Michelle R Archuleta

20220293123, Sep 15, 2022

Mar 10, 2022

17/692070

- Greenwood Village CO, US
Michelle Archuleta - Lakewood CO, US
Morgan Cox - Lakewood CO, US
Mark Fogerty - Seneca SC, US
Robert Scordia - Ridgewood NY, US
Michael V. Bivins - Orlando FL, US
Allison A. Sakara - Lake Wales FL, US

Covid Cough, Inc. - Greenwood Village CO

G10L 25/66
G10L 25/27
G10L 25/72

Systems and methods of the present disclosure enable authentication and/or anomaly detection using machine learning-based modelling. Audio recordings that represent audio from a forced cough vocalizations are received from a user device. One or more audio filters extract forced cough vocalization recordings from the audio recordings and signal data signatures representative of the forced cough vocalization recordings are generated. Gaussian mixture models are produced for each unique combination of the signal data signatures, where each unique combination include a group of model baselines and a test match baseline. Each Gaussian mixture model is used to produce a match value for the associated test match baseline based on the associated model baselines, and a statistical score is determined for each match value. One or more baseline Gaussian mixture models are determined based on the statistical score and stored in a user profile.

20220215248, Jul 7, 2022

Jan 4, 2022

17/568539

- Greenwood Village CO, US
Mark Fogarty - Seneca SC, US
Michael V. Bivins - Orlando FL, US
Robert Durham - Jacksonville FL, US
Allison A. Sakara - Lake Wales FL, US
Mona Kelley - Springfield TN, US
Karl Kelley - Springfield TN, US
Morgan Cox - Lakewood CO, US
Nolan Donaldson - Denver CO, US
Adam Stogsdil - Cypress TX, US
Simon Kotchou - Phoenix AZ, US
Robert F. Scordia - Brooklyn NY, US
Kitty Kolding - Parker CO, US
Anne Humpich - Downers Grove IL, US
Michelle Archuleta - Lakewood CO, US

G06N 3/08

Systems and methods of the present disclosure enable signal data signature detection using a memory unit and processor, where the memory using stores a computer program or computer programs created by the physical interface on a temporary basis. The computer program, when executed, cause the processor to perform steps to receive a signal data signature recording from at least one data source, receive a dataset of labeled signal data signature recordings including signal data signature recording labels, identify, using at least one machine learning model, boundaries within the dataset of labeled signal data signature recordings, classify the signal data signature recording to produce an output label using a compendium of signal data signature classifiers based on the boundaries within the dataset of labeled signal data signature recordings, determine an output type of the signal data signature recording, and display the output label on a display media.

20210357586, Nov 18, 2021

Sep 4, 2019

17/273600

- Greenwood Village CO, US
Michelle Archuleta - Lakewood CO, US

G06F 40/253
G06F 40/30
G06N 3/08
G06F 40/166
G06F 40/205

Methods, systems, and apparatus, including computer programs language encoded on a computer storage medium for a language modification system whereby input jargon language is modified to plain language using a reinforcement learning system with a real-time reward grammar engine. The actions of an agent are limited by three different methods: an operational window that defines the grammatical boundary or states that an agent can perform actions within an environment, state groups that specify that actions must be performed to all states belonging to a state group, and the length of the environment or input sentence. The reinforcement learning agent learns a policy of edits and modifications to a sentence such that the output sentence is grammatical and retains the intended meaning.

20210326713, Oct 21, 2021

Sep 24, 2019

17/277314

Michelle N Archuleta - Lakewood CO, US

G06N 5/00
G06F 40/35
G06F 40/247
G06N 3/08

Methods, systems, and apparatus, including computer programs language encoded on a computer storage medium for a word-to-logic system whereby input text is used to extract the symmetry of word relationships, quantify symmetry, and negate symmetrical relationships into logical equations evaluate logical equation using an automated theorem prover and return the logical state of the input text. A real-time logic engine utilizes the derived logical equations as a set of ‘a priori’ assumptions such that a user can query the system and receive an output that indicates the logical state of the query.