Abstract: Disclosed are systems and methods for forecasting inbound telecommunications, and more particularly, for analyzing real-time and historical call center data, and applying a forecasting model to said data in order to predict inbound call volume. Additionally, tools are disclosed for manipulating call center data and generating visual representations of metrics pertaining to forecasting call center data via a dashboard.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are systems and methods for routing inbound communications at call centers in order to enhance customer experience and call center agent efficiency. In order to effectively route communications, customer service call types and one or more agent performance rates responding to said customer service calls are analyzed in order to determine which one or more call center agents should be routed which specific customer service call type. Additionally, tools are disclosed for analyzing call center agent performance rates and assigning call center agents to service specific customer service call types.

Abstract: A method and system are provided for assessing damage to a structure. According to one embodiment, the method includes using a classifier that is trained to associate respective portions of in an image of a structure with respective external parts of the structure to detect several of external parts of a structure in a first image of the structure. The method also includes using a first machine learning system, trained to perform segmentation of an image, to identify one or more image segments in a second image, where each image segment represents damage of a particular type and, using a second machine learning system, trained to associate image segments with external parts identified in an image, to associate a first image segment with a first external part, indicating damage to the first external part.

Abstract: Systems and methods forecast inbound telecommunications, and more particularly, analyze real-time and historical call center data, and apply a forecasting model to the data in order to predict inbound call volume. These systems and methods employ tools that manipulate call center data and generate visual representations of metrics pertaining to forecasting call center data via a dashboard.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for dynamically managing data associated with transactions of wearable items. For example, a method may include receiving wearable item data from one or more electronic tenant interfaces, hosting an electronic warehouse operations portal and/or an electronic administrative portal, receiving one or more electronic user transactions initiated at one or more user platforms, updating one or more transaction databases and one or more analytics databases, based on the one or more electronic user transactions, receiving one or more wearable item operations requests, initiating one or more microservices to fulfill the one or more wearable item operations requests, and updating at least one of the one or more transaction databases and one or more analytics databases based on completion of the one or more wearable item operations requests.

Abstract: Disclosed are systems and methods for forecasting inbound telecommunications, and more particularly, for analyzing real-time and historical call center data, and applying a forecasting model to said data in order to predict inbound call volume. Additionally, tools are disclosed for manipulating call center data and generating visual representations of metrics pertaining to forecasting call center data via a dashboard.

Abstract: A system and method for a low complexity and robust spatiotemporal combining for video enhancement is disclosed. In one embodiment, the method includes computing a standard deviation estimate between a video frame and a temporally neighboring frame of the video frame in a video sequence, computing an error value, for each sub-block of pixels within a block of pixels in a current video frame, between pixel values within the sub-block in the current video frame and corresponding motion compensated pixel values in a temporally neighboring video frame of the current video frame, computing a temporal weighting factor for each sub-block of pixels as a function of the error value and the standard deviation estimate, and combining the block of pixels in the current video frame and their corresponding motion compensated pixel values in the temporally neighboring video frame using the computed temporal weighting factor.

Abstract: A scanner is disclosed. The scanner detects when the to surface of the ADF scan glass is contaminated.

Abstract: A system and method for script and orientation detection of images are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a concatenated vertical document vectors (VDV) and a horizontal document vector (HDV) are computed. In addition, a substantially matching script and orientation is obtained by comparing the computed concatenated VDV and HDV of the image with reference VDV and HDV associated with each script and orientation, respectively. Also, the substantially matching script and orientation are declared as the script and orientation of the image, if the computed concatenated VDV and HDV of the image substantially match with the reference VDV and HDV of the matching script and orientation, respectively.

Abstract: A system and method for script and orientation detection of images using artificial neural networks (ANNs) are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a zonal density run (ZDR) is obtained for each connected component in the extracted textual content. In addition, a concatenated vertical document vector (VDV), horizontal document vector (HDV), and zonal density vector (ZDV) is computed by normalizing the obtained VCR, HCR, and ZDR, respectively, for each connected component. Moreover, the script in the image is determined using a script detection ANN module and the concatenated VDV, HDV, and ZDV of the image. Also, the orientation of the image is determined using an orientation detection ANN module and the concatenated VDV, HDV, and ZDV of the image.

Abstract: A system and method for script and orientation detection of images are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a concatenated vertical document vectors (VDV) and a horizontal document vector (HDV) are computed. In addition, a substantially matching script and orientation is obtained by comparing the computed concatenated VDV and HDV of the image with reference VDV and HDV associated with each script and orientation, respectively. Also, the substantially matching script and orientation are declared as the script and orientation of the image, if the computed concatenated VDV and HDV of the image substantially match with the reference VDV and HDV of the matching script and orientation, respectively.

Abstract: A system and method for script and orientation detection of images using artificial neural networks (ANNs) are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a zonal density run (ZDR) is obtained for each connected component in the extracted textual content. In addition, a concatenated vertical document vector (VDV), horizontal document vector (HDV), and zonal density vector (ZDV) is computed by normalizing the obtained VCR, HCR, and ZDR, respectively, for each connected component. Moreover, the script in the image is determined using a script detection ANN module and the concatenated VDV, HDV, and ZDV of the image. Also, the orientation of the image is determined using an orientation detection ANN module and the concatenated VDV, HDV, and ZDV of the image.