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 June 2024 was 49%. 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 facilitates the transfer of feature vectors from the first vector space to the second, even when the vectors are not synonymous, thereby enriching the second vector space with additional data. The data analyzing unit then conducts analysis on the feature vectors stored in the updated second vector space.
Further claims detail the technical aspects of the apparatus, including the use of eigenvectors for mapping and the specific types of data sets that can be utilized, such as text data and chemical formula data. The apparatus can compute word feature vectors from textual data and chemical formula feature vectors from chemical data, allowing for a comprehensive analysis of relationships across different domains. The claims also outline the internal components of the vector computation units, such as word extraction and vector computation units, which work together to derive meaningful index values that reflect the relationships between the texts and words or chemical structures. This innovative approach aims to improve data analysis capabilities by leveraging the relationships between diverse data types.
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