HubSpot. has filed a patent for a method involving neural networks to process entity feature encodings for deduplication, generating likelihood representations of entity pairs being duplicates, training a machine learning model, and utilizing it to deduplicate objects in a database. The technology aims to improve computer systems and data stores. GlobalData’s report on HubSpot gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on HubSpot, Predictive modeling techniques was a key innovation area identified from patents. HubSpot's grant share as of January 2024 was 49%. Grant share is based on the ratio of number of grants to total number of patents.
Entity deduplication using neural network and machine learning model
A recently filed patent (Publication Number: US20230418793A1) outlines a method for deduplication using a neural network and machine learning model. The method involves processing entity feature encodings to generate reduced vectors, creating likelihood representations of duplicate entity pairs, training a machine learning model with these likelihoods and control values, and utilizing the model to deduplicate objects in a database. The patent also describes the use of artificial intelligence proxies for deduplication processes with high computation demands, such as matching strings associated with objects and applying heuristics to objects.
Furthermore, the patent details a system and a non-transitory machine-readable storage medium that implement the method described. These implementations involve processing entity feature encodings, generating likelihood representations of duplicate entity pairs, training a machine learning model, and utilizing it for deduplication. The system and storage medium also incorporate artificial intelligence proxies for deduplication processes with high computation demands, including using text encoders, trained neural networks, and merge indicator functions. Overall, the patent presents a comprehensive approach to deduplication using neural networks, machine learning models, and artificial intelligence proxies to efficiently manage and clean databases.
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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.
