Abstract: Disclosed is a method for generating a prediction model. The model can be used in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include extracting event features from event codes and structuring the event features into feature vectors. A first dimension of a first feature vector corresponds to a first event feature, and a second dimension of the first feature vector corresponds to a second event feature. The method can also include generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.

Abstract: Generating expanded responses that guide continuance of a human-to computer dialog that is facilitated by a client device and that is between at least one user and an automated assistant. The expanded responses are generated by the automated assistant in response to user interface input provided by the user via the client device, and are caused to be rendered to the user via the client device, as a response, by the automated assistant, to the user interface input of the user. An expanded response is generated based on at least one entity of interest determined based on the user interface input, and is generated to incorporate content related to one or more additional entities that are related to the entity of interest, but that are not explicitly referenced by the user interface input.

Abstract: The present disclosure relates to systems, devices and methods for receiving biosensor data acquired by a medical device, e.g., relating to glucose concentration values, and controlling the access and distribution of that data. In some embodiments, systems and methods are disclosed for monitoring glucose levels, displaying data relating to glucose values and metabolic health information, and controlling distribution of glucose data between applications executing on a computer, such as a smart phone. In some embodiments, systems and methods are disclosed for controlling access to medical data such as continuously monitored glucose levels, synchronizing health data relating to glucose levels between multiple applications executing on a computer, and/or encrypting data.

Abstract: The present disclosure relates to novel methods for preparing antibacterial aminoglycoside compounds and the compounds used in such preparations.

Abstract: Methods and apparatuses related to federated search of multiple sources with conflict resolution are disclosed. A method may comprise obtaining a set of data ontologies (e.g., types, properties, and links) associated with a plurality of heterogeneous data sources; receiving a selection of a graph comprising a plurality of graph nodes connected by one or more graph edges; and transforming the graph into one or more search queries across the plurality of heterogeneous data sources. A method may comprise obtaining a first data object as a result of executing a first search query across a plurality of heterogeneous data sources; resolving, based on one or more resolution rules, at least the first data object with a repository data object; deduplicating data associated with at least the first data object and the repository data object prior to storing the deduplicated data in a repository that has a particular data model.

Abstract: Described are systems and methods that enable secure real time communication (“RTC”) sessions that may be used, for example, for editing and movie production. Client devices may interact with an RTC management system to collaborate on different files retained on the different client devices, without the files having to be uploaded from the client device on which it is stored. In addition, on-going multifactor authentication may be performed for each client device of an RTC session during the RTC session and/or video authentication may be used to grant access into an RTC session. Still further, to improve the quality of the exchanged video information and to reduce transmission requirements, in response to detection of events, such as a pause event, a high resolution image of a paused video may be generated and sent for presentation on the display of each client device, instead of continuing to stream a paused video.

Abstract: Methods and apparatus for producing virtual concerts or other online collaborative events. One or more musicians or other presenters or contributors at disparate geolocations transmit continuums of audio and/or image data as well as synchronization data continuums. Digital processing may be used to superimpose musicians or other presenters or contributors onto a virtual background (such as a concert stage or other virtual environment). Audio and video feeds from multiple musicians or other presenters or contributors may be synchronized to give the appearance of each being on the same stage in a 2D, 3D or virtual reality (VR) environment. Multiple continuums of audio data may be transmitted at different speeds and quality in order to allow musicians, presenters or other contributors to remain synchronized while a remote production studio receives high quality data transmissions for generation of a combinative, immersive, multi-dimensional end user audio/visual experience.

Abstract: Examples described herein relate to a network interface device that performs: offloading processing of fragments of a packet to an accelerator; processing non-fragmented packets; and prioritizing dropping of fragments of the packet over dropping of non-fragmented packets. Offloading processing of fragments of the packet to the accelerator can include: the accelerator performing: reassembling the fragments of the packet into a first reassembly packet; and based on congestion associated with at least one of the fragments of the packet of the first reassembly packet: dropping fragments of the first reassembly packet associated with one or more flows; halting reassembly of the first reassembly packet; and forwarding a second packet to a host system, wherein the second packet indicates that congestion occurred, identifies one or more impacted flows, and indicates a number of dropped packet fragments.

Abstract: A method for performing a key exchange between a first device, a second device, and an intermediary device, wherein the intermediary device: transmits to the first device a first secret symbol string over a first quantum channel and a first basis set over a first channel, transmits to the second device a second secret symbol string over a second quantum channel and a second basis set over a second channel, and generates a third symbol string by combining the first and second secret symbol strings and transmits data representative of the third symbol string to the second device, whereby the first and second devices perform a quantum key exchange and sifting based on the first and second secret symbol strings and first and second basis sets, and a fourth set of symbols is generated by the second device by combining the second secret symbols with the third symbol string.

Abstract: Disclosed are systems and methods for providing automated or semi-automated technical support for patients using medical devices, such as continuous glucose monitoring systems. Disclosed embodiments of automated tech support system include collection and storage of copies of streams of medical device data on multiple servers, analysis and comparison of data streams, remote tech support initiation and usage of the automated tech support system for providing improved products and services by storing and analyzing historical tech support data.

Abstract: Systems and methods for application performance management across one or more networks are disclosed. A system includes a plurality of geographically distributed computing devices executing one or more applications. A plurality of collectors are distributed across the one or more networks, each collector being positioned proximate a respective computing device. The collectors may sample, by each of the plurality of collectors, a plurality of trace events received from the applications executing on the computing devices proximate the collector. The collectors may retain in memory sampled trace events that meet a configurable attribute. The collectors may use probabilistic sets and tail-based sampling to determine root events and policies for identifying relevant traces. Benefits can be achieved in bandwidth savings, network cost and cyber security.

Abstract: The disclosed computer-implemented method may include accessing various antenna elements and identifying parameters for an antenna that is to be formed using the accessed antenna elements. The method may also include assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include generating, using a machine-learning model of a computing device, a set of antenna designs. The method may also include tokenizing, by the computing device, each antenna design in the generated set of antenna designs. Additionally, the method may include predicting, by the machine-learning model of the computing device, a frequency response for each tokenized antenna design. Furthermore, the method may include comparing, by the computing device, the frequency response for each tokenized antenna design. Finally, the method may include selecting, by the computing device based on the comparison, an antenna design that meets a performance threshold for the frequency response. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A disclosed computer-implemented method may include (1) receiving (A) a set of design specifications associated with an antenna performance characteristic, (B) a set of requirements for an antenna architecture, (C) a parameterization that describes parameters of an antenna structure, and (D) a set of bounds for the parameterization, and (2) determining, based on the set of design specifications, the set of requirements, the parameterization, and the set of bounds, and in accordance with a global optimization algorithm and a local tuning algorithm, a design for the antenna architecture. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.

Abstract: The disclosed computer-implemented method may include accessing various antenna elements and identifying parameters for an antenna that is to be formed using the accessed antenna elements. The method may also include assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A consumer is identified at a transaction terminal. A natural-language voice chatbot is initiated for the transaction. The chatbot is configured with store information for a store associated with the terminal and, optionally, with preferences registered to the consumer. A natural language voice dialogue using, at least the store information, is initiated between the chatbot and the consumer while the consumer is at the terminal and during the transaction. The consumer may order store items from the store and pay for the items through the dialogue. In an embodiment, the items ordered are delivered to the terminal where the consumer is located.

Abstract: Disclosed is a method for generating a prediction model. The model can be used in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include extracting event features from event codes and structuring the event features into feature vectors. A first dimension of a first feature vector corresponds to a first event feature, and a second dimension of the first feature vector corresponds to a second event feature. The method can also include generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.

Abstract: Disclosed is a computer-implemented method for generating a prediction model. The model can be for use in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include: extracting event features from event codes and structuring the event features into feature vectors; and generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters being assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.