types of learning models, such as task-specific, multitask, or task-agnostic models, to generate these user representations efficiently.

Furthermore, the patent includes claims for a system, a non-transitory computer-readable medium, and a method for executing the user representation model, generating user representations, and storing them in user profiles with reduced storage requirements compared to the original event sequence data. The user representation models can be task-specific, multitask, or task-agnostic, and can include different types of neural networks like long short-term memory (LSTM) networks or autoencoder networks. Overall, the patent focuses on optimizing the storage and representation of user data in a system efficiently.

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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.