Abstract: A method includes receiving a request. The request is encrypted to generate an encrypted request. The encrypted request includes a version information for a first software package deployed in a standalone computing system, and a region information and a hardware information for the standalone computing system. The encrypted request is sent to a respective regional data center of a cloud computing system based on the region information of the first software package. A second software package is identified among software packages stored in the respective regional data center. The second software package corresponds to the hardware information of the first software package. The version information of the first software package is compared to a version information of the second software package. In response to identifying that the second software package is more recent than the first software package, the second software package is deployed to the standalone computing system.

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: A method for destressing an agent in a contact center in real-time may include receiving an agent's login and routing incoming voice calls to the agent. A monitoring and triggering application (“MTA”) may receive data about the agent's performance during calls from a statistics server. A graphics processing unit may run an MTA artificial intelligence machine learning (“MTA AI/ML”) algorithm to determine a stress level of the agent by analyzing video, audio, and other metrics during calls. The MTA AI/ML algorithm may recommend to the agent's manager that the agent be given a destress break. When approved, calls may be routed to other agents while the agent receives a video, audio, or game to destress. The agent may receive the break without any negative impact on metrics relating to the agent's performance. The agent may re-enter the call routing server to receive incoming calls upon completion of the break.

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: Methods and systems for forming molybdenum layers on a surface of a substrate and structures and devices formed using the methods are disclosed. Exemplary methods include forming an underlayer prior to forming the molybdenum layer. The underlayer can be used to manipulate stress in the molybdenum layer and/or reduce a nucleation temperature and/or deposition temperature of a step of forming the molybdenum layer.

Abstract: Systems, computer program products, and methods are described herein for developers to initiate development operations from within a development environment of a user application. This can also be extendable dynamically for different user groups (e.g., testers, support, internal associates or employees, or the like), and environments (e.g., lower-level environments, production environments, or the like). As such, the invention offers a dynamic debug mode feature for virtual assistants based on different sections of users and environments.

Abstract: Systems and processes are disclosed to leverage conversational AI across voice and digital channels by automating employee interactions with clients to deliver humanized experiences with speed and accuracy. Live phone conversation. Transcriptions between clients and agents are generated. AI assist provides automation based on client interactions. Prompts are provided to speak to customers. Real time updates and summaries are sent to client devices and streamed to customize agent sessions. Auto navigation of agent views and manipulations of the agent screen are optimized by AI. Instant client validations are enabled via audible and visual confirmations on client devices. Various other AI implementations for improved processing our similarly disclosed.

Abstract: Embodiments of the present invention provide a system for conversion of real-time voice logs to user interface logs for debugging of Interactive Voice Response (IVR) interactions. The system is configured for determining that a user has initiated a real-time voice interaction with a voice interaction processing platform, generating a real-time voice log associated with the real-time voice interaction, parsing the real-time voice log, via a real-time parser, converting, in real-time via a real-time converter, the parsed voice log to a real-time user interface log, generating a real-time user interface comprising the real-time user interface log, and displaying the real-time user interface comprising the real-time user interface log to the user for debugging the real-time voice interaction.

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: 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: 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: 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. 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 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: 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: In one embodiment, an apparatus comprises a log circuit to: identify an input associated with a logarithm operation, wherein the logarithm operation is to be performed by the log circuit using piecewise linear approximation; identify a first range that the input falls within, wherein the first range is identified from a plurality of ranges associated with a plurality of piecewise linear approximation (PLA) equations for the logarithm operation, and wherein the first range corresponds to a first equation of the plurality of PLA equations; compute a result of the first equation based on a plurality of operands associated with the first equation; and return an output associated with the logarithm operation, wherein the output is generated based at least in part on the result of the first equation.

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: Systems, computer program products, and methods are described herein for determining optimal resource transfer routing using quantum gateway optimization.

Abstract: An apparatus and method for cleaning or etching a molybdenum film or a molybdenum nitride film from an interior of a reaction chamber in a reaction system are disclosed. A remote plasma unit is utilized to activate a halide precursor mixed with an inert gas source to form a radical gas. The radical gas reacts with the molybdenum film or the molybdenum nitride film to form a by-product that is removed from the interior of the reaction chamber by a purge gas.