Abstract: A system and method for determining location information of a portable device relative to an object based on an environment of the object is provided. The system and method may determine an environment of the object based on a received signal characteristic of communications transmitted from a first object device disposed on the object. Based on the determined type of environment or a characteristic thereof, a locator or an adapter, or both, may be selected for determining location information about a portable device relative to the object.

Abstract: An apparatus includes a protection circuit electrically connected to first and second voltage domains. The protection circuit includes a first silicon-controlled rectifier (SCR) and a second SCR connected in anti-parallel configuration. The first SCR is configured to connect the first voltage domain and the second voltage domain based on detection of a first triggering condition. The second SCR is configured to connect the second voltage domain and the first voltage domain based on detection of a second triggering condition. The protection circuit is configured to isolate the first and second voltage domains without the triggering conditions.

Abstract: A system and method are provided for obtaining calibration data for a portable device relative to an object in order to facilitate defining a locator for determining a location of the portable device relative to the object. The system and method may obtain calibration data pertaining to a signal characteristic of communications transmitted from the portable device and corresponding to a known location of the portable device (e.g., truth data).

Abstract: A system and method for determining location information of a portable device relative to an object based on an environment of the object is provided. The system and method may determine an environment of the object based on a received signal characteristic of communications transmitted from a first object device disposed on the object. Based on the determined type of environment or a characteristic thereof, a locator or an adapter, or both, may be selected for determining location information about a portable device relative to the object.

Abstract: Techniques are described for generating metrics about an individual's experience. One of the method describes providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual's experience. The method includes associating, by at least one processor, the metric of the individual's experience with the individual. The method also includes communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.

Abstract: Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data from previous service sessions between service representative(s) and individual(s). Training data may include, for previous service session(s), a session record (e.g., audio record) of the session and a set of survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that is a combination of a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network.

Abstract: Techniques are described for real time monitoring and tagging of media content generated during a service session between an individual and a service representative (SR). Media content received during a service session may be dynamically tagged with one or more metadata tags during the service session, the tag(s) indicating portion(s) of the media content that potentially include sensitive information. Tagging may be based on a SR's gestures, clicks, keystrokes, mouse-overs, and/or other actions performed in a SR user interface (UI) during the service session. The media content may be redacted based on the tags to remove and/or obfuscate potentially sensitive information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for coordinating callers with customer service representatives. One of the methods includes in response to receiving identification information from a caller, selecting a virtual machine from a plurality of virtual machines, the virtual machine configured to generate a virtual user interface for an application executing on the virtual machine. The method includes populating the virtual user interface with information about the caller based on the received identification information. The method includes sharing the virtual user interface with an application executing on a computer of a customer service representative. The method includes connecting the caller with the customer service representative.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media are used for coordinating callers with customer service representatives. One of the methods includes identifying a number of callers. The method also includes dynamically adjusting a number of customer service representatives based on the number of callers.

Abstract: Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data such as communications from previous service sessions between service representative(s) and individual(s), and survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that combines a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network. Individuals can be clustered according to the metric(s) and/or other factors, and the cluster(s) can be employed for routing incoming service requests.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a coordinating callers with customer service representatives is described. One of the methods includes identifying a number of callers. The method also includes dynamically adjusting a number of customer service representatives based on the number of callers.

Abstract: A system and method for determining location information of a portable device relative to an object based on an environment of the object is provided. The system and method may determine an environment of the object based on a received signal characteristic of communications transmitted from a first object device disposed on the object. Based on the determined type of environment or a characteristic thereof, a locator or an adapter, or both, may be selected for determining location information about a portable device relative to the object.

Abstract: A system and method are provided for obtaining calibration data for a portable device relative to an object in order to facilitate defining a locator for determining a location of the portable device relative to the object. The system and method may obtain calibration data pertaining to a signal characteristic of communications transmitted from the portable device and corresponding to a known location of the portable device (e.g., truth data).

Abstract: A system and method for determining location information of a portable device relative to an object is provided. The system and method may include communicating with a secondary portable device separate from the portable device, which may be considered a primary portable device. The secondary portable device may be not be configured to communicate with the object or unaware that it is communicating with the object, and may receive communication parameters from the primary portable device.

Abstract: A system and method for determining location information based on a reference profile for a reference device, and a system and method for determining the reference profile. The system may determine the reference locator with respect to the reference device based on a plurality of samples obtained with respect to communications between the reference device and an object device. An adapter locator may be determined for the reference locator for samples obtained with respect to communications between a test device and object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for sending a forked media stream of an electronic communication between a customer and an agent to an analysis system for a duration. Receiving analysis results obtained from one or more analytics performed on the forked media stream. Then, determining whether to perform one or more operations in response to the analysis results.

Abstract: Semiconductor devices having busing layouts configured to reduce on-die capacitance are disclosed herein. In one embodiment, a semiconductor device includes an electrostatic discharge device electrically connected in parallel with an integrated circuit and configured to divert high voltages generated during an electrostatic discharge event away from the integrated circuit. The semiconductor device further includes a signal bus and a power bus electrically connected to the electrostatic discharge device. The signal bus includes a plurality of first fingers grouped into first groups and the power bus includes a plurality of second fingers grouped into second groups. The first groups are positioned generally parallel to and interleaved between the second groups.

Abstract: A method includes in response to receiving identification information from a caller, selecting a virtual machine from a plurality of virtual machines, the virtual machine configured to generate a virtual user interface for an application executing on the virtual machine. The method includes populating the virtual user interface with information about the caller based on the received identification information. The method includes sharing the virtual user interface with an application executing on a computer of a customer service representative. The method includes connecting the caller with the customer service representative.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media are used for coordinating callers with customer service representatives. One of the methods includes identifying a number of callers. The method also includes dynamically adjusting a number of customer service representatives based on the number of callers.

Abstract: Techniques are described for generating metric(s) that predict survey score(s) for a service session. Model(s) may be trained, through supervised or unsupervised machine learning, using training data from previous service sessions between service representative(s) and individual(s). Training data may include, for previous service session(s), a session record (e.g., audio record) of the session and a set of survey scores provided by the serviced individual to rate the session on one or more criteria (e.g., survey questions). The model(s) may be trained to output, based on an input session record, metric(s) that each correspond to a survey score that would have been provided by the individual had they completed the survey. The model may be a concatenated model that is a combination of a language model output from a language classifier recurrent neural network, and an acoustic model output from an acoustic feature layer convolutional neural network.