Abstract: Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Abstract: Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Abstract: Systems, methods, and computer program products include smart capacity workload routing with workload modeling. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is processed and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model. An agent with capacity that is available to handle the requested user communication is identified. A communication slot for the identified agent is activated and defined by the identified workload measurement, and the request is routed to the identified agent and updating available workload capacity in the system.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: The present disclosure relates generally to facilitating routing of communications. One example includes a communication server determining capacities associated with a terminal devices based on workloads for agents associated with the terminal devices. Historical acceptance data is accessed for past interaction invitations to user devices associated with one or more criteria. Current data is then used to determine available interactions and to facilitate interactions using interaction invitations based on the historical data and the current number of available interactions.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: In some embodiments, a set of user groups can be defined, with each group relating to a different webpage experience, user action, etc. Requests are assigned to one of the groups based on actual webpage presentation features and/or user actions. A group-specific model is generated for each group and translates user information to a preliminary result (e.g., a purchasing probability). A model combination includes a weighted combination of a set of available group-specific models. User information is processed using the model combination to generate a model result. The model result is evaluated to determine whether a requested webpage is to be customized in a particular manner and/or an opportunity is to be offered.

Abstract: Systems, methods, and computer program products include smart capacity workload routing with workload modeling. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is processed and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model. An agent with capacity that is available to handle the requested user communication is identified. A communication slot for the identified agent is activated and defined by the identified workload measurement, and the request is routed to the identified agent and updating available workload capacity in the system.

Abstract: Systems and methods can be provided for predicting responses during communication sessions with network devices. In some implementations, systems and methods can facilitate predicting responses using machine learning techniques. Messages received through a platform can be stored in a repository. A machine learning model may be trained using the stored messages. When a terminal device is communicating with a network device in a communication session, the messages exchanged in the communication session and the machine learning model can be used to predict future responses in real-time. The predicted future responses can be presented at the terminal device. A predicted response can be selected at the terminal device. Upon selection, the selected predicted response is transmitted to the network device during the communication session.

Abstract: Systems and methods can be provided for predicting responses during communication sessions with network devices. In some implementations, systems and methods can facilitate predicting responses using machine learning techniques. Messages received through a platform can be stored in a repository. A machine learning model may be trained using the stored messages. When a terminal device is communicating with a network device in a communication session, the messages exchanged in the communication session and the machine learning model can be used to predict future responses in real-time. The predicted future responses can be presented at the terminal device. A predicted response can be selected at the terminal device. Upon selection, the selected predicted response is transmitted to the network device during the communication session.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: Implementations are directed to updating a trained voice bot that is deployed for conducting conversations on behalf of a third-party. A third-party developer can interact with a voice bot development system that enables the third-party developer to train, update, validate, and monitor performance of the trained voice bot. In various implementations, the trained voice bot can be updated by updating a corpus of training instances that was initially utilized to train the voice bot, and updating the trained voice bot based on the updated corpus. In some implementations, the corpus of training instances may be updated in response to identifying occurrence(s) of behavioral error(s) of the trained voice bot while the conversations are being conducted on behalf of the third-party. In additional or alternative implementations, the corpus of training instances may be updated in response to determining the trained voice bot does not include a desired behavior.

Abstract: In some embodiments, a set of user groups can be defined, with each group relating to a different webpage experience, user action, etc. Requests are assigned to one of the groups based on actual webpage presentation features and/or user actions. A group-specific model is generated for each group and translates user information to a preliminary result (e.g., a purchasing probability). A model combination includes a weighted combination of a set of available group-specific models. User information is processed using the model combination to generate a model result. The model result is evaluated to determine whether a requested webpage is to be customized in a particular manner and/or an opportunity is to be offered.

Abstract: Systems, methods, and computer program products include smart capacity workload routing with workload modeling. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is processed and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model. An agent with capacity that is available to handle the requested user communication is identified. A communication slot for the identified agent is activated and defined by the identified workload measurement, and the request is routed to the identified agent and updating available workload capacity in the system.

Abstract: Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.

Abstract: The present disclosure relates generally to facilitating routing of communications. One example includes a communication server determining capacities associated with a terminal devices based on workloads for agents associated with the terminal devices. Historical acceptance data is accessed for past interaction invitations to user devices associated with one or more criteria. Current data is then used to determine available interactions and to facilitate interactions using interaction invitations based on the historical data and the current number of available interactions.

Abstract: The present disclosure relates generally to facilitating routing of communications. One example includes a communication server determining capacities associated with a terminal devices based on workloads for agents associated with the terminal devices. Historical acceptance data is accessed for past interaction invitations to user devices associated with one or more criteria. Current data is then used to determine available interactions and to facilitate interactions using interaction invitations based on the historical data and the current number of available interactions.

Abstract: Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.

Abstract: The present disclosure relates generally to facilitating routing of communications. One example includes a communication server determining capacities associated with a terminal devices based on workloads for agents associated with the terminal devices. Historical acceptance data is accessed for past interaction invitations to user devices associated with one or more criteria. Current data is then used to determine available interactions and to facilitate interactions using interaction invitations based on the historical data and the current number of available interactions.

Abstract: Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.