Abstract: Techniques are disclosed for training a feature extraction model. A computing device can receive a training image and generate noised feature vectors using a feature extraction model characterized by first parameters and taking the training image as input. The computing device can determine the noised feature vectors by at least determining a feature vector for individual pixels in the training image and applying noise to each feature vector. The computing device can generate a reconstructed image using a reconstructor model characterized by second parameters and taking the noised feature vectors as input. The computing device can determine a reconstruction loss by comparing the training image with the reconstructed image and a noise loss using the noise applied to each feature vector. The computing device can update the first parameters based on the noise loss.

Abstract: A system and method that allocate call capacity based on the need to divide the call capacities of at least some call-termination devices across geographic regions. Accordingly, the allocation system uses various input parameters as constraints in a linear programming optimization, which has the objective of maximizing the capacity allocation of a device to fulfill the traffic demands of each region being processed. The input parameters that are used include i) the traffic demand forecast of each geographic region being evaluated, ii) the available call capacity of each call-termination device, iii) the call destinations associated with each region, and iv) service levels associated with each given call destination. Call-capacities are separately allocated for i) the predicted traffic that is expected in the next time period and ii) an additional margin of excess traffic above and beyond the expected traffic.

Abstract: A system and method blend particular quality considerations into the process of expanding the route choices in a route table, such that more calls can be successfully routed while maximizing certain objectives. The quality considerations drive how additional routes are chosen for the route table and how call volumes are allocated to the chosen routes so that overall profitability can be maximized within the governing constraints of quality targets, route quality, predicted traffic, and route capacity.

Abstract: A system and method that allocate call capacity based on the need to divide the call capacities of at least some call-termination devices across geographic regions. Accordingly, the allocation system uses various input parameters as constraints in a linear programming optimization, which has the objective of maximizing the capacity allocation of a device to fulfill the traffic demands of each region being processed. The input parameters that are used include i) the traffic demand forecast of each geographic region being evaluated, ii) the available call capacity of each call-termination device, iii) the call destinations associated with each region, and iv) service levels associated with each given call destination. Call-capacities are separately allocated for i) the predicted traffic that is expected in the next time period and ii) an additional margin of excess traffic above and beyond the expected traffic.

Abstract: A system and method that allocate call capacity based on the need to divide the call capacities of at least some call-termination devices across geographic regions. Accordingly, the allocation system uses various input parameters as constraints in a linear programming optimization, which has the objective of maximizing the capacity allocation of a device to fulfill the traffic demands of each region being processed. The input parameters that are used include i) the traffic demand forecast of each geographic region being evaluated, ii) the available call capacity of each call-termination device, iii) the call destinations associated with each region, and iv) service levels associated with each given call destination. Call-capacities are separately allocated for i) the predicted traffic that is expected in the next time period and ii) an additional margin of excess traffic above and beyond the expected traffic.

Abstract: A system and method blend particular quality considerations into the process of expanding the route choices in a route table, such that more calls can be successfully routed while maximizing certain objectives. The quality considerations drive how additional routes are chosen for the route table and how call volumes are allocated to the chosen routes so that overall profitability can be maximized within the governing constraints of quality targets, route quality, predicted traffic, and route capacity.

Abstract: A system and method blend particular quality considerations into the process of expanding the route choices in a route table, such that more calls can be successfully routed while maximizing certain objectives. The quality considerations drive how additional routes are chosen for the route table and how call volumes are allocated to the chosen routes so that overall profitability can be maximized within the governing constraints of quality targets, route quality, predicted traffic, and route capacity.

Abstract: A system and method that allocate call capacity based on the need to divide the call capacities of at least some call-termination devices across geographic regions. Accordingly, the allocation system uses various input parameters as constraints in a linear programming optimization, which has the objective of maximizing the capacity allocation of a device to fulfill the traffic demands of each region being processed. The input parameters that are used include i) the traffic demand forecast of each geographic region being evaluated, ii) the available call capacity of each call-termination device, iii) the call destinations associated with each region, and iv) service levels associated with each given call destination. Call-capacities are separately allocated for i) the predicted traffic that is expected in the next time period and ii) an additional margin of excess traffic above and beyond the expected traffic.

Abstract: A system and method that allocate call capacity based on the need to divide the call capacities of at least some call-termination devices across geographic regions. Accordingly, the allocation system uses various input parameters as constraints in a linear programming optimization, which has the objective of maximizing the capacity allocation of a device to fulfill the traffic demands of each region being processed. The input parameters that are used include i) the traffic demand forecast of each geographic region being evaluated, ii) the available call capacity of each call-termination device, iii) the call destinations associated with each region, and iv) service levels associated with each given call destination. Call-capacities are separately allocated for i) the predicted traffic that is expected in the next time period and ii) an additional margin of excess traffic above and beyond the expected traffic.

Abstract: A system and method blend particular quality considerations into the process of expanding the route choices in a route table, such that more calls can be successfully routed while maximizing certain objectives. The quality considerations drive how additional routes are chosen for the route table and how call volumes are allocated to the chosen routes so that overall profitability can be maximized within the governing constraints of quality targets, route quality, predicted traffic, and route capacity.

Abstract: A system and method blend particular quality considerations into the process of expanding the route choices in a route table, such that more calls can be successfully routed while maximizing certain objectives. The quality considerations drive how additional routes are chosen for the route table and how call volumes are allocated to the chosen routes so that overall profitability can be maximized within the governing constraints of quality targets, route quality, predicted traffic, and route capacity.

Abstract: A tiered service model for a digital multimedia contact center assigns an entering contact to an initial service tier based on routing criteria for the contact and may escalate or de-escalate the contact to a different service tier if the routing criteria chances. The routing criteria is initially determined based on a media type associated with the contact. The digital multimedia contact center contains a set of media routers, each of which passes a contact of a particular media type to a workflow engine which executes workflows to direct the processing of contacts at service tiers that require agent activity. Agents are allocated to contacts by a dynamic automate contact distributor and the appropriate media router is used to route the contact to an agent. The workflow engine also executes workflows for agents to control the allocation of agents to contacts.

Abstract: A tiered service model for a digital multimedia contact center assigns an entering contact to an initial service tier based on routing criteria for the contact and may escalate or de-escalate the contact to a different service tier if the routing criteria chances. The routing criteria is initially determined based on a media type associated with the contact. The digital multimedia contact center contains a set of media routers, each of which passes a contact of a particular media type to a workflow engine which executes workflows to direct the processing of contacts at service tiers that require agent activity. Agents are allocated to contacts by a dynamic automate contact distributor and the appropriate media router is used to route the contact to an agent. The workflow engine also executes workflows for agents to control the allocation of agents to contacts.

Abstract: A tiered service model for a digital multimedia contact center assigns an entering contact to an initial service tier based on routing criteria for the contact and may escalate or de-escalate the contact to a different service tier if the routing criteria changes. The routing criteria is initially determined based on a media type associated with the contact. The digital multimedia contact center contains a set of media routers, each of which passes a contact of a particular media type to a workflow engine which executes workflows to direct the processing of contacts at service tiers that require agent activity. Agents are allocated to contacts by a dynamic automate contact distributor and the appropriate media router is used to route the contact to an agent. The workflow engine also executes workflows for agents to control the allocation of agents to contacts.

Abstract: A method and apparatus for optimizing routing through a data network is disclosed. Plural simulations are run on various links between network routers, and a central location may be used to compile the information from the links and calculate parameters of paths through the network. A graphical user interface to specify parameters of the simulations is also provided.

Abstract: A tiered service model for a digital multimedia contact center assigns an entering contact to an initial service tier based on routing criteria for the contact and may escalate or de-escalate the contact to a different service tier if the routing criteria changes. The routing criteria is initially determined based on a media type associated with the contact. The digital multimedia contact center contains a set of media routers, each of which passes a contact of a particular media type to a workflow engine which executes workflows to direct the processing of contacts at service tiers that require agent activity. Agents are allocated to contacts by a dynamic automate contact distributor and the appropriate media router is used to route the contact to an agent. The workflow engine also executes workflows for agents to control the allocation of agents to contacts.