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:
May. 29, 2018

Filed:

Mar. 10, 2017
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Wen Liu, Santa Clara, CA (US);

Ashutosh Modi, Saarbruecken, DE;

Bogdan Georgescu, Plainsboro, NJ (US);

Francisco Pereira, Jersey City, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/27 (2006.01); G06F 17/30 (2006.01); G06F 19/00 (2018.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06F 17/271 (2013.01); G06F 17/279 (2013.01); G06F 17/2785 (2013.01); G06F 17/3043 (2013.01); G06F 19/345 (2013.01); G06N 3/0445 (2013.01); G06N 3/08 (2013.01);
Abstract

A computer-implemented method for predicting answers to questions concerning medical image analytics reports includes splitting a medical image analytics report into a plurality of sentences and generating a plurality of sentence embedding vectors by applying a natural language processing framework to the plurality of sentences. A question related to subject matter included in the medical image analytics report is received and a question embedding vector is generated by applying the natural language processing framework to the question. A subset of the sentence embedding vectors most similar to the question embedding vector is identified by applying a similarity matching process to the sentence embedding vectors and the question embedding vector. A trained recurrent neural network (RNN) is used to determine a predicted answer to the question based on the subset of the sentence embedding vectors.


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