Verizon Patent and Licensing Inc.

Richter Gedeon Vegyeszeti Gyar Rt

Egyt Gyogyszervegyeszeti Gyar

Biogal Gyogyszergyar

Human Oltoanyagtermelo Es Kutato Intezet

Horvath, Istvan

Istvan Horvath

Andrew W Herson

Csaba Rekeczky

Domonkos Huszar

Laszlo Szporny

Kent W Ryhorchuk

Nick G Suizo

Brian Refsdal

Christopher Kennedy

Akos Kiss

Tibor Gyimesi

Egon Karpati

Gyorgy Hajos

Sandor Gorog

Lajos Kisfaludy

Gyorgy Fekete

Peter Aranyi

Lajos Toldy

Jozsef Reiter

Katalin Csomor

Tibor Lang

Laszlo Pongo

Geza Szilagyi

Jozsef Toth

Sandor Holly

Miklos Low

Attila Szemzo

Laszlo Tvos

Janos Sagi

Andras Horvath

Miklos Nagy

Janos Nagy

Anna Boor Nee Mezei

Anna Szabolcs Nee Borbas

Csaba Molnar

Felician Benda

Peter Ruzsa

Laszlo Sztankov

Zoltan Riedl

Aniko Naray

Gabor Erdosi

Maria Peredy Nee Kajtar

Csilla Rethati

Istvan Koczka

Pal Ivan

Dora E Babicz

Ildiko Fritzsche Nee Lukacs

Tamas Somorai

Judit Gyorgyi Nee Edelenyi

Raymund Machovich

Gellert Nacsa

Gabor Majoros

Anna E Csorgo

Balint Magyar

Mark Szabo

Techniques described herein provide for the use of a heterogeneous artificial intelligence/machine learning ('AI/ML') architecture, in which relatively complex AI/ML, techniques may be used in conjunction with more lightweight AI/ML techniques in order to leverage the accuracy of relatively complex AI/ML techniques with the reduced processing power and/or time requirements of more lightweight AI/ML techniques. A teacher model system may utilize processing resource and/or time-intensive AI/ML techniques and/or models in order to determine classifications associated with source data, and may provide such classifications to a student model system that may utilize the classifications in accordance with less processing resource and/or time-intensive AI/ML techniques in order to accurately classify sensor data in real time or near-real time.

Systems and methods for domain-specific enhancement of real-time models through edge-based learning

A device may receive a video and corresponding camera information associated with a camera that captured the video, and may select an object in the video and a wire model for the object. The device may adjust an orientation, location, or size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model. The device may identify the object in another frame of the video, and may align the adjusted wire model on the object in the other frame. The device may interpolate the adjusted wire model for the object for intermediate frames of the video between the first and other frames, and may generate three-dimensional annotations for the video based on the adjusted wire models. The device may train a machine learning model based on the three-dimensional annotations.

Systems and methods for generating three-dimensional annotations for training a machine learning model

A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Identification of a poorly parked vehicle and performance of a first group of actions to cause one or more other devices to perform a second group of actions

Identification and performance of an action related to a poorly parked vehicle

A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll. Subsequent to the installation of the video cameras using the calculated geo-location parameters, the system may verify accuracy of detection of the service area, and update the regression model.

Machine learning-based device placement and configuration service

A device may receive initial registration data identifying initial camera parameters associated with a camera device provided at a location, and may receive location data associated with the location captured by the camera device. The device may receive map data identifying a map image of the location, and may transform the initial registration data into estimated camera parameters. The device may process the location data, with the estimator model, to generate extracted data, and may process the estimated camera parameters, the extracted data, and the map data, with a parameter optimization model, to identify camera parameters for the camera device. The device may provide the camera parameters to the camera device to cause the camera device to be configured based on the camera parameters.

Systems and methods for utilizing modeling to automatically determine configuration parameters for cameras

The invention relates to (E)-5-(2-bromovinyl)-uridine and its derivatives of general formula VI, ##STR1## wherein R.sup.1 stands for a hydrogen atom, C.sub.1-8 alkanoyl group, benzoyl group or a benzoyl group substituted in para position either with a C.sub.1-4 alkyl group or a halogen atom, and a process for preparing them by brominating 2',3',5'-tri-O-acyl-5-ethyl-uridine of general formula IV, ##STR2## wherein R is identical with R.sup.1, except where R.sup.1 stands for a hydrogen atom, dehydrohalogenating the resulting dibromo compound of general formula V ##STR3## and optionally deacylating it. The resulting compounds of general formula VI are exhibiting significant potency against Herpes simplex virus species and are of remarkably low acute toxicity.

Process for the preparation of 5-(2-bromovinyl)-uridine

The invention relates to acylated 1,2,4-triazole derivatives of general formula I ##STR1## wherein R.sup.1 represents an unsubstituted phenyl group or a phenyl group substituted by one or more halogen atoms, lower alkyl, alkoxy, acyloxy, hydroxy, amino, azido, nitro, trifluoromethyl, lower alkylthio, alkylsulfinyl, or alkylsulfonyl group, and

Antiviral acylated 1,2,4-triazole derivatives

The invention relates to new, pharmaceutically active copolymers with heparin-like activity, pharmaceutically acceptable salts thereof and a process for their preparation. The new copolymers comprise the units of the formula I ##STR1## wherein X stands for hydrogen or methyl and Y is hydrogen, units of the formula III ##STR2## optionally units of the formula II ##STR3## wherein Z' is hydrogen, and chain terminating units, formed from the units of the formulae I, III and optionally II under the conditions of copolymerization, in a statistical arrangement, and the pharmaceutically acceptable salts thereof, which contain, in addition to the above chain-members, units of the formulae IV and/or V ##STR4## wherein A is a cation/ satisfactorily replace the organogenic heparin, or give a synergistic combination with that,

Pharmaceutically active copolymers, process for their preparation and

New .DELTA..sup.1,3,5 -3-chloro-pregnane derivatives of the general formula (I), ##STR1## wherein X is hydrogen atom, acetyl group or a chloroacetyl group, and

3-Chloro-pregnane derivatives and a process for the preparation thereof

Adjuvant particles are prepared from lipoid-soluble substances, and compositions comprising the particles and biologically active substances adsorbed thereon are provided. The adjuvant systems are capable of binding a wide variety of biologically active substances, and can be used in vivo and in vitro as well.

Process for preparing lipoid-biologically active particles

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Budapest, Hungary

Istvan Horvath