Jorge E Vasquez, Age 72Alexandria, VA

2018036, Dec 20, 2018

Jun 16, 2017

15/624919

- Reston VA, US
Jorge Luis VASQUEZ - Fairfax VA, US

MetroStar Systems, Inc. - Reston VA

G06Q 10/06
G06F 11/34
G06F 11/30
G06F 11/32

Embodiments described herein are directed systems and methods that include the actions of receiving sensor data from a sensor configured to monitor a resource and analyzing the sensor data to identify one or more determined activities of the resource. A determined activity is selected, based on criteria, for a collection of additional information, and a query is generated for the collection of the additional information to reduce an error potential of the determined activity. The query is transmitted and results are received. Based on the results, the determined activity is updated, then stored with a timestamp as an activity data object in an activity data store. A record is generated that comprises the determined activity.

2018026, Sep 13, 2018

May 9, 2018

15/975746

- Reston VA, US
John William Seaman - Reston VA, US
Ali Reza MANOUCHEHRI - Reston VA, US
Jorge Luis VASQUEZ - Fairfax VA, US
Lee Evan KOHN - Arlington VA, US

Zoomph, Inc. - Reston VA

G06N 7/00
G06N 5/02

An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Naïve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group.

2018012, May 10, 2018

Sep 15, 2017

15/705820

- Reston VA, US
John William Seaman - Reston VA, US
Jorge Luis Vasquez - Fairfax VA, US
Reza Ali Manouchehri - Reston VA, US
Lee Evan Kohn - Arlington VA, US

Zoomph, Inc. - Reston VA

G06N 7/00
G06N 5/02

A system includes a processor and a memory device communicatively coupled to the processor. The system also includes a database communicatively coupled to the processor. The database is configured to store a first plurality of prediction sets. Each prediction set is associated with a respective individual within a first population. Each prediction set comprises a plurality of prediction results, and each prediction result corresponds to a selected one of a plurality of features. The processor is configured to receive a request for a distribution value associated with a selected feature and one or more parameters. The distribution value indicate how often the selected feature appears in a second population of individuals, the second population being defined by the one or more parameters.

2017030, Oct 19, 2017

May 12, 2017

15/594526

- Reston VA, US
John William Seaman - Reston VA, US
Ali Reza MANOUCHEHRI - Reston VA, US
Jorge Luis VASQUEZ - Fairfax VA, US
Lee Evan KOHN - Arlington VA, US

Zoomph, Inc. - Reston VA

G06N 7/00
G06N 5/02

An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Naïve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group. For example, an advertisement may be selected and displayed based on the predicted feature.

2017016, Jun 15, 2017

Nov 9, 2016

15/347777

- Reston VA, US
John William Seaman - Reston VA, US
Ali Reza MANOUCHEHRI - Reston VA, US
Jorge Luis VASQUEZ - Fairfax VA, US
Lee Evan KOHN - Arlington VA, US

G06N 5/02
G06N 7/00

An individual having a plurality of first features and a second characteristic is identified. A plurality of second features associated with a second characteristic is determined. For each first feature among the plurality of first features, a respective probability distribution indicating, for each respective second feature, a probability that a person having the respective second feature has the first feature, is determined, thereby generating a plurality of probability distributions. A probabilistic classifier is used to combine the plurality of probability distributions, thereby generating a merged probability distribution. A Monte Carlo method is used to generate a prediction set based on the merged probability distribution, the prediction set including a plurality of prediction values for the second characteristic of the individual, each respective prediction value being associated with one of the plurality of second features. The prediction set is stored in a memory. The probabilistic classifier may include a Naïve-Bayes method. Prediction sets may be generated for each of a plurality of individuals, and used to predict a feature associated with a group. For example, an advertisement may be selected and displayed based on the predicted feature.

2017006, Mar 2, 2017

Aug 28, 2015

14/839607

- Reston VA, US
Jorge Luis Vasquez - Fairfax VA, US

G06Q 30/02
G06Q 50/00
G06F 17/30

A system includes a plurality of large screen display devices located at a venue, and a storage adapted to store one or more items of social media content having respective content tags. The system also includes one or more processors adapted to select a plurality of items of social media content having content tags matching one or more predetermined criteria, cause first items selected from the plurality of items of social media content to be displayed, during a live event occurring at the venue, on each of the plurality of large screen display devices, select a second item having a particular content tag from the first plurality of items of social media content, identify an advertisement image having an advertisement campaign tag that matches the particular content tag of the second item, and cause the second item and the advertisement image to be displayed, at a selected time during the live event, simultaneously on each of the plurality of large screen display devices.

2014024, Aug 28, 2014

Mar 13, 2013

13/801564

- Reston VA, US
Theresa Laureen Kattula - Fairfax VA, US
Jorge Luis Vasquez - Fairfax VA, US

MetroStar Systems, Inc. - Reston VA

G06Q 30/02
G06Q 50/00

705 1453

Methods, systems, and apparatus are disclosed which include filtering social media data for content resources that match one or more search terms, generating a feed based on the filtering, associating a content resource tag with each content resource, associating the feed with a first section of a layout of a content display, associating an advertisement campaign with a second section of the layout, providing the layout to be displayed on a display device, providing the content resources from the feed to be displayed in the first section of the layout, determining that one of the content resources currently displayed in the first section of the layout is associated with a content resource tag that matches one of the campaign tags of the advertisement campaign; and providing the advertisement image to be displayed in the second section of the layout.