Abstract: A contact center server provides to an agent device one or more automated response recommendations determined by an executable virtual assistant platform to correspond to customer message data received from a customer device as part of conversation data. Further, the contact center server receives a selection of one of the automated response recommendations from the agent device and identifies agent response data to the customer message data transmitted from the agent device to the customer device. Further, the contact center server, using one or more classification models, determines when there is a change between the selected response recommendation and the identified agent response data. Further, the contact center server associates one or more tags to the identified agent response data in the conversation data when the determination indicates the change.

Abstract: Asset error remediation is provided. Risk and classification of an asset error are analyzed to prioritize asset error remediation for an asset based on risk criticality, risk context, and vulnerability level corresponding to the asset by detecting suspicious behavior and risk exposure to the asset in a heterogeneous distributed computing environment using artificial intelligence. A priority of the asset error remediation is determined to fix the asset within the heterogeneous distributed computing environment based on the risk and the classification of the asset error. A set of action steps is performed to fix the asset within the heterogeneous distributed computing environment based on the priority of the asset error remediation.

Abstract: Disclosed is a method for displaying actionable information on an electronic vehicle display panel which includes: receiving data from a plurality of sensors. The data received from each of the plurality of sensors is analyzed to determine a data category for the data from each sensor, wherein each data category corresponds to an information priority level. The data from each of the plurality of sensors is displayed according to the determined data category, wherein data within a data category corresponding to a high information priority level is displayed more prominently relative to other data, and wherein data within a data category corresponding to a low information priority level is displayed less prominently relative to other data; and displaying at least a portion of the data as at least one from the set of: a fuel cell voltage difference, a hydrogen flow rate, a temperature discrepancy, and a rate of temperature change.

Abstract: A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.

Abstract: Systems described herein may dynamically add one or more proxy agents to a cloud data storage system to process an information management job, such as restore job. Upon completion of the job or at some other appropriate interval, the system can power down and decommission the proxy agents and/or the virtual machines on which the proxies reside according to a cleanup schedule (e.g., at hourly or minute intervals). The system takes into account currently existing proxies or virtual machines when processing a restore request to determine the need for new proxies to service the restore request. In this manner the system can save costs and computing resources through efficient virtual machine deployment and retirement.

Abstract: Systems described herein may dynamically add one or more proxy data protection agents to a cloud data storage system to process a data protection job. Upon completion of the job or at some other appropriate interval, the system can power down and decommission the proxy data protection agents and/or the virtual machines on which the data protection proxies reside according to a cleanup schedule (e.g., at hourly or minute intervals). In order to improve the allocation of computing resources, the system takes into account currently existing proxies or virtual machines when processing a backup request to determine the need for new proxies to service the backup request. In this manner the system can save costs and computing resources through efficient virtual machine deployment and retirement.

Abstract: A semiconductor package is disclosed. In one example, the semiconductor package comprises a semiconductor chip, a molded body encapsulating the semiconductor chip and comprising a top face and an opposing bottom face and four side faces connecting the top and bottom faces, and a plurality of electrical contacts arranged on two of the side faces of the molded body, wherein the other two side faces are metal-free side faces, and wherein the molded body comprises a cut surface at no more than one of the side faces.

Abstract: A contact center server receives an utterance from a customer device as part of a conversation. The contact center server identifies utterance parameters of the utterance, generates a first response to the utterance based on the utterance parameters, and outputs the utterance parameters and the first response to an agent device. The contact center server receives agent-identified-information corresponding to the utterance or agent-modified-information corresponding to the utterance parameters from the agent device. Subsequently, the contact center server generates a second response to the utterance based on the agent-identified-information or the agent-modified-information and outputs the second response to the agent device.

Abstract: A fuel cell system having at least one fuel cell with an external surface; and one or more of audio, image, or strain sensors external to the fuel cell surface, configured for detecting a change in the external surface of the fuel cell indicative of a fault condition. The at last one sensor may include a visual camera, an IR camera, an IR detector, or a UV-responsive camera, or an ultrasound transducer, a piezoelectric sensor and a vibration sensor, or a surface acoustic wave detector, or a mass spectrometer.

Abstract: A contact center server receives an input from a customer device as part of a conversation. The contact center server (CCS) identifies at least one of: one or more intents, one or more entities, or one or more entity values from the input. The CCS detects one or more escalation conditions based on the input and pauses the conversation based on the detected one or more escalation conditions. The CCS outputs a conversation transcript and the identified one or more intents, the one or more entities, or the one or more entity values to an agent device. The CCS receives agent-identified-information or agent-modified-information from the agent device. Subsequently, the CCS resumes the conversation by providing a response to the input based on the received agent-identified-information or the agent-modified-information.

Abstract: An apparatus includes a first optical circuit and a second optical circuit. The first optical circuit has a network of interconnected interferometers to perform an M-mode universal transformation on N input optical modes that are divided into (M?1) groups of pulses. The first optical circuit also includes M input ports. Each input port of a first (M?1) input ports is configured to receive a corresponding group of pulses in the (M?1) groups of pulses. The first optical circuit also includes M output ports and a first delay line to couple an Mth output port with an Mth input port. The second optical circuit includes a network of beamsplitters and swap gates to perform a (2M?3)-mode residual transformation. The first optical circuit and the second optical circuit are configured to perform an arbitrary N-mode unitary transformation to the N input optical modes via a rectangular architecture.

Abstract: A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.

Abstract: A virtual assistant server receives conversational inputs as part of a conversation from a customer device and generates a version of a data record of the conversation upon: the receiving of each of the conversational inputs, or receiving each output generated by one of a plurality of software modules when the one of the software modules receives a system input from the conversation management framework. The virtual assistant server provides each of the generated versions of the data record to a communication orchestrator and receives for each of the generated versions of the data record, execution instructions from the communication orchestrator. Further, the virtual assistant server communicates with one or more of the software modules based on the received execution instructions, and provides based on the communicating, one or more of the outputs of the software modules to the customer device, when the one or more of the outputs comprise one or more responses to one or more of the conversational inputs.

Abstract: A virtual assistant server extracts concept(s) from a received user query and correlates the extracted concept(s) to a plurality of nodes in a knowledge graph to identify qualifying path(s) in the knowledge graph. Further, when the two or more qualifying paths are identified, the virtual assistant server addresses one or more ambiguities in the knowledge graph by: prompting the user device to resolve a current one of the one or more ambiguities progressing from contextual nodes towards leaf nodes in the knowledge graph; receiving a response to the prompt from the user device; and repeating the prompting and the receiving until each of the one or more ambiguities is resolved and a matching path to the received query is identified. Finally, the virtual assistant server outputs content associated with a leaf node of the matched path to the user device.

Abstract: A server manages conversation data for a plurality of conversations between agent devices and customer devices and provides to the agent devices, one or more automated response recommendations to one or more customer messages by executing one or more dialog flows. The server, using a classification model, determines for each of the messages when there are one or more changes between the corresponding one or more automated response recommendations and one or more agent responses. Further, the server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the server identifies one or more patterns in the one or more tags associated to the one or more agent responses and provides one or more modification recommendations to the one or more dialog flows to one or more enterprise user devices based on the identified one or more patterns.

Abstract: Techniques related to video coding include efficient frame loss recovery using a feedback channel acknowledgement to decode base layer frames.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: Implementations disclosed describe systems and methods to optimize allocation of wireless subcarriers to station devices in wireless networks. In an example implementation, the disclosed techniques may include determining, by a station device in a wireless network, that a current set of wireless subcarriers, allocated by an access point device of the wireless network for the station device, is to be changed, generating, by the station device, a feedback information characterizing a current state of one or more of the wireless subcarriers, and sending, by the station device, the feedback information to the access point device.

Abstract: According to an aspect of the present invention disclosed herein, there is provided buckle, comprising: a male component and a female component, wherein: the male component comprises: a slot for a strap at an end of the male component opposite the side where the male component is configured to insert into the female component; an inclined insert; and one or more mating slots in the inclined insert; and the female component comprises: a top wall, a bottom wall, and sidewalls forming a receiving portion; a plurality of teeth; a slot for a strap; and a chord, wherein the chord is configured that when pulled the teeth disengage just enough for the male component to be released.

Abstract: A virtual assistant server determines one or more use cases of one or more utterances received from one or more customer devices. The virtual assistant server executes one or more dialog flows corresponding to the one or more use cases of the one or more utterances, where the executing comprises providing one or more inputs to a fine-tuned large language model comprising: use case context, customer context, conversation context, business rules, and exit conditions. The virtual assistant server receives one or more outputs corresponding to the one or more inputs from the fine-tuned large language model, and determines one or more responses based on the one or more outputs. Subsequently, the virtual assistant server provides the one or more responses to the one or more customer devices.