US 12,292,398 B2
Systems and methods for interpreting high energy interactions
Brandon Lee Goodchild Drake, Greeley, CO (US)
Assigned to Veracio Ltd., Salt Lake City, UT (US); and Decision Tree, LLC, Greeley, CO (US)
Filed by DECISION TREE, LLC, Greeley, CO (US); and VERACIO, LTD., West Valley City, UT (US)
Filed on Dec. 7, 2023, as Appl. No. 18/532,178.
Application 18/532,178 is a continuation of application No. 17/282,206, granted, now 11,874,240, previously published as PCT/US2019/054741, filed on Oct. 4, 2019.
Claims priority of provisional application 62/741,231, filed on Oct. 4, 2018.
Prior Publication US 2024/0125717 A1, Apr. 18, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G01N 23/223 (2006.01); G01N 23/207 (2018.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01)
CPC G01N 23/223 (2013.01) [G01N 23/2076 (2013.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01)] 25 Claims
OG exemplary drawing
 
1. An analysis method, comprising:
training a machine learning module for interpreting high energy interactions, wherein training the machine learning module comprises:
impinging radiation from a source on an analyte;
detecting energy interactions resulting from the impinging radiation using a radiation detector, wherein the radiation detector produces a signal indicative of the detected energy interactions;
adjusting the signal from the radiation detector to emphasize specific parts of the signal of the radiation detector that are associated with quantitative or qualitative values;
producing quantitative and qualitative models derived from the machine leaning module; and
applying the machine learning module to additional energy interactions.