Abstract: Arrangements for machine learning-based dynamic learning are provided. In some examples, audio data associated with a plurality of calls may be received and analyzed to identify a topic and sub-topic of each call and metadata of each call. Feedback data may also be received. A machine learning model may be executed by inputting, to the model, the identified topic and sub-topic and metadata of each call, and the feedback data, to output one or more topics or sub-topics of concern. A plurality of ongoing calls may be monitored to identify an ongoing call related to one of: a topic or sub-topic of concern. A plurality of agents who are not subject matter experts in the identified topic or sub-topic of concern and are available may be identified and joined, via respective computing devices, to the ongoing call in a dynamic learning session.

Abstract: Arrangements for intelligent call routing are provided. Audio data from a call between an agent device and a user device may be received and a topic of the call and metadata may be extracted from the audio data. The extracted topic and metadata may be compared to an alert signals database to output whether an alert will be attached to the call. Agent parameters associated with the agent who is part of the call may be identified. A machine learning model may be executed by inputting, to the model, the extracted topic, extracted metadata, the output of whether the alert will be attached to the call, and the agent parameters, to output a routing signal. Based on the routing signal, a second call communication session may be initiated between the agent computing device and a computing device of at least one of another agent or a supervisor.

Abstract: Systems and methods are disclosed for real time, frictionless channel switching to map and transpose a transaction on a preferred channel from an incoming channel. The systems and methods may use a machine learning model that processes features from a plurality of channels to determine the preferred channel. An omni channel processor is used to determine intended functionality data of the transaction for the preferred channel. The systems and methods may generate transposed intent data based on the intended functionality data of the transaction for the preferred channel. A deep link is generated and embedded with the transposed intent data and a channel access token for the transaction on the preferred channel. The deep link is configured to access and complete the requested transaction on the preferred channel.

Abstract: Systems and methods are disclosed for real time, frictionless channel switching to map and transpose a transaction on a preferred channel from an incoming channel. The systems and methods may use a machine learning model that processes features from a plurality of channels to determine the preferred channel. An omni channel processor is used to determine intended functionality data of the transaction for the preferred channel. The systems and methods may generate transposed intent data based on the intended functionality data of the transaction for the preferred channel. A deep link is generated and embedded with the transposed intent data and a channel access token for the transaction on the preferred channel. The deep link is configured to access and complete the requested transaction on the preferred channel.

Abstract: Systems and methods are disclosed for real time, frictionless channel switching to map and transpose a transaction on a preferred channel from an incoming channel. The systems and methods may use a machine learning model that processes features from a plurality of channels to determine the preferred channel. An omni channel processor is used to determine intended functionality data of the transaction for the preferred channel. The systems and methods may generate transposed intent data based on the intended functionality data of the transaction for the preferred channel. A deep link is generated and embedded with the transposed intent data and a channel access token for the transaction on the preferred channel. The deep link is configured to access and complete the requested transaction on the preferred channel.

Abstract: An apparatus may comprise a memory communicatively coupled to a processor. The processor may be configured to receive a request to perform multiple data processing operations and determine multiple sub-dialogues corresponding to the request. The sub-dialogues are part of a plurality of IVR operations. The processor may be further configured to route the request to a microservice of the microservices based at least in part upon the sub-dialogues. The microservice may be configured to fulfill the request. The processor may generate a response to the request associated with the microservice, update publishing commands in accordance with one or more rules and policies in response to generating the response, and publish the first microservice. The publishing commands associated with the microservice may be updated in isolation from the rest of the microservices.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies a particular data object that is unique to the original programming language. The system determines a counterpart data object that is used for a particular purpose as the identified data object in a target programming language. The system creates a second piece of code in the target programming language by replacing the particular data object with the counterpart data object. The system determines if the second piece of code is configured to perform a task that the first piece of code is programmed to perform. In response to determining that the second piece of code is configured to perform the particular task, the system executes the second piece of code.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies an expression syntax in the first piece of code and determines an interpretation of the expression syntax. The system creates a sentence that indicates an interpretation of a respective code line. The system determines a counterpart data object in a target programming language for each word in the created sentence. In response, the system creates a second piece of code in the target programming language, such that the second price of code satisfies syntax rules in the target programming language. In response to determining that the second piece of code is configured to perform a particular task that the first piece of code is configured to, the system executes the second piece of code.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies a particular data object that is unique to the original programming language. The system determines a counterpart data object that is used for a particular purpose as the identified data object in a target programming language. The system creates a second piece of code in the target programming language by replacing the particular data object with the counterpart data object. The system determines if the second piece of code is configured to perform a task that the first piece of code is programmed to perform. In response to determining that the second piece of code is configured to perform the particular task, the system executes the second piece of code.

Abstract: A system receives a set of input data streams from different data sources. At least one of the set of input data streams comprises a message that indicates an anomaly with respect to a topic associated with a software application. The system determines the topic, the anomaly, and a set of metadata associated with the topic from the set of input data streams. The set of metadata comprises an occurrence frequency of the anomaly in messages, a number of data sources from which messages are received, or a timeframe window within which the messages are received. The system assigns a set of weight values to the set of metadata and determines an accumulated weight value. If it is determined that the accumulated weight value is more than a threshold weight value, the system communicates an alert indicating to execute a countermeasure action that addresses the anomalous topic.

Abstract: A system determines content indicated on a website where a software application is accessible. The system receives a set of rules comprising a first rule that indicates a first generated response is to be published on one or more first platforms. The system generates a first text response according to the first rule and based on the determined content, an anomaly associated with a software application, and a countermeasure action that addresses or resolves the anomaly. The system determines whether the first generated text response corresponds to an expected response. In response to determining that the first generated text response corresponds to the expected response, the system publishes the first generated text response on the one or more first platforms.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies an expression syntax in the first piece of code and determines an interpretation of the expression syntax. The system creates a sentence that indicates an interpretation of a respective code line. The system determines a counterpart data object in a target programming language for each word in the created sentence. In response, the system creates a second piece of code in the target programming language, such that the second price of code satisfies syntax rules in the target programming language. In response to determining that the second piece of code is configured to perform a particular task that the first piece of code is configured to, the system executes the second piece of code.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies an expression syntax in the first piece of code and determines an interpretation of the expression syntax. The system creates a sentence that indicates an interpretation of a respective code line. The system determines a counterpart data object in a target programming language for each word in the created sentence. In response, the system creates a second piece of code in the target programming language, such that the second price of code satisfies syntax rules in the target programming language. In response to determining that the second piece of code is configured to perform a particular task that the first piece of code is configured to, the system executes the second piece of code.

Abstract: A system identifies data objects from a first piece of code in an original programming language and creates a knowledge graph that represents a relationship among the data objects. The system identifies a particular data object that is unique to the original programming language. The system determines a counterpart data object that is used for a particular purpose as the identified data object in a target programming language. The system creates a second piece of code in the target programming language by replacing the particular data object with the counterpart data object. The system determines if the second piece of code is configured to perform a task that the first piece of code is programmed to perform. In response to determining that the second piece of code is configured to perform the particular task, the system executes the second piece of code.

Abstract: A system is provided for user-initiated authentication of an electronic communication channel using a secure computing application token. In particular, the system may comprise a token generation application installed on an endpoint device of a user, where the application may be configured to perform authentication of communication channels based on receiving user input. Upon receiving a request to establish a secure communication channel from a remote device, the endpoint device may receive a voice sample from the user. Based on the attributes of the voice sample and of the application session, the endpoint device may generate an alphanumeric character string that may serve as a secure token to be verified against a second character string received from the remote device. If a match is detected, the endpoint device may authenticate and generate the communication channel.

Abstract: A control system, that includes a primary controller and various auxiliary controllers, is configured to facilitate a built-in self-test (BIST) of a system-on-chip (SoC). The primary controller is configured to initiate a BIST sequence associated with the SoC. Based on the BIST sequence initiation, each auxiliary controller is configured to schedule execution of various self-test operations on various functional circuits, various memories, and various logic circuits of the SoC by various functional BIST controllers, various memory BIST controllers, and various logic BIST controllers of the SoC, respectively. Based on the execution of the self-test operations, each auxiliary controller further generates various status bits with each status bit indicating whether at least one functional circuit, at least one memory, or at least one logic circuit is faulty. Based on the status bits generated by each auxiliary controller, a fault diagnosis of the SoC is initiated.

Abstract: A control system, that includes a primary controller and various auxiliary controllers, is configured to facilitate a built-in self-test (BIST) of a system-on-chip (SoC). The primary controller is configured to initiate a BIST sequence associated with the SoC. Based on the BIST sequence initiation, each auxiliary controller is configured to schedule execution of various self-test operations on various functional circuits, various memories, and various logic circuits of the SoC by various functional BIST controllers, various memory BIST controllers, and various logic BIST controllers of the SoC, respectively. Based on the execution of the self-test operations, each auxiliary controller further generates various status bits with each status bit indicating whether at least one functional circuit, at least one memory, or at least one logic circuit is faulty. Based on the status bits generated by each auxiliary controller, a fault diagnosis of the SoC is initiated.

Abstract: An error management system can include register sets associated with an error reaction. The test errors are injected in functional signals based on activation of multiple bits in one of the register sets. When the functional signals with the injected test errors are received by the error management system, multiple bits in the other register set are activated. The error management system generates an activated indication signal when a number of the activated bits in one register set matches a number of activated bits in the other register set. When the indication signal is activated, the error management system generates a reaction signal indicative of the error reaction. Thus, the error management system generates a single reaction signal in response to the injected test errors requiring the same reaction.

Abstract: Embodiments of the present invention provide a system for automatically identifying and mapping interaction attributes to generate custom assessment functionality. The system is configured for identifying one or more interaction channels associated with an entity, gathering flow sets associated with the one or more interaction channels, identifying one or more segments associated with the one or more interaction channels based on the flow sets, mapping one or more attributes with the one or more segments, and generating an assessment functionality based on mapping the one or more attributes with the one or more segments.

Abstract: Aspects of the disclosure relate to computing systems for receiving user requests through channels of a webpage and performing computational functions corresponding to the user requests. In one embodiment, a computing platform may detect a change corresponding to input parameters for performing a user request through a first representation of a webpage. The computing platform may update, based on the detected change corresponding to the input parameters, a natural language processing application associated with a second representation of the webpage. The computing platform may generate, based on the updated natural language processing application associated with the second representation of the webpage, a logical table including one or more questions to ask a user through the second representation of the webpage in response to one or more user inputs.