Abstract: In various examples, hybrid models for determining intents in conversational AI systems and applications are disclosed. Systems and methods are disclosed that use a machine learning model(s) and a data file(s) that associates requests (e.g., questions) with responses (e.g., answers) in order to generate final responses to requests. For instance, the machine learning model(s) may determine confidence scores that indicate similarities between the requests from the data file(s) and an input request represented by text data. The data file(s) is then used to determine, based on the confidence scores, one of the responses that is associated with one of the requests that is related to the input request. Additionally, the response may then used to generate a final response to the input request.

Abstract: In various examples, contextual data may be generated using structured and unstructured data for conversational AI systems and applications. Systems and methods are disclosed that use structured data (converted to unstructured form) and unstructured data, such as from a knowledge database(s), to generate contextual data. For instance, the contextual data may represent text (e.g., narratives), where a first portion of the text is generated using the structured data and a second portion of the text is generated using the unstructured data. The systems and methods may then use a neural network(s), such as a neural network(s) associated with a dialogue manager, to process input data representing a request (e.g., a query) and the contextual data in order to generate a response to the request. For instance, if the request includes a query for information associated with a topic, the neural network(s) may generate a response that includes the requested information.

Abstract: There are provided systems and methods for dynamic node insertion of secondary services for high-availability during main decision failure at runtime. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. A main decision service may normally be used for data processing and decision-making. However, at certain times, the main decision service may fail, such as if a data processing node fails to process data or times out while processing a data processing request, such as during electronic transaction processing. During this runtime, a dynamic injection processor may dynamically inject a node that performs a call to a secondary service to process the data on behalf of the node and/or main decision service so that a response is provided to the data processing request.

Abstract: A differential video rendering system, including a graphics processing unit (GPU); a graphical display coupled to the GPU; a video decoder configured to decode a bitstream of encoded data into a plurality of sets of decoded blocks; at least one processor configured to: generate, based on a first set of the plurality of sets of decoded blocks, a first differential video frame comprising a plurality of sets of differential regions, normalize each set of the plurality of sets of differential regions to a fixed size block to provide a normalized plurality of sets of differential regions, map a respective set of the normalized plurality of sets of differential regions to align with a respective tile size region of a plurality of tile size regions conforming with the GPU, generate a hierarchal region tree based on the normalized plurality of sets of differential regions mapped to the plurality of tile size regions, and generate a plurality of optimal regions based on the hierarchal region tree satisfying a predefin

Abstract: The present disclosure pertains to an improved computing architecture allowing for better utilization of CPU, memory, network bandwidth, and other computing resources, particularly within the context of a decision service system that handles client requests. These methods and systems may be directed to the evaluation and use of a partial response from a decision service system when the generation of a full response by the decision service system is predicted to take longer than what is expected.

Abstract: Systems and methods for monitoring network activity. The methods include receiving at an interface a first logging parameter for a first network device, wherein the first logging parameter specifies how the first network device is to record data associated with the first network device; communicating the first logging parameter to the first network device; and indicating to the first network device a first network-accessible location to where the first network device is to transmit its recorded data, wherein the first network device is configured to record data in accord with the first logging parameter and transmit the recorded data to the first network-accessible location.

Abstract: Systems and methods for predicting interactions between objects and predicting a trajectory of an object are presented herein. A system can obtain object data associated with a first object and a second object. The object data can have position data and velocity data for the first object and the second object. Additionally, the system can process the obtained object data to generate a hybrid graph using a graph generator. The hybrid graph can have a first node indicative of the first object and a second node indicative of the second object. Moreover, the system can process, using an interaction prediction model, the generated hybrid graph to predict an interaction type between the first node and the second node. Furthermore, the system can process, using a graph neural network model, the predicted interaction type between the first node and the second node to predict a trajectory of the first object.

Abstract: Implementations relate to proactively aggregating client device data to append to client assistant data that is communicated to a server device in response to a user request to a client automated assistant. When a user request that is associated with, for example, a peripheral client device, is received at a client device, the client device can communicate, to a server device, data that embodies the user request (e.g., audio data and/or local speech recognition data), along with peripheral device data that was received before the client device received the user request. In this way, the client automated assistant can bypass expressly soliciting peripheral device data each time a user request is received at another client device. Instead, a peripheral device can proactively communicate device data to a client device so that the device data can be appended to request data communicated to the server device from a particular client device.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support reselection procedures for power saving in industrial internet of things (IIoT) operations. In a first aspect, a method of wireless communication includes a user equipment (UE) setting a reselection flag to a no-reselection state either by default or in response to a source cell signal-to-noise ratio (SNR) exceeding a predetermined maximum SNR. The UE can set the flag to the allow-reselection state in response to the target cell SNR range exceeding the source cell SNR range or the source cell SNR being below a predetermined minimum SNR. The UE may then either reselect to the target cell when appropriate or maintain service on the source cell based on the state of the reselection flag. Other aspects and features are also claimed and described.

Abstract: A luminaire including an outer support member having a plurality of LED arrays; a driver housing centrally located within an interior of the outer support member; a plurality of attachment arms extending from the outer support member and secured to the driver housing, resulting in open spaces between the attachment arms and between outer support member and the driver housing and a plurality of heat dissipating fins extending from an upper surface of the outer support member to the driver housing.

Abstract: A differential video rendering system, including a graphics processing unit (GPU); a graphical display coupled to the GPU; a video decoder configured to decode a bitstream of encoded data into a plurality of sets of decoded blocks; at least one processor configured to: generate, based on a first set of the plurality of sets of decoded blocks, a first differential video frame comprising a plurality of sets of differential regions, normalize each set of the plurality of sets of differential regions to a fixed size block to provide a normalized plurality of sets of differential regions, map a respective set of the normalized plurality of sets of differential regions to align with a respective tile size region of a plurality of tile size regions conforming with the GPU, generate a hierarchal region tree based on the normalized plurality of sets of differential regions mapped to the plurality of tile size regions, and generate a plurality of optimal regions based on the hierarchal region tree satisfying a predefin

Abstract: The present invention, includes: a damper unit provided between an airbag module that acts as a damper mass for a steering wheel and the steering wheel to dampen vibrations of the steering wheel, an engagement mechanism provided in the airbag module that maintains the airbag module on the steering wheel in a relatively displaceable manner, and protecting means provided on the airbag module for stopping external forces to the engagement mechanism in a non-engaged state with the steering wheel, that may cause deformation, and thereby protecting the engagement mechanism.

Abstract: A technical validation mechanism is described that includes the use of facial feature recognition and tokenization technology operating in combination with machine learning models can be used such that specific facial or auditory characteristics of how an originating script is effectuated can be used to train the machine learning models, which can then be used to validate a video or a particular dynamically generated passphrase by comparing overlapping phonemes or phoneme transitions between the originating script and the dynamically generated passphrase.

Abstract: Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.

Abstract: Methods and systems for generating a predictive HER2 score using machine learning models are disclosed. An example method generally includes identifying a plurality of nuclei and membrane segments in regions of interest in an input image using a first machine learning model. For the plurality of nuclei and membrane segments identified in the input image, a plurality of features are extracted and classified into one of a plurality of feature categories. Using a second machine learning model, a predictive HER2 score indicating the likelihood of whether a stained tissue sample captured in the input image is HER2 positive or HER2 negative is generated based on the classification assigned to the plurality of extracted features associated with the plurality of segments.

Abstract: Systems and methods provide for managing faulting network devices. A first network device can receive an error. The first network device can generate one or more frames including data indicative of the error. The first network device can broadcast the one or more frames to one or more neighboring network devices. It may be determined that the first network device is inaccessible. The first data can be retrieved and presented from a second network device among the one or more neighboring network devices. In some embodiments, a network management system can utilize the first data to generate a machine learning model that classifies whether network devices are instances of network devices designated for a Return Merchandise Authorization (RMA) process. In some embodiments, the network management system can apply the first data to a machine learning classifier to determine whether to initiate the RMA process for the first network device.

Abstract: Systems and methods that process, classify, and provide intelligent insights related to received documents such as notice documents in real-time. The system and methods leverage a novel framework of artificial intelligence and machine learning techniques to identify a requirement in the document (e.g., a government notice) and generate actionable suggestions thereto.

Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Abstract: There are provided systems and methods for determining processing weights of rule variables for rule processing optimization. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules for decision-making of data including real-time data in production computing environments. Rules may correspond to coded statements that perform an automated decision-making service for the computing services and platforms of the service provider. When writing rules, different variables for data objects may be utilized, where each variable may perform a different operation and/or utilize a different operation for fetching and retrieving data used during rule processing. Each variable may therefore have a different data processing weights based on processing requirements of the data. Thus, optimization of rule authoring may be performed by mapping variables to other similar variables and showing a processing weight of each variable.