FMR had four patents in big data during Q1 2024. FMR LLC filed patents in Q1 2024 for methods and apparatuses related to intelligent imputation of missing data in machine learning datasets, disambiguating unrecognized abbreviations in search queries using machine learning, and generating compliance indicators on a user interface for continuous integration/continuous deployment systems. These inventions involve analyzing data characteristics, executing machine learning models, and displaying compliance levels on user interfaces. GlobalData’s report on FMR gives a 360-degree view of the company including its patenting strategy. Buy the report here.

FMR grant share with big data as a theme is 25% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Intelligent generation of code for imputation of missing data in a machine learning dataset (Patent ID: US20240069874A1)

The patent filed by FMR LLC describes methods and apparatuses for intelligent imputation of missing data in a machine learning dataset. The system involves analyzing the dataset to generate characteristics of the missing values, selecting an imputation algorithm based on these characteristics, determining a computing environment for executing the algorithm, generating code for the imputation process, and integrating the code into a machine learning platform to assign replacement values to the missing data. The method also includes receiving datasets from remote devices, analyzing missing value distributions, and selecting appropriate imputation algorithms based on the dataset's characteristics.

The system further involves determining the computing environment based on the dataset size, generating code based on user-specified output file formats, and integrating the code into a machine learning platform for seamless execution. The patent outlines a detailed process for analyzing missing data, selecting suitable imputation algorithms, and executing the imputation process efficiently in different computing environments. Additionally, the system allows for the handling of various types of missing data scenarios, such as numeric or categorical features, classification or regression problems, and time series data. Overall, the patent provides a comprehensive framework for intelligent imputation of missing data in machine learning datasets, enhancing the accuracy and reliability of data analysis and model training processes.

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