The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 28, 2025

Filed:

Oct. 04, 2021
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Elena Pochernina, London, GB;

John Winn, Cambridge, GB;

Matteo Venanzi, London, GB;

Ivan Korostelev, London, GB;

Pavel Myshkov, London, GB;

Samuel Alexander Webster, Cambridge, GB;

Yordan Kirilov Zaykov, Cambridge, GB;

Nikita Voronkov, Bothell, WA (US);

Dmitriy Meyerzon, Bellevue, WA (US);

Marius Alexandru Bunescu, Redmond, WA (US);

Alexander Armin Spengler, Cambridge, GB;

Vladimir Gvozdev, Sammamish, WA (US);

Thomas P. Minka, Cambridge, GB;

Anthony Arnold Wieser, Fen Ditton, GB;

Sanil Rajput, San Francisco, CA (US);

John Guiver, Saffron Walden, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 16/901 (2019.01); G06F 16/903 (2019.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 16/9024 (2019.01); G06F 16/90335 (2019.01);
Abstract

In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.


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