US 11,809,417 B2
Apparatus and method for transforming unstructured data sources into both relational entities and machine learning models that support structured query language queries
Adam Oliner, San Francisco, CA (US); Maria Kazandjieva, Menlo Park, CA (US); Eric Schkufza, Oakland, CA (US); Mher Hakobyan, Mountain View, CA (US); Irina Calciu, Palo Alto, CA (US); and Brian Calvert, San Francisco, CA (US)
Assigned to Graft, Inc., San Francisco, CA (US)
Filed by Graft, Inc., San Francisco, CA (US)
Filed on Sep. 28, 2021, as Appl. No. 17/488,043.
Claims priority of provisional application 63/216,431, filed on Jun. 29, 2021.
Prior Publication US 2023/0072311 A1, Mar. 9, 2023
Int. Cl. G06F 16/242 (2019.01); G06F 16/2458 (2019.01); G06F 16/28 (2019.01); G06F 16/33 (2019.01); G06N 20/20 (2019.01)
CPC G06F 16/2448 (2019.01) [G06F 16/2458 (2019.01); G06F 16/285 (2019.01); G06F 16/3332 (2019.01); G06N 20/20 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A non-transitory computer readable storage medium with instructions executed by a processor to:
receive from a network connection different sources of unstructured data;
form an entity combining one or more sources of the unstructured data, wherein the entity has relational data attributes;
create a representation for the entity, wherein the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner;
create an enrichment model to predict a property of the entity; and
process a query to produce a query result, wherein the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model, wherein Structured Query Language (SQL) extensions are supplied to facilitate the operation to process the query to produce the query result, wherein the SQL extensions include SQL extensions to query against time-windowed versions of data.