Daniel Fernandez

US Patent:

20220374454, Nov 24, 2022

Filed:

Jul 15, 2022

Appl. No.:

17/812984

Inventors:

- Palo Alto CA, US
Rahul Mehta - New York NY, US
Daniel Erenrich - Mountain View CA, US
Guillem Palou Visa - London, GB
Roger Hu - New York NY, US
Xavier Falco - London, GB
Rowan Gilmore - London, GB
Eli Bingham - New York NY, US
Jason Prestinario - New York NY, US
Yifei Huang - Jersey City NJ, US
Daniel Fernandez - New York NY, US
Jeremy Elser - New York NY, US
Clayton Sader - San Francisco CA, US
Rahul Agarwal - San Francisco CA, US
Matthew Elkherj - Menlo Park CA, US
Nicholas Latourette - San Francisco CA, US
Aleksandr Zamoshchin - Aurora CO, US

International Classification:

G06F 16/28
G06K 9/62

Abstract:

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

US Patent:

20200380001, Dec 3, 2020

Filed:

Aug 14, 2020

Appl. No.:

16/994276

Inventors:

- Palo Alto CA, US
Daniel Fernandez - New York NY, US

International Classification:

G06F 16/2455
G06F 9/30
G06F 8/38
G06F 8/30
G06F 8/33
G06F 8/20

Abstract:

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

US Patent:

20190213191, Jul 11, 2019

Filed:

Mar 18, 2019

Appl. No.:

16/357108

Inventors:

- Palo Alto CA, US
Daniel Fernandez - New York NY, US

International Classification:

G06F 16/2455
G06F 8/30
G06F 8/38
G06F 9/30

Abstract:

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

US Patent:

20190079937, Mar 14, 2019

Filed:

Nov 13, 2018

Appl. No.:

16/189040

Inventors:

- Palo Alto CA, US
Rahul Mehta - New York NY, US
Daniel Erenrich - Mountain View CA, US
Guillem Palou Visa - London, GB
Roger Hu - New York NY, US
Xavier Falco - London, GB
Rowan Gilmore - London, GB
Eli Bingham - New York NY, US
Jason Prestinario - New York NY, US
Yifei Huang - Jersey City NJ, US
Daniel Fernandez - New York NY, US
Jeremy Elser - New York NY, US
Clayton Sader - San Francisco CA, US
Rahul Agarwal - San Francisco CA, US
Matthew Elkherj - Menlo Park CA, US
Nicholas Latourette - San Francisco CA, US
Aleksandr Zamoshchin - Aurora CO, US

International Classification:

G06F 17/30

Abstract:

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

US Patent:

20180052891, Feb 22, 2018

Filed:

Aug 9, 2017

Appl. No.:

15/673231

Inventors:

- PALO ALTO CA, US
DANIEL FERNANDEZ - NEW YORK NY, US

International Classification:

G06F 17/30
G06F 9/30
G06F 9/44

Abstract:

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

US Patent:

20170052958, Feb 23, 2017

Filed:

Aug 10, 2016

Appl. No.:

15/233149

Inventors:

- Palo Alto CA, US
Rahul Mehta - New York NY, US
Daniel Erenrich - Mountain View CA, US
Guillem Palou Visa - London, GB
Roger Hu - New York NY, US
Xavier Falco - London, GB
Rowan Gilmore - London, GB
Eli Bingham - New York NY, US
Jason Prestinario - New York NY, US
Yifei Huang - Jersey City NJ, US
Daniel Fernandez - New York NY, US
Jeremy Elser - New York NY, US
Clayton Sader - San Francisco CA, US
Rahul Agarwal - San Francisco CA, US
Matthew Elkherj - Menlo Park CA, US
Nicholas Latourette - San Francisco CA, US
Aleksandr Zamoshchin - Aurora CO, US

International Classification:

G06F 17/30

Abstract:

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.