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:
Oct. 15, 2019

Filed:

Nov. 21, 2018
Applicant:

Fmr Llc, Boston, MA (US);

Inventors:

Gopalakrishnan Subramanian, Acton, MA (US);

Srinivas Gururaja Rau, Bangalore, IN;

Bhanu Prashanthi Murthy, Bangalore, IN;

Ankan Pal, Bangalore, IN;

Akhilesh Raghavendrachar Srinivasachar Kaddi, Karnataka, IN;

Ralph Hollinshead, Cary, NC (US);

Shankar Vaidhyanathan, Bangalore, IN;

Assignee:

FMR LLC, Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/00 (2006.01); G06F 11/07 (2006.01); G06N 20/10 (2019.01); G06F 16/33 (2019.01); G06F 16/25 (2019.01); G06F 16/35 (2019.01);
U.S. Cl.
CPC ...
G06F 11/079 (2013.01); G06F 11/0709 (2013.01); G06F 11/0751 (2013.01); G06F 11/0778 (2013.01); G06F 11/0793 (2013.01); G06F 16/254 (2019.01); G06F 16/3347 (2019.01); G06F 16/355 (2019.01); G06N 20/10 (2019.01);
Abstract

Methods and systems are described for data lineage identification and change impact prediction. Servers capture metadata that defines data objects associated with data sources. The servers determine direct relationships between data sources based upon the captured metadata. The servers identify indirect relationships between the data sources. The servers generate a data lineage across the data sources for the data objects. The servers extract unstructured text from database incident tickets and match the unstructured text to the metadata. The servers generate a multidimensional vector for the data objects based upon the data lineage and the unstructured text. The servers train a classification model using the vectors to predict a change impact score for each data object. The servers receive a request to change a data object. The servers determine a change impact score for the data object. When the score is below a threshold, the servers execute the change.


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