US 12,079,992 B1
Utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy
Nathan Henry Lazar, Salt Lake City, UT (US); Conor Austin Forsman Tillinghast, Salt Lake City, UT (US); James Douglas Jensen, Farmington, UT (US); James Benjamin Taylor, Midlothian, VA (US); Berton Allen Earnshaw, Cedar Hills, UT (US); Marta Marie Fay, Salt Lake City, UT (US); Renat Nailevich Khaliullin, Salt Lake City, UT (US); Jacob Carter Cooper, Sandy, UT (US); Imran Saeedul Haque, Salt Lake City, UT (US); Seyhmus Guler, Salt Lake City, UT (US); Kyle Rollins Hansen, Kaysville, UT (US); and Safiye Celik, Sudbury, MA (US)
Assigned to Recursion Pharmaceuticals, Inc., Salt Lake City, UT (US)
Filed by Recursion Pharmaceuticals, Inc., Salt Lake City, UT (US)
Filed on Dec. 21, 2023, as Appl. No. 18/393,041.
Claims priority of provisional application 63/582,702, filed on Sep. 14, 2023.
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06V 10/74 (2022.01); G06V 10/94 (2022.01); G06V 20/69 (2022.01); G16B 40/00 (2019.01)
CPC G06T 7/0012 (2013.01) [G06V 10/761 (2022.01); G06V 10/95 (2022.01); G06V 20/698 (2022.01); G16B 40/00 (2019.02); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30072 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, from a client device, a request for a benchmark measure for a perturbation embedding model;
identifying perturbation data for the perturbation embedding model comprising a plurality of perturbation unit embeddings generated by the perturbation embedding model from a plurality of perturbation experiment units;
determining, utilizing a benchmark model, a univariate benchmark measure from the plurality of perturbation unit embeddings by utilizing a first perturbation unit embedding from the plurality of perturbation unit embeddings corresponding to a shared perturbation class and a second perturbation unit embedding from the plurality of perturbation unit embeddings corresponding to the shared perturbation class by:
determining a first similarity measure between the first perturbation unit embedding and the second perturbation unit embedding;
determining a second similarity measure between the first perturbation unit embedding and a third perturbation unit embedding corresponding to the shared perturbation class;
determining a third similarity measure between the second perturbation unit embedding and the third perturbation unit embedding; and
determining a combined similarity measure for the shared perturbation class from the first similarity measure, the second similarity measure, and the third similarity measure; and
providing, for display via a user interface of the client device, the univariate benchmark measure for the perturbation embedding model.