Abstract: Techniques are described for routing a customer communication to an agent having appropriate expertise to handle the current communication associated with a customer using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of the current communication; generate, using a composite emotion model running on the one or more processors, a composite emotional score for the current communication based on the set of emotion factor values for the current communication; determine a routing recommendation for the current communication that identifies an agent having appropriate expertise to handle the current communication based on at least the composite emotional score; and route the current communication in accordance with the routing recommendation to a computing device of the agent.

Abstract: A method may include receiving a primary document including one or more selection boxes, one or more text lines, and one or more annotations. The method may include determining, a class based on the annotations. The method may include identifying the one or more selection boxes and one or more text lines of the primary document. The method may include generating a graph representing the one or more selection boxes and the one or more text lines. The method may include mapping each of the one or more selection boxes to a respective text line of the one or more text lines of the graph based at least in part on one or more characteristics associated with the selection boxes. The method may include generating a key-value pair associated with each of the one or more text lines and generating a document model of the primary document.

Abstract: A method may include detecting one or more selection boxes and one or more text lines in a primary document. The method may include determining respective vectors associated with the selection box and adjacent text lines to the selection box in a plurality of directions. The method may include determining a set of respective vectors associated with a unique selection box. The method may include determining a variance between respective vectors in the set of respective vectors and identifying a particular direction corresponding to a minimal variance between the respective vectors in the set of respective vectors as compared to a variance of other sets of respective vectors. The method may include generating a key-value pair based on the set of respective vectors characterized by the minimal variance. The method may include generating a document model, including the key-value pair, and extracting data according to the document model.

Abstract: A method comprising: dividing a 3D space into a voxel grid comprising a plurality of voxels; associating a plurality of distance values with the plurality of voxels, each distance value based on a distance to a boundary of an object; selecting an approximate interpolation mode for stepping a ray through a first one or more voxels of the 3D space responsive to the first one or more voxels having distance values greater than a threshold; and detecting the ray reaching a second one or more voxels having distance values less than the first threshold; and responsively selecting a precise interpolation mode for stepping the ray through the second one or more voxels.

Abstract: Techniques are described for generating a set of emotion factor values using one or more machine learning models for customer communications. For example, a computing system includes one or more processors in communication with a memory. The one or more processors are configured to receive communication data of a current communication associated with a customer, apply the communication data to an emotion-based indexer as input wherein the emotion-based indexer includes a set of machine-learning models for a set of emotion factors, generate as output from the emotion-based indexer a set of emotion factor values for the current communication wherein each emotion factor value indicates the measure of a particular emotion factor in the current communication, apply the set of emotion factor values to an emotion classification model, and classify the current communication into an emotion state based on the set of emotion factor values.

Abstract: A method comprising: dividing a 3D space into a voxel grid comprising a plurality of voxels; associating a plurality of distance values with the plurality of voxels, each distance value based on a distance to a boundary of an object; selecting an approximate interpolation mode for stepping a ray through a first one or more voxels of the 3D space responsive to the first one or more voxels having distance values greater than a threshold; and detecting the ray reaching a second one or more voxels having distance values less than the first threshold; and responsively selecting a precise interpolation mode for stepping the ray through the second one or more voxels.

Abstract: The present disclosure is directed to a technique for reduction of latency in network traffic data transmissions. The method includes: parsing an online document to determine a messaging identifier used to communicate over an asynchronous network-based communication channel between a computing device and a content provider device; pinging, via the asynchronous network-based communication channel, the content provider device corresponding to the messaging identifier; receiving, from the content provider device, a response to the pinging; verifying, based on the response, whether the content provider device is operational to receive electronic messages over the asynchronous network-based communication channel; and authorizing, responsive to verification of the content provider device, a content generator component to insert the messaging identifier in one or more instances of an online content item.

Abstract: Techniques are described herein for generating block extender model. An example method includes a system accessing a base model trained for identifying a base class. The system can access an extender comprising block extenders, the extender class distinct from the base class. The system can connect the extender with the base model to generate an augmented model. The system can input training data to the augmented model, the training data is provided to the base model and the extender, the training data comprising data points associated with the extender class. The system can train the extender model to identify the extender class based at least in part on the training data and the signal received from the base machine learning model. The system can generate a trained extender based at least in part on the training, the extender trained to identify an object associated with the extender class.

Abstract: Systems and methods for message delivery prioritization that can include receiving, via an application programming interface, a messaging request of an entity to transmit one or more messages to a plurality of users, selecting one or more message transmission options based on message-associated delivery attributes, and causing the one or more messages to be transmitted to the plurality of users using the selected one or more message transmission options.

Abstract: Techniques for extracting information from unstructured documents that enable an ML model to be trained such that the model can accurately distinguish in-distribution (“in-D”) elements and out-of-distribution (“OO-D”) elements within an unstructured document. Novel training techniques are used that train an ML model using a combination of a regular training dataset and an enhanced augmented training dataset. The regular training dataset is used to train an ML model to identify in-D elements, i.e., to classify an element extracted from a document as belonging to one of the in-D classes contained in the regular training dataset. The augmented training dataset, which is generated based upon the regular training dataset may contain one or more augmented elements which are used to train the model to identify OO-D elements, i.e., to classify an augmented element extracted from a document as belonging to an OO-D class instead of to an in-D class.

Abstract: A method includes, using a processor, identifying a septum and a Left Atrium Appendage (LAA) of a heart of a patient in an anatomical map of at least part of the heart. An entry surface over which a medical device is defined on the anatomical map, which is to be delivered via a sheath that penetrates the septum, is to engage with the LAA. A normal to the entry surface is calculated. A plurality of curves is calculated that each (i) have one end that is tangent to the normal, (ii) have a second end touching the septum, and (iii) comply with specified mechanical properties of the sheath. Multiple candidate locations on the septum are derived from the curves, for transseptal puncture with the sheath. The multiple candidate locations are presented to a user.

Abstract: Embodiments herein relate to a multi-bit flip-flop circuit which uses unidirectional transistors to allow sharing of transistors among a set of flip-flops, while avoiding charge sharing within or between the flip-flops. Clock devices in the circuit can be shared to reduce the clock transistor gate capacitance and associated power consumption. The shared transistors can provide keeper circuits and/or tri-state inverters in a primary latch and a secondary latch in each flip-flop. One example implementation uses tri-state keeper sharing. Another example implementation uses tri-state keeper and/or pass gate sharing. Another example implementation uses pass gate sharing and no keeper.

Abstract: Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving a request from a client device to join a room, the room associate with a plurality of other client devices streaming multimedia content; adding the client device to the room in response to the request; computing a synchronization time for the client based on time values associated with the client device and the other client devices; and transmitting the synchronization time to the client device, causing the client device to adjust playback of the multimedia content based on the synchronization time.

Abstract: The present disclosure is directed to a technique for reduction of latency in network traffic data transmissions. The system parses an online document to determine a messaging identifier used to communicate over an asynchronous network-based communication channel with a content provider device. The system assembles a first instance of the online content item with the messaging identifier. An intermediary appliance located on the asynchronous network-based communication channel in between the first computing device and the content provider device intercepts data packets including the electronic message The system determines a quality of the asynchronous network-based communication channel. The system blocks insertion of the messaging identifier in a second instance of the online content item prior to transmission of the second instance of the online content item to a second computing device to reduce latency by preventing additional messages from being sent to the messaging identifier.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first bucket associated with a first profile and/or a second bucket associated with a second profile are configured. First processes of a first evaluation period may be assigned to the first bucket. The first processes may be performed according to the first profile associated with the first bucket. Second processes of the first evaluation period may be assigned to the second bucket. The second processes may be performed according to the second profile associated with the second bucket. Evaluation metrics associated with the first bucket and the second bucket may be determined based upon the first processes and the second processes. Based upon the evaluation metrics, the first bucket may be selected to be a production bucket during a second evaluation period following the first evaluation period.

Abstract: A disclosed example includes a read local bitline; and a plurality of pulldown transistor circuits coupled to the read local bitline, a first one of the pulldown transistor circuits including: a first low threshold voltage transistor, the first low threshold voltage transistor including a first drain terminal coupled to the read local bitline; and a second low threshold voltage transistor, the second low threshold voltage transistor including a second drain terminal coupled to a first source terminal of the first low threshold voltage transistor, the second low threshold voltage transistor to persist a voltage level detectable at a gate terminal of the second low threshold voltage transistor, the voltage level representative of a bit of information.

Abstract: In the embodiments, a novel prefetch service implemented on a prefetch server is provided to execute content (either targeted or selected) fetch requests in parallel with other requests thereby reducing the overall latency of a content delivery system. In some aspects, a method is provided to receive a prefetch request to prefetch a targeted content item to be presented prior to presenting a primary content item on a user device, the prefetch request including prefetch request parameters; generate, a callback URL associated with the prefetch request; transmit, to a primary content server, the callback URL; obtain a targeted content configuration corresponding to the prefetch request parameters; obtain the targeted content item based on the targeted content configuration; receive, a request to provide the targeted content item, the request including the callback URL; and transmit the targeted content item.

Abstract: A flip-flop circuit includes first and second storage circuits. The flip-flop circuit is configurable to store first values of a data signal in the first storage circuit in response to rising edges of a clock signal and to store second values of the data signal in the second storage circuit in response to falling edges of the clock signal during a double edge triggered mode. The flip-flop circuit is configurable to store third values of the data signal in the first storage circuit and to output the third values from the first storage circuit in response to the clock signal during a single edge triggered mode.

Abstract: A computing device may access visually rich documents comprising an image and metadata. A graph, based on the image or metadata, can be generated for a visually rich document. The graph's nodes can correspond to words from the visually rich document. Features for nodes can be determined by the device. The device may generate model labeled graphs by assigning a pseudo-label to nodes using a pretrained model. The device may generate a plurality of graph labeled graphs by assigning a pseudo-label to nodes by matching a first node from a first graph to at least a second node from a second graph. The device may generate a plurality of updated graphs by cross referencing labels from the model labeled graphs and the graph labeled graphs. Until a change in labels is below a threshold, a model can be trained to perform key-value extraction using the updated graphs.