United Services Automobile Association (usaa)

Victor Kwak

Ruthie D Lyle

Ryan Thomas Russell

Gregory Brian Meyer

Nolan Serrao

Mark Anthony Lopez

Christopher M Russell

Ravi Durairaj

Rachel Elizabeth Csabi

Zachery C Lake

Yogen Rai

Samantha Elizabeth Taylor

Hollie Ilene King

Nicholas C Wheeler

Seth E Ethington

Jesse Williams Plymale

William Daniel Farmer

Huihui Wu

Justin R Nash

Michael Lebowitz

Reynaldo Medina, Iii

Ashley Raine Philbrick

Andre Rene Buentello

Yevgeniy Viatcheslavovich Khmelev

Jose L Romero, Jr

Gregory David Hansen

Stacy Callaway Huggar

Emily Kathleen Krebs

Andrew L Anaruk

Pooja Krishnaswamy

Curtis Mark Bell

Rachel Ann Krebs

Jon Daniel McEachron

Jon D Mceachron

Pooja Krishnawamy

A call center data transfer system includes one or more processors that are configured to receive call data related to information provided by a caller during a first portion of a call with a first customer service representative. The one or more processors are also configured to assign an identifier to the call in response to receipt of an indication that the call should be transferred from the first customer service representative, associate the identifier with the call data in one or more databases, receive the identifier as an input during a second portion of the call with a second customer service representative, and use the identifier to retrieve the call data from the one or more databases. The one or more processors are further configured to display the call data via a display for visualization by the second customer service representative.

Call center data transfer system

A fraud detection system includes a source communication device associated with a source and a client device associated with a client to identify fraudulent calls to provide mitigating or remedial actions. The client device includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the client device to monitor a wireless communication between the client device and the source communication device to identify a keyword, a combination of keywords, a context, and/or a topic of the wireless communication. Moreover, the instructions cause the client device to identify fraudulent detection characteristics of the wireless communication based at least in part of the keyword, the combination of keywords, the context, and/or the topic, identify the wireless communication as fraudulent communication based on the fraudulent detection characteristics, and identify and implement a remedial action based at least in part on a threshold of the wireless communication.

Fraudulent call monitoring

A system and method for performing machine learning in a mobile computing device which is configured to be coupled with a cloud computing system is disclosed. The method may include activating, on the mobile computing device, a machine learning application, which accesses a local machine learning system including a local machine learning model, periodically updating the local machine learning system based upon updates for the local machine learning system received from a global machine learning system hosted by the cloud computing system, performing machine learning based on received training data, and periodically transmitting changes to the local machine learning system from the mobile computing device to the global machine learning system hosted by the cloud computing system.

Edge computing for machine learning

A system includes a client device including a processor, a telephony radio that supports telephony communications, and a data radio that supports data communications. The client device conducts, via the telephony radio, a call with a telephony service. Further the client device identifies a context of the call, identifies a subset of stored private data associated with the context of the call; and in parallel with the call, provides the subset of the stored private data, via a data radio, to a data service.

Parallel data provision

A computing device establishes communication with a financial institution communication device, and receives an instruction for a user to perform an action to authenticate the user. The action includes providing user-provided data indicative of a location. The computing device determines first location information based on the user-provided data. The computing device also determines second location information using a location sensor. The computing device authenticates the used based on determining that the first location information correlates to the second location information.

Systems and methods for fraud prevention

Systems and methods ingest extensive data regarding real-time transactions to determine correlation between a variety of parameters and possible fraud events. Possible fraud can be predicted, and notifications delivered to a fraud team. Correlations can be discovered using unsupervised machine learning, and particular systems and methods can utilize natural language processing to both receive and provide information concerning fraud risk and countermeasures.

Fraud detection using augmented analytics

Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Machine learning based call routing system

An electronic device receives operational data indicative of an operational characteristic of a vehicle from a sensor of the electronic device, a sensor of the vehicle, and/or images/videos captured by a camera. The electronic device determines that the vehicle has had a potential incident and a likelihood that the potential incident has actually occurred based on analysis of the operational data. The electronic device also receives risk management data associated with the vehicle from a database, and determines a severity level for the potential incident based on the operational data and the risk management data. The electronic device then sends a notification indicative of the potential incident based on the likelihood that the potential incident has actually occurred and the severity level for the potential incident to a third-party remote system (e.g., of a towing service, an emergency service, or both) to request assistance.

Systems and methods for integrated traffic incident detection and response

A conditions monitoring system may include a system controller. The controller may include a device processor and a non-transitory computer readable medium. The computer readable medium may include instructions executable by the device processor to perform the following steps: receiving positional data from a plurality of wearable devices of a plurality of associated wearers; making a determination of a possible dangerous environmental condition based on data received from the wearable device, including the positional data; applying one or more filters to eliminate false positive determinations of possible dangerous conditions; and providing instructions, based on the determination of a possible dangerous environmental condition, to the wearable device for the wearer to follow.

Environmental conditions monitoring system

An autonomous vehicle includes vehicle sensors that detect driving conditions and a communication interface communicatively coupled with a driving condition database via a communication network. The driving condition database stores vehicle sensor information indicative of first driving conditions and mobile device sensor information indicative of second driving conditions. Additionally, in response to a connection rating determination logic determining that a connection rating between the communication interface and the communication network below a threshold, a controller of the autonomous vehicle gathers, processes, and stores the vehicle sensor information and the mobile device sensor information for subsequent communication to the driving condition database and operates the autonomous vehicle based on the vehicle sensor information and the mobile device sensor information.

Systems and methods for detecting and transmitting driving condition information related to an autonomous vehicle

A system and method for improving the download time of emails in an environment in which a server distributes emails to persons working in close proximity to each other. When these persons receive multi-recipient emails intended for several or all of these persons, the server distributing the emails delivers the multi-recipient emails to the first one of the persons who logged on to read his or her emails on his or her personal computer, for distribution to the other persons over a personal area network. This reduces the download time for the persons downloading their emails at a subsequent time.

Method for distributing emails

A system and method for detecting an anomaly based on user interaction with a mobile computing device is disclosed. The method includes activating, on the mobile computing device, an application, which is configured to store the machine learning model and the behavioral profile generated by the machine learning model in the memory of the mobile computing device; receive, track, and store in the memory of the mobile computing device an input pattern including navigation information and identification information inputted from the user during the user interaction with the mobile computing device; detect anomaly based on the user interaction with the mobile computing device by comparing the stored behavioral profile with the stored input pattern; and prohibit user to have further access to the financial application including user accounts in response to detection of the anomaly.

Method and apparatus for anomaly detection for user behavior

A customer service system that includes a customer device associated with a customer, an agent device associated with an agent, and a monitoring system. The monitoring system includes a memory that stores instructions and a processor that executes the instructions. The instructions, when executed by the processor, cause the monitoring system to monitor a conversation between the customer device and the agent device to identify a context of the conversation. Moreover, the instructions cause the monitoring system to identify a disclosure notice based at least part on the context of the conversation, in which the disclosure notice comprises an indication of a disclosure to be read in light of the context of the conversation. Furthermore, the instructions cause the monitoring system to provide the disclosure notice to the agent, by rendering the disclosure notice in a graphical user interface (GUI) of the agent device, monitor and evaluate a disclosure provision of the disclosure notice from the agent to the customer, and provide disclosure provision feedback regarding evaluation of the disclosure provision to the agent.

Disclosure quality assurance

System and methods for pre-fetching data are provided. In one embodiment, an electronic device includes communications circuitry communicatively coupled to a data provider system over a network to obtain streaming data for completion of tasks. The electronic device also includes pre-fetch circuitry that identifies a dead zone in the network; calculates a pre-fetch data size amount based at least in part upon a size of the dead zone; and requests the pre-fetch data size amount of the data, to mitigate lost streaming ability in the dead zone.

Growing community of inventors

Frisco, TX, United States of America

Victor Kwak