Meta Platforms had nine patents in ecommerce during Q1 2024. Meta Platforms Inc’s patents filed in Q1 2024 focus on utilizing machine learning modules to assess social networking relationships and generate recommendations, providing user recommendations for entities with recommendation scores, predicting user intent to take action and delivering relevant content items, and verifying the integrity of program code in processor enclaves. These patents showcase the company’s innovative approaches to enhancing user experiences and data security. GlobalData’s report on Meta Platforms gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Meta Platforms grant share with ecommerce as a theme is 33% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Machine learning-based social network relationship and recommendation generator (Patent ID: US20240086987A1)

The patent filed by Meta Platforms Inc. describes systems and methods for utilizing machine learning modules to evaluate social networking relationships and provide recommendations. The process involves gathering user information, product information, and social networking data to create a social networking graph. A first machine learning module is used to generate recommendations, such as product recommendations, based on user information and the social networking graph. The machine learning model is trained using product recommendations and previous purchases. Additionally, a second machine learning module generates weighting characteristics for users and products, which are then applied to generate product recommendations.

The computer-implemented method outlined in the patent involves receiving user and product information, creating a social network graph based on shared links, and using a machine learning module to generate product recommendations. The method includes training the machine learning model based on the recommendations, utilizing various types of user information like search data and browsing data, and considering different types of common links such as relationship type, shared interaction, location, and interest. Furthermore, the method involves applying a second machine learning module to generate weightings for user and product characteristics, which are then used to generate product recommendations through the first machine learning module.

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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.