FRONTEO. has been granted a patent for a data analysis apparatus that utilizes vector computation units to create two distinct vector spaces from different data sets. It includes a mapping unit to integrate non-synonymous feature vectors, enabling comprehensive data analysis across both vector spaces. GlobalData’s report on FRONTEO 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 FRONTEO, Predictive modeling techniques was a key innovation area identified from patents. FRONTEO's grant share as of July 2024 was 45%. Grant share is based on the ratio of number of grants to total number of patents.
Data analysis apparatus using vector computation and mapping
The granted patent US12026461B1 describes a data analysis apparatus designed to enhance the processing and analysis of data sets through the formation of vector spaces. The apparatus includes two distinct vector computation units: the first unit computes feature vectors from a first data set, while the second unit processes a different second data set to generate additional feature vectors. These vectors reflect relationships between the data and predetermined elements. A vector mapping unit is also included, which facilitates the mapping of feature vectors from the first vector space to the second, even when the items are not synonymous. This mapping is guided by a computed mapping vector, allowing for the integration of data from disparate sources and enhancing the analytical capabilities of the system.
Further claims detail the technical aspects of the apparatus, including the use of eigenvectors for mapping synonymous feature vectors and the specific configurations of the vector computation units. These units can analyze text data and chemical formula data, enabling the apparatus to compute word feature vectors and chemical formula feature vectors. The system is designed to perform comprehensive data analysis on the updated second vector space, which includes both the original and newly mapped feature vectors. This innovative approach allows for a more nuanced understanding of the relationships within the data, potentially benefiting various fields such as natural language processing and chemical informatics.
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