Daniel Fernandez

20220374454, Nov 24, 2022

Jul 15, 2022

17/812984

- 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

G06F 16/28
G06K 9/62

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.

20200380001, Dec 3, 2020

Aug 14, 2020

16/994276

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

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

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.

20190213191, Jul 11, 2019

Mar 18, 2019

16/357108

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

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

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.

20190079937, Mar 14, 2019

Nov 13, 2018

16/189040

- 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

G06F 17/30

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.

20180052891, Feb 22, 2018

Aug 9, 2017

15/673231

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

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

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.

20170052958, Feb 23, 2017

Aug 10, 2016

15/233149

- 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

G06F 17/30

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.