Brian James Calvert

US Patent:

20220414157, Dec 29, 2022

Filed:

Jun 29, 2022

Appl. No.:

17/853673

Inventors:

- 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
Brian CALVERT - San Francisco CA, US
Daniel WOOLRIDGE - Los Angeles CA, US
Deven NAVANI - San Jose CA, US

International Classification:

G06F 16/901
G06F 16/21
G06F 16/28

Abstract:

A non-transitory computer readable storage medium has instructions executed by a processor to maintain a repository of machine learning directed acyclic graphs. Each machine learning directed acyclic graph has machine learning artifacts as nodes and machine learning executors as edges joining machine learning artifacts. Each machine learning artifact has typed data that has associated conflict rules maintained by the repository. Each machine learning executor specifies executable code that executes a machine learning artifact as an input and produces a new machine learning artifact as an output. A request about an object in the repository is received. A response with information about the object is supplied.

US Patent:

20220414254, Dec 29, 2022

Filed:

May 3, 2022

Appl. No.:

17/735994

Inventors:

- 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
Brian CALVERT - San Francisco CA, US
Deven NAVANI - San Jose CA, US

International Classification:

G06F 21/62
G06F 21/31
G06F 16/33
G06F 16/338

Abstract:

A non-transitory computer readable storage medium with instructions executed by a processor maintains a collection of data access connectors configured to access different sources of unstructured data. A user interface with prompts for designating a selected data access connector from the data access connectors is supplied. Unstructured data is received from the selected data access connector. Numeric vectors characterizing the unstructured data are created from the unstructured data. The numeric vectors are stored and indexed.

US Patent:

20230069958, Mar 9, 2023

Filed:

Feb 23, 2022

Appl. No.:

17/678942

Inventors:

- 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
Brian CALVERT - San Francisco CA, US
Daniel WOOLRIDGE - Los Angeles CA, US

International Classification:

G06F 16/31
G06F 16/33

Abstract:

A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.

US Patent:

20230072311, Mar 9, 2023

Filed:

Sep 28, 2021

Appl. No.:

17/488043

Inventors:

- 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
Brian CALVERT - San Francisco CA, US

International Classification:

G06F 16/242
G06F 16/2458
G06F 16/28
G06N 20/20
G06F 16/33

Abstract:

A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where 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. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.