Cynthia Freeman

US Patent:

20220351099, Nov 3, 2022

Filed:

May 16, 2022

Appl. No.:

17/745422

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Spokane Valley WA, US
James DelloStritto - Reston VA, US

International Classification:

G06Q 10/06
G06F 17/18
G06Q 30/00
H04M 15/00

Abstract:

Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

US Patent:

20230004891, Jan 5, 2023

Filed:

Sep 7, 2022

Appl. No.:

17/939632

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Spokane Valley WA, US
James DelloStritto - Reston VA, US

International Classification:

G06Q 10/06
G06Q 30/00
H04M 15/00
G06F 17/18

Abstract:

Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.

US Patent:

20210358478, Nov 18, 2021

Filed:

Jul 30, 2021

Appl. No.:

17/389839

Inventors:

- Alpharetta GA, US
Ian Roy Beaver - Spokane WA, US
Cynthia Freeman - Albuquerque NM, US
Jonathan Patrick Merriman - Spokane WA, US

International Classification:

G10L 15/06
G10L 15/01
G10L 15/22
G06F 40/35

Abstract:

A method of determining influence of language elements in script to an overall classification of the script by perturbing the dataset representing a conversation. In some instances, for example, in the conversation, the language elements and turns within the conversation (e.g., in a chat bot) are analyzed for their influence in escalation or non-escalation of the conversation to a higher level of resolution, e.g., to a human representative or manager.

US Patent:

20200401768, Dec 24, 2020

Filed:

Jun 17, 2020

Appl. No.:

16/903916

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Albuquerque NM, US

International Classification:

G06F 40/47
G06F 40/279

Abstract:

In an implementation, a method for detecting anomalies in textual items is provided. The method includes: receiving a first plurality of textual items by a computing device; training a language model using the received first plurality of textual items by the computing device; after training the language model, receiving a second plurality of textual items by the computing device; calculating a cross-entropy for each textual item in the second plurality of textual items by the computing device using the language model; and detecting an anomaly in at least one of the textual items of the second plurality of textual items by the computing device using the calculated cross-entropies.

US Patent:

20200401940, Dec 24, 2020

Filed:

Jun 10, 2020

Appl. No.:

16/897400

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Spokane Valley WA, US

International Classification:

G06N 20/00
G06F 17/18

Abstract:

Systems and methods are provided framework for automatically choosing the appropriate generalized linear model (GLM) given a time series of count data, and for anomaly detection on time series data. A dispersion parameter is determined and used to determine whether the count data is overdispersed data or underdispersed data. The overdispersed data or the underdispersed data is used to determine a GLM to apply on the dataset. Using the determined GLM on the data, anomalies can be determined.

US Patent:

20200286469, Sep 10, 2020

Filed:

Mar 5, 2020

Appl. No.:

16/810310

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Spokane WA, US

International Classification:

G10L 15/183
G10L 15/06
G06F 40/20
G06N 20/00

Abstract:

A framework for reducing the number of textual items reviewed to determine the source of or reason for an anomaly in a time series that is used to track metrics in textual data is provided. According the framework, textual items in a time window corresponding to the anomaly are ranked according to the cross-entropy as determined by applying a language model to the relevant textual items and ranking textual items that most likely triggered an anomaly in time series data based on the cross-entropy value. In an aspect, a predetermined number of textual items having the highest cross-entropy are provided or all textual items having cross-entropy value higher than predetermine threshold are provided.

US Patent:

20200210393, Jul 2, 2020

Filed:

Sep 13, 2019

Appl. No.:

16/569984

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Spokane Valley WA, US
Jonathan Merriman - Spokane Valley WA, US

International Classification:

G06F 16/215
G06F 16/2458

Abstract:

Disclosed are a framework and method for selecting an anomaly detection method for each of a plurality of class of time series based on characteristics a time series example that represents an expected form of data. The method provides classification of a given time series into one of known classes based on expected properties of the time series, filtering the set of possible detection methods based on the time series class, evaluating the remaining detection methods on the given time series using the specific evaluation metric and selecting and returning a recommended anomaly detection method based on the specific evaluation metric.

US Patent:

20200110805, Apr 9, 2020

Filed:

Aug 21, 2019

Appl. No.:

16/546941

Inventors:

- Alpharetta GA, US
Cynthia Freeman - Albuquerque NM, US
Andrew T. Pham - Modesto CA, US

International Classification:

G06F 17/27
G06F 3/0482
G06F 3/0481
H04L 29/08
H04L 29/06

Abstract:

Features, libraries, and techniques are provided herein for determining the kinds of relational language that are present. Applying audio, emojis, and sentiment shifts as features may be used to determine whether the customer is providing backstory, whether there is ranting, etc. Textual features may be considered, as well as audio features may be considered.