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 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 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 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 executes a dialog flow corresponding to a use case of one or more utterances received from a customer device. Further, a large language model (LLM) is selected from a plurality of LLMs to perform response generation based on an execution state of the dialog flow. Further, a plurality of outputs is received from the selected LLM based on a plurality of prompts provided to the selected LLM to fulfill one or more execution goals of the dialog flow. Further, when one or more of the plurality of outputs comprise: one or more entities extracted from the one or more utterances and a response to be transmitted to the customer device, the adherence of: the extracted one or more entities to one or more business rules and the response to one or more conversation rules, are validated. Subsequently, the response of the one or more of the plurality of outputs is transmitted to the customer device when the corresponding validation is successful.

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.

Abstract: Described herein are techniques for improving performance of a data system by profiling and executing background services based on task type profiles. Techniques described herein can break down background services into tasks and provide the ability to accurately size the tasks based on task types and provide recommendations for gateways for autoscaling and throttling computing resources accordingly.

Abstract: A contact center 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 contact center server, using a classification model, determines for each of the one or more customer messages when there are one or more changes between the corresponding one or more automated response recommendations and corresponding one or more agent responses. Further, the contact center server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the contact center 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 flow 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: A computer-implemented method is disclosed. The method includes obtaining, by a de-duplication server, a candidate pair of a plurality of digitally stored documents from a document database. Text elements are identified from each digitally stored document in the candidate pair in response, and the text elements are stored as document extraction attributes. The method then automatically computes and stores relative positional differences of the text elements between each digitally stored document of the candidate pair and a document similarity score based on the relative positional differences. The relative positional differences are compared with a similarity function to form a difference similarity vector for the candidate pair. The difference similarity vector comprises components corresponding to each relative positional difference. The components of the difference similarity vector are aggregated to determine a final score for the candidate pair.

Abstract: A contact center 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 contact center server, using a classification model, determines for each of the one or more customer messages when there are one or more changes between the corresponding one or more automated response recommendations and corresponding one or more agent responses. Further, the contact center server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the contact center 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 flow 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: A resilient incident management system is provided that continues operation when certain outages occur. An RSS feed is utilized to indicate incident-related information despite the certain outages. In an embodiment, the incident management system includes a redundant architecture that comprises a traffic manager and a backup traffic manager. The incident management system receives a client request for incident-related information and determines a priority level or a performance level of end points associated with the request. Based on a status of the traffic manager and the backup traffic manager, as well as based on the priority level or the performance level, the incident management system causes a computer operation to be executed via the redundant architecture to retrieve a response from a producer and generate the RSS feed.

Abstract: A method for backfilling records dropped by a distributed node system during a transfer to an external database includes initially determining whether an attempt to transfer a data record failed or succeeded. In response to determining that the transfer succeeded, the data record is appended at its original location include a first transfer status identifier. In response to determining that the transfer failed, the data record is appended to include a second transfer status identifier and queued copy of the data record is created by copying the data record to a delayed re-try queue. The method further includes verifying that the transfer status identifier of the queued copy is identical to the transfer status identifier of a newest version of the data record within the distributed node system and, responsive to the verification, attempting a subsequent transfer of the queued copy of the data record to the external database.

Abstract: In an embodiment, a method includes receiving training data representing historic consumer demand for products, detecting changepoints in that data that may be associated with disruptive events, identifying relevant data for modeling, performing clustering, processing configuration information, training one or more machine learning models that are capable of evaluating other received data more accurately, and outputting results to a user display device.

Abstract: The disclosure herein pertains to determining high occupancy vehicle (HOV) lane eligibility of a vehicle and position of the vehicle with respect to an HOV lane. A method for an on board unit (OBU) of a vehicle may include receiving a traveler information message (TIM) from a roadside unit (RSU), the TIM having a payload including codes indicating lane parameters and HOV criteria of an HOV lane. The OBU determines whether or not the vehicle is HOV lane-eligible and whether or not the vehicle is in the HOV lane, and outputs a corresponding notification.

Abstract: A data storage management system comprises features for initiating failover orchestration jobs that invoke recovery resources on demand in a cloud computing environment. Backed up data that is stored persistently in the cloud computing environment may be rapidly restored within the cloud computing environment for use in disaster recovery and/or in test and verification scenarios. This approach may be contrasted to systems where a failover system is “always on” at the failover destination, such as having failover resources always up and running in the cloud computing environment. Such resources typically include a failover virtual machine (VM), a virtual machine datastore for the restored data, and one or more computing resources for restoring an auxiliary copy to the VM's datastore. The cloud-based failover resources are deactivated or taken down once the failover event ends.