BenevolentAI has been granted a patent for a computer-implemented method that embeds text describing relationships between entities of interest. The method involves generating sets of embeddings for separable entities and using them in a machine learning model or classifier. The patent aims to enhance data analysis and information retrieval processes. GlobalData’s report on BenevolentAI 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 BenevolentAI, was a key innovation area identified from patents. BenevolentAI's grant share as of February 2024 was 21%. Grant share is based on the ratio of number of grants to total number of patents.
Text embedding for entities and relationships using machine learning
A recently granted patent (Publication Number: US11886822B2) discloses a computer-implemented method for embedding a portion of text describing a relationship for one or more entities of interest. The method involves receiving a portion of text containing data representative of the relationship, generating sets of embeddings for separable entities within the text, inputting these embeddings into a machine learning model or classifier, and storing the generated embeddings in an embedding vocabulary dataset. The method includes steps for retrieving embeddings from the dataset, generating out-of-vocabulary embeddings when necessary, and forming composite embeddings for the received text based on combinations of the generated embeddings. Additionally, the patent covers the formation of composite embeddings for multiple portions of text and inputting them into a machine learning model or classifier trained to identify specific relationships for the entities of interest.
Furthermore, the patent describes the use of various machine learning techniques such as feedforward neural networks, recursive neural networks, convolutional neural networks, and autoencoder neural networks in the embedding model and the machine learning model or classifier. The method also involves generating sets of embeddings for separable entities and relationship entities within the text, combining these embeddings to form composite embeddings, and inputting them into the machine learning model or classifier for predicting evidence supporting the relationship described in the text. The patent also covers the generation of an embedding vocabulary dataset based on training an embedding model using a labelled training dataset representative of multiple portions of text, showcasing a comprehensive approach to embedding text data for relationship analysis using machine learning technologies.
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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
