Techwell, Inc.

Cdw Corporation

Zenith Electronics Corporation

Gregory Tomezak

Mark J Fimoff

Jin Hong Kim

Yuqing Chen

Rajat Swaroop

Yuxi He

Sunrito Bhattacharya

Tyler A Brown

Sreenivasa M Nerayanuru

Joseph Kessler

Dan Verdeyen

Andre Coetzee

Suresh Bellam

A method for machine learning-based account manager virtual assistant staging includes receiving a message and a classification, generating a staging record, generating a status using staging rules, generating an order when the message classification is order and the status is complete, and transmitting the order. An account manager virtual assistant staging system includes a processor and a memory storing instructions that cause the system to receive a message and a classification, generate a staging record, generate a status using staging rules, generate an order when the message classification is order and the status is complete, and transmit the order. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to receive a message and a classification, generate a staging record, generate a status using staging rules, generate an order when the message classification is order and the status is complete, and transmit the order.

Account manager virtual assistant staging using machine learning techniques

A method for machine learning-based account manager virtual assistant message processing includes receiving a message of a user, preprocessing the message to generate a normalized data set, analyzing the normalized data using a classification machine learning model, analyzing the normalized data using a deep learning machine learning model, generating a response based on the output of the first and second machine learning models, and transmitting the response to the user. An account manager virtual assistant computing system includes a processor and a memory storing instructions that cause the account manager virtual assistant system to receive a message from a user, process the message using a first machine learning model, process the message using a second machine learning model, when the message is a request for quote, generate a response and transmit the response to the user.

Account manager virtual assistant using machine learning techniques

A method and system for determining a step size of an adaptive equalizer for a digital data receiver. The data received by the receiver includes coded symbols and uncoded symbols. The method includes determining a first error estimate based on decoded symbols corresponding to the coded symbols, determining a second error estimate based on the uncoded symbols, adaptively selecting the first error estimate or the second error estimate based on a convergence criterion, and determining a step size based on the selected error estimate.

Method for determining the step size for an LMS adaptive equalizer for 8VSB

Embodiments of the invention relate to systems and methods for tracking phase in a receiver which uses multiple phase tracking techniques. A phase tracking module generates a plurality of symbol decisions related to received 8-level-vestigial-sideband ('8VSB') signals, determines a phase tracking threshold value based on a mean square error, receives an estimated imaginary component of a transmitted signal based on the symbol decisions, and determines a phase estimate based on the imaginary component of the transmitted signal and the plurality of symbol decisions. The phase tracking module selects one of a first phase tracking technique and a second phase tracking technique based on the phase estimate. The first phase tracking technique is selected when the phase estimate is greater than the phase tracking threshold value, and the second phase tracking technique is selected when the phase estimate is less than the phase tracking threshold value.

Method and system for tracking phase in a receiver for 8VSB

An output of an equalizer is decoded to produce first decoded data having a first accuracy. The output of the equalizer is further decoded to produce second decoded data having a second accuracy, and the second accuracy is greater than the first accuracy. Tap weights for the equalizer are calculated using the first and second decoded data. The calculated tap weights are applied to the taps of the equalizer.

CIR estimating decision feedback equalizer with phase tracker

A method and system for channel tracking in an adaptive equalizer for a digital data receiver. The method includes determining a first set of tap weights and a second set of tap weights of the equalizer, and determining a difference between the first and second sets of tap weights. The method also includes determining an error estimate based on symbols received at the receiver, comparing the error estimate with a divergence threshold, and determining a step size factor based on the difference and the comparison.

Growing community of inventors

Buffalo Grove, IL, United States of America

Gregory Tomezak