ADOBE had 15 patents in big data during Q4 2023. ADOBE Inc has developed innovative approaches for determining causal relationships in mixed datasets, generating actionable customer segments based on key performance indicators, training decision tree models, and incorporating unobserved behaviors in user segment generation and future action predictions. These advancements in analytics and machine learning aim to enhance marketing insights and intelligence for more targeted and effective marketing strategies. 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 big data as a theme is 66% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Discovering causal relationships in mixed datasets (Patent ID: US20230385854A1)

The patent filed by ADOBE Inc. introduces a method for determining causal relationships in mixed datasets containing both continuous and discrete variables. The approach involves producing an undirected graph to establish dependency among the variables before discretizing the continuous data. This ensures that information regarding dependence is preserved throughout the process. The method includes steps such as discretizing data based on neighboring discrete variables, identifying causal relationships through a greedy search of candidate directed graphs, and displaying the directed graph visually to convey the relationships among the variables.

Furthermore, the patent describes a system that implements this method, utilizing a probabilistic machine learning algorithm to identify the directed graph reflecting causal relationships among the variables. The system produces an undirected graph indicating dependency among the variables, discretizes continuous data based on neighboring discrete variables, and determines the directed graph based on the discretized dataset and the undirected graph. By considering forbidden edges and performing a greedy search over causal Bayesian networks, the system effectively identifies the causal relationships in the mixed dataset. Overall, the patent introduces a comprehensive approach to analyzing mixed datasets and determining causal relationships, which can be valuable for marketing insight and intelligence platforms.

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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.