Cdw Corporation

Joseph Kessler

Andre Coetzee

Dan Verdeyen

Suresh Bellam

Gregory Tomezak

Yuqing Chen

Rajat Swaroop

Yuxi He

Sunrito Bhattacharya

A system includes a processor, and a memory containing instructions that, when executed, cause the voice control hub computing system to receive a request; receive and transmit an utterance; convert the utterance to a string; analyze the string using a machine learning model trained using a command interpreter; and receive and dispatch the intent and the entity. A method includes receiving a request; receiving and transmitting an utterance; converting the utterance to a string; analyzing the string using a machine learning model trained using a command interpreter; and receiving and dispatching the intent and the entity. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: receive a request; receive and transmit an utterance; convert the utterance to a string; analyze the string using a machine learning model trained using a command interpreter; and receive and dispatch the intent and the entity.

Voice control hub methods and systems

A computing system includes a processor; an input device; and a memory containing instructions that, when executed, cause the computing system to receive a data description corresponding to a graphical user interface control; identify the graphical user interface control using a trained convolutional neural network; and display an action palette. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive a data description corresponding to a graphical user interface control; identify the graphical user interface control using a trained convolutional neural network; and display an action palette. A computer-implemented method includes receiving a data description corresponding to a graphical user interface control; identifying the graphical user interface control using a trained convolutional neural network; and displaying an action palette.

Visual programming methods and systems for intent dispatch

A computing system for enabling a user to control a legacy application of an enterprise using voice commands includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to receive a user utterance; generate an output by analyzing the utterance using a speech-to-text application programming interface; and perform an action with respect to an element of the legacy application. A computer-implemented method includes receiving a user utterance; generating an output by analyzing the utterance using a speech-to-text application programming interface; and performing an action with respect to an element of the legacy application. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to receive a user utterance; generate an output by analyzing the utterance using a speech-to-text application programming interface; and perform an action with respect to an element of the legacy application.

Automated graphical user interface control methods and systems using voice commands

A selective database rollback method includes identifying a table, identifying a root key, storing a target rollback date, retrieving schema information including keys, ordering the table, iterating over the table, storing the current table state, obtaining a transaction journal, and reverse-applying the transaction journal to generate an output file. A method or system for identifying database key includes iterating over a table's columns and rows, generating similarity metrics by comparing column-wise and/or row-wise data, and comparing the generated similarity metrics to a threshold value. A server includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the server to identify a table, identify a root key, store a target rollback date, retrieve schema information including keys, order the table, iterate over the table, store the current table state, obtain a transaction journal, and reverse-apply the transaction journal to generate an output file.

Selective database data rollback

A method includes capturing an event, analyzing the event to generate graphs, receiving a natural language utterance, identifying an entity and a command, modifying the graphs; and emitting an application prototype. An application prototyping server includes a processor; and a memory storing instructions that, when executed by the processor, cause the server to capture an event, analyze the captured event to generate graphs, receive a natural language utterance, identify an entity and a command, modify the graphs; and emit an application prototype. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: capture an event, analyze the captured event to generate graphs, receive a natural language utterance, identify an entity and a command, modify the graphs; and emit an application prototype.

Voice-driven application prototyping using machine-learning techniques

A selective database rollback method includes identifying a table, identifying a root key, storing a target rollback date, retrieving schema information including keys, ordering the table, iterating over the table, storing the current table state, obtaining a transaction journal, and reverse-applying the transaction journal to generate an output file. A method for identifying database key includes iterating over a table's columns and rows, generating similarity metrics by comparing column-wise and/or row-wise data, and comparing the generated similarity metrics to a threshold value. A server includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the server to identify a table, identify a root key, store a target rollback date, retrieve schema information including keys, order the table, iterate over the table, store the current table state, obtain a transaction journal, and reverse-apply the transaction journal to generate an output file.

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 system includes a processor, an input device, and a memory storing instructions that, when executed, cause the system to display a palette and receive a user selection of one or more actions, and store the selected actions in association with the palette in an electronic database. In another aspect, a non-transitory computer readable medium includes program instructions that when executed, cause a computer to display, on a graphical user interface, a palette, receive, via the input device, a user selection of one or more actions, and store the selected one or more actions in association with the palette in a database. A method includes displaying a palette and receiving, via the input device, a user selection of one or more actions, and storing the selected one or more actions in association with the palette in an electronic database.

A system includes a processor and a memory storing instructions that, when executed by the processor, cause the system to receive an utterance, transmit the utterance to a cloud to generate an intent and an entity, receive the intent and the entity, and perform an action with respect to a graphical user interface. A method includes receiving an utterance, transmitting the utterance to a cloud to generate an intent and an entity, receiving the intent and the entity, and performing an action with respect to a graphical user interface. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to receive an utterance, transmit the utterance to a cloud to generate an intent and an entity, receive the intent and the entity, and perform an action with respect to a graphical user interface.

A method includes receiving a message, enqueueing the message, dequeueing the message by a fraud detection service, analyzing the message using a trained machine learning model, analyzing an isolated domain name, storing the message, and causing a mitigation action. A computing system includes a transceiver, a processor, a memory storing instructions that when executed by the processor cause the system to receive a message, enqueue the message, dequeue the message, analyze the message using a machine learning model, analyze an isolated domain name, store the message, and cause a mitigation action. A non-transitory computer readable medium contains instructions that when executed, cause a computer to receive a message, enqueue the message, dequeue the message by a fraud detection service, analyze the message using a machine learning model, analyze an isolated domain name, store the message, and cause a mitigation action.

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Grayslake, IL, United States of America

Joseph Kessler