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:
Jun. 13, 2023

Filed:

Mar. 20, 2020
Applicant:

Illumina, Inc., San Diego, CA (US);

Inventors:

Kishore Jaganathan, San Francisco, CA (US);

John Randall Gobbel, Brisbane, CA (US);

Amirali Kia, San Mateo, CA (US);

Assignee:

Illumina, Inc., San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G16B 40/20 (2019.01); G06N 3/08 (2023.01); G16B 40/00 (2019.01); G06N 3/04 (2023.01); G06F 16/907 (2019.01); G06N 3/084 (2023.01); G06V 10/82 (2022.01); G06F 18/23 (2023.01); G06F 18/24 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06F 18/23211 (2023.01); G06N 7/01 (2023.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/44 (2022.01); G06V 10/98 (2022.01); G06N 5/046 (2023.01);
U.S. Cl.
CPC ...
G16B 40/20 (2019.02); G06F 16/907 (2019.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/23 (2023.01); G06F 18/23211 (2023.01); G06F 18/24 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 7/01 (2023.01); G06V 10/454 (2022.01); G06V 10/763 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/7784 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); G16B 40/00 (2019.02); G06N 5/046 (2013.01);
Abstract

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.


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