Illumina had 11 patents in digitalization during Q1 2024. The patents filed by Illumina Inc in Q1 2024 involve methods for extracting information from datasets using optical character recognition and classifying them into different classes, assigning quality scores to bases called by a neural network-based base caller, and generating a hash table to improve mapping of reads by storing interval information for extended seeds in a seed extension tree. GlobalData’s report on Illumina gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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Illumina grant share with digitalization as a theme is 45% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Systems and methods for automated classification of a document (Patent ID: US20240070170A1)

The patent filed by Illumina Inc. describes a method for extracting information from a dataset, such as a document, by receiving the dataset at an information handling device, extracting textual information using optical character recognition, and classifying the dataset into different classes based on similarity scores calculated for different window regions of the dataset. The method involves training a machine-learning model to classify documents into various classes by identifying focus words, extracting text regions containing these words, and establishing associations between documents and classes based on the extracted text regions.

Furthermore, the patent outlines the use of free text documents, specifically medical reports or breast imaging reports, for training the machine-learning model. It also details the classification of breast imaging reports into classes like fatty, scattered fibroglandular density, heterogeneously dense, extremely dense, and indeterminate. The training process involves computing training relevance metrics for documents, applying these metrics to the model, and optimizing hyperparameters using an iterative grid search algorithm to enhance the accuracy of the machine-learning model. The system described in the patent includes a computer system with memory storing instructions and a processor to execute operations related to training the machine-learning model for document classification based on text regions containing focus words.

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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.