Sebastian Brinkmann

US Patent:

20190333078, Oct 31, 2019

Filed:

Jan 16, 2018

Appl. No.:

16/470212

Inventors:

- San Francisco CA, US
Hendrick Bartel - San Francisco CA, US
Sebastian Brinkmann - San Francisco CA, US
Philip Kim - San Francisco CA, US
James P. Hawley - Oakland CA, US
Yang Ruan - Oakland CA, US
Eli Reisman - Berkeley CA, US
Mark Strehlow - San Francisco CA, US
Faithlyn Tulloch - San Francisco CA, US

International Classification:

G06Q 30/02
G06N 20/00

Abstract:

Methods and systems of assessing aggregate sentiment over a plurality of time increments of a time period are provided. A maximum aggregation factor that is associated with a particular time period is assigned. A plurality of time increments over the time period are received. For each time increment, the BISV is subtracted from the ISV to form a BISV/ISV difference value. The BISV/ISV difference value is normalized by dividing by the maximum possible difference, thereby determining a modulator. For each time increment, a value is assigned to a recency of the particular time increment to a most recent incremental sentiment value update event, thereby determining a decay factor. The maximum aggregation factor associated with a particular time period is modulated by multiplying a determined modulator and a determined decay factor associated with each time increment within the evaluated time interval. The modulated maximum aggregation factor is applied to aggregated sentiment values, thereby determining an aggregate sentiment value for each time increment over the time period.

US Patent:

20190205341, Jul 4, 2019

Filed:

Aug 29, 2017

Appl. No.:

16/328673

Inventors:

- San Francisco CA, US
Sebastian Brinkmann - San Francisco CA, US
Hendrik Bartel - San Francisco CA, US
James P. Hawley - Oakland CA, US
Phillip Kim - San Fransisco CA, US
Yang Ruan - Oakland CA, US
Mark Strehlow - San Fransisco CA, US
Faithlyn A. Tulloch - San Fransisco CA, US

International Classification:

G06F 16/906
G06F 16/9536
G06F 16/9537
G06F 16/908

Abstract:

Systems and methods are provided for identifying meta-events. A plurality of event items are received over a given period of time. The plurality of event items are analyzed to determine one or more areas of interests. One or more characteristics of the plurality of events items is measured. The measured number of event items within the particular area of interest within the given time period are compared against a measured number of even items within the particular area of interest within a previous time period. It is determined that a meta-event has occurred when the difference between the measured number of event items within the particular area of interest compared to the measured number of items within the particular area of interest within a previous time period exceeds a threshold measure of event items.