Abstract: A system receives an input from a user, where the input indicates a request to perform one or more tasks. The system generates a plurality of utterances from the input. Each utterance indicates a different operation. The system determines a sequence of the operations to be performed in order to perform the one or more tasks. The system identifies a conversational software algorithm that is configured to process a respective utterance based on the topic of the utterance. The system communicates each utterance to the respective conversational software algorithm. The system receives responses from the conversational software algorithms. The system determines whether each response is valid. The system generates a final response based on the valid responses. The system outputs the final response.

Abstract: A system receives an input from a user, where the input indicates a request to perform one or more tasks. The system generates a plurality of utterances from the input. Each utterance indicates a different operation. The system determines a sequence of the operations to be performed in order to perform the one or more tasks. The system identifies a conversational software algorithm that is configured to process a respective utterance based on the topic of the utterance. The system communicates each utterance to the respective conversational software algorithm. The system receives responses from the conversational software algorithms. The system determines whether each response is valid. The system generates a final response based on the valid responses. The system outputs the final response.

Abstract: A method includes monitoring an interaction of a user with a metaverse session. A notification is sent to a first developer that the user is facing an issue while interacting with the metaverse session. The first developer is allowed access to the metaverse session at a location of the issue using a first developer avatar. One or more error flags are generated within a field of view of the first developer avatar in the metaverse session. Each error flag includes a respective error code. In response to determining that a first error code of a first error flag matches a first stored error code, a first solution is determined. The first solution includes a first stored solution corresponding to the first stored error code. In response to determining that the first solution solves the issue of the user, the first solution is deployed to the metaverse session.

Abstract: Methods for depositing a film on a surface of substrate by cyclical deposition methods including pulsed purge processed are disclosed. The pulsed purge processes include introducing a purge gas into a reaction chamber at a first flow rate, and introducing a purge gas into the reaction chamber at second flow rate, the first flow rate being different to the second flow rate.

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 for generating an expandable pool of virtual agents to provide solutions in a virtual environment comprises a call center server in communication with a metaverse server. The call center server generates the virtual agents with corresponding skills of each real-world agent using a machine learning model based on historical observation operations performed by each particular real-world agent and a historical rule set to address the service issue. The metaverse server identifies the virtual agents in the virtual environment. The call center server routes a service inquiry with a service issue from a user equipment of a user to the metaverse server when determining that a particular real-world agent is not available. The metaverse server identifies a first virtual agent with a first skill and conducts a first interaction between the first virtual agent and a user avatar to generate the customized solution to address the service issue.

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 includes a memory and a processor coupled to the memory. The processor receives a first user credential associated with a first user and authorizes an avatar of the first user to enter a virtual environment. The processor receives a first selection of an experience theme and a second selection of at least one product type from the selected experience theme. The processor determines a first data file that provides a first benefit count to the first user when a first amount of data objects is transferred from a first data file of the first user to a second data file. The processor indicates to the first user that the first data file provides the first benefit count to the first user, receives a command to obtain the first data file, and initiates a registration process to register the first user to obtain the first data file.

Abstract: A communication assist service may extract one or more video frames during a communication session between a user device and a terminal device. The video frames may include a representation of an object associated with an issue for which the communication session was established. The communication assist service may generate a feature vector from the video frames and execute a trained neural network configured to generate predictions associated with a resolution to the issue. The neural network may output predicted actions that if executed may resolve the issue or provide additional information that will improve a likelihood of resolving the issue. The communication assist service may then transmit the predicted actions to the terminal device in real time.

Abstract: A communication assist service may extract one or more video frames during a communication session between a user device and a terminal device. The video frames may include a representation of a gesture provided by a user of the user device. The communication assist service may generate a feature vector from the video frames and execute a trained neural network configured to generate classify the semantic meaning of gesture. The neural network may output the semantic meaning of the gesture, which may be used to generate an appropriate communication response to the gesture. The communication assist service may facilitate transmission of the communication response to a device of the new communication session in real time.

Abstract: Embodiments of the present invention provide a system for intelligent prediction and implementation of dynamically changing requirements relating to incoming network requests. The system is configured for determining that the entity system associated with an entity received a user request from a user, establishing a secure communication link with the entity system, extracting, via a requirement evaluator, through the secure communication link, at least a part of the user request that requires a requirement evaluation, predicting, via a requirement predictor, one or more requirements that are associated with the part of the user request, evaluating the one or more requirements and cataloged requirements stored in a requirement catalog, determining a real-time deviation between the one or more requirements and the cataloged requirements, and providing a solution for the real-time deviation.

Abstract: Methods, systems, and assemblies suitable for gas-phase processes are disclosed. An exemplary assembly includes a susceptor plate and a susceptor attachment. The susceptor attachment can comprise a ramp region and a conductance control region above and exterior to the ramp region. Methods, systems, and assemblies can be used to obtain desired (e.g., composition and/or thickness) profiles of material on a substrate surface.

Abstract: A system for performing a network service between a user device, an entity server, and a designated server that uses user sensitive data in the performance of a service subscribed to by the user and offered by the first entity server. The user sensitive data is provided to the designated server. The designated server generates a subscription identifier by encrypting the sensitive data using a public key of a public and private key pair, generated using a public key infrastructure, and issues the encrypted subscription identifier to the user device and the entity server for use in place of the sensitive data. The user device and the entity server use the designated entity server as a proxy for operations that rely upon the actual sensitive data. In these operations, the designated entity server resolves the subscription identifier by decrypting the subscription identifier using a private key of the key pair.

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 solution implementation system includes a solution registry with a corrective action for each of a plurality of predefined root causes of exception errors. The system receives communication data transmitted through a distributed network comprising a plurality of servers. An exception error is detected corresponding to a failed transmission of the communication data between communicatively coupled servers of the distributed network. A first root cause of the exception error is determined. Properties of the distributed network at the time of the detected exception error are determined. Using the solution registry, a first corrective action is determined for the first root cause that is associated with the determined properties of the distributed network at the time of the detected exception error. The first corrective action is automatically implemented to resolve the exception error.

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: Embodiments of the invention are directed to machine learning-based anomaly detection in program code. The system provides a machine learning (ML) anomaly detection model component structured to detect architectural flaws in program code based on processing application logs associated with technology program code and determining flow sequences between a plurality of layers of code. In particular, the system trains the machine learning (ML) anomaly detection model that is structured to (i) construct a first application layer map based on mapping each of the plurality of first classes associated with the first technology program code to one or more application layers, (ii) determine a first architecture pattern associated with the first technology program code, and (iii) determine whether the first technology program code is associated with an anti-pattern.