Acronis International Gmbh

Parallels International Gmbh

Parallels IP Holdings Gmbh

Parallels Holdings, Ltd.

Acronis Inc.

Swsoft Holdings, Ltd.

Parallels Software International, Inc.

Other

Plesk International Gmbh

Ingram Micro Inc.

Constructor Technology Ag

Constructor Education and Research Genossenschaft

Swsoft Holding, Ltd.

Parallels

Acronis Interna Banal Gmbh

Serguei M Beloussov

Nikolay Dobrovolskiy

Stanislav Protasov

Alexander G Tormasov

Stanislav S Protassov

Maxim V Lyadvinsky

Alexey Koryakin

Vladimir Strogov

Andrey A Omelyanchuk

Mark Shmulevich

Anton Zelenov

Yuri Per

Kirill Korotaev

Ivan Korobov

Nikolay Grebennikov

Sergey Ulasen

Andrey Kulaga

Maxim V Tsypliaev

Paul Gafa

Alexander Grechishkin

Alexey Sergeev

Alexey Dod

Alexey Kostyushko

Anatoly Stupak

Liubov Kulakova

Oleg Melnikov

Vyacheslav Levchenko

Anton Enakiev

Oleg Ishanov

Ruslan Sadovnikov

Sanjeev Solanki

Vasily Semyonov

Konstantin Ozerkov

Sergey Pachkov

Oleg P Volkov

Cláudio Rodrigues

Andrey Naenko

Pavel Emelyanov

Daniel Farrugia

Alexey Petrukhin

Dmitry Chepel

Joseph Portelli

Marco Borg

Andrey Zaitsev

Maxim Kudrin

Alexander Aplemakh

Dmitry Martynov

Eugene Aseev

Maxim V Goldobin

Victor Lysenko

Iurii Ovchinnikov

Maxim Kuzkin

Dennis Lunev

Andrey Redko

Juri V Tsibrovski

Grigory Nikolaenko

Anastasia Pereberina

Victor Shulga

Igor Kornachev

Vladimir Simonov

Denis Lamtsov

Alexey A Ruslyakov

Igor E Korshunov

Anton Bobykin

Mark A Will

Dennis S Dyatlov

Yuri Pudgorodsky

Candid Wüest

Alexey Borodin

Oleg Shaikhatarov

Vladimir Miroshkin

Mikhail M Lavrentiev

Dmitry Egorov

Yury Averkiev

Mark Smulevich

Vitaly Pogosyan

Mikhail A Ershov

Pavel Semenov

Evgeny A Aseev

Ilya Baimetov

Kailash Sivanesan

Hiten Gajjar

Prabhuraj Reddy

Elena A Koryakina

Vladimir Grebenschikov

Alexey Kuznetzov

Matthew Cini Sarreo

Alexey Kutuzov

Alexander Andreev

Michael Joseph Toutonghi

Michael Chadwick

Alain Marie Patrice Gentilhomme

Denis Lunev

Igor Marnat

Alexey G Kobets

Andrey Stupachenko

Disclosed herein are systems and method efficiently executing a program operation on a cloud-based service. In an exemplary aspect, a method comprises receiving a request to perform a program operation on a cloud-based service and at least one user constraint for performing the program operation, and determining a plurality of sub-operations that are comprised in the program operation. The method comprises identifying a plurality of service component combinations offered by the service provider that can execute the program operation, and identifying at least one processing constraint of each service component. The method comprises determining a service component combination from the plurality of service component combinations for executing the program operation based on the at least one user constraint and processing constraints. The method comprises executing the program operation by the determined service component combination.

Systems and methods for optimized execution of program operations on cloud-based services based on processing constraints

Disclosed herein are systems and method for determining environment dimensions based on landmark detection, the method including: training, with a dataset including a plurality of images featuring an environment and labelled landmarks in the environment, a neural network to identify the labelled landmarks in an arbitrary image of the environment; receiving an input image depicting the environment; generating an input tensor based on the received input image; inputting the input tensor into the neural network, wherein the neural network is configured to generate an output tensor including a position of each identified landmark and a visibility score associated with each position; calculating a homography matrix between each position in the output tensor along a camera plane and a corresponding position in an environment plane, based on a pre-built model of the environment; and outputting an image that visually connects each landmark along the environment plane based on the homography matrix.

Systems and methods for determining environment dimensions based on landmark detection

The subject matter of this specification can be implemented in, among other things, a method that includes storing first client information identifying a first client device, identifying a location of the first client device, and identifying a first remote access session that provides the first client device access to resources of the remote access system. The method further includes receiving access information identifying an access device and a user account associated with the first client information. The access information indicates that the user account has been successfully authenticated by the access device. The method further includes, in response to the receipt of the access information, causing the first client device to connect to the first remote access session with the remote access system as a background process of the client device while a user interface of the client device remains locked.

Connecting to remote access session based on proximity of mobile device

Systems and methods for optimized execution of program operations on cloud-based services

Systems and methods for processing virtual machine V/O requests by virtualization extension modules. An example method comprises receiving, by a virtual machine monitor (VMM) running on a host computer system, a request initiated by a virtual machine managed by the VMM; processing the request by a VMM extension module identified by one or more parameters of the request; invoking, by the VMM extension module, an application programming interface (API) exported by the VMM to perform an action identified by the request; and resuming execution of the virtual machine.

Virtualization extension modules

Disclosed herein are systems and method for customizing a user workspace environment using artificial intelligence-based analysis. In one exemplary aspect, a method comprises detecting user actions in a user workspace environment that provides access to a plurality of workspace elements. The method comprises generating a plurality of rules for customizing the user workspace environment, wherein each rule links a set of input parameters of a machine learning algorithm as criteria with an output user action and an output identifier of the workspace element, and wherein each rule assigns at least one customization action that (1) reduces an amount of steps to perform in the user workspace environment to access the workspace element associated with the output identifier and (2) reduces a processing time to perform the output user action. When a criterion is fulfilled, the method comprises executing a corresponding customization action that alters the user workspace environment.

Systems and methods for customizing a user workspace environment using A.I-based analysis

Disclosed herein are systems and method for synthetic file scanning. In one exemplary aspect, a method may comprise, for each respective file in a plurality of files on a storage device, determining a respective likelihood of the respective file being targeted by the malicious activity, and including, in a subset of files, each respective file in the plurality of files with a respective likelihood that is greater than a threshold likelihood. The method may comprise for each respective file in the subset of files, identifying at least one fragment of the respective file that is susceptible to the malicious activity. The method may comprise extracting and storing at least one fragment from each respective file in a synthetic file, and scanning the synthetic file periodically instead of the plurality of files.

Systems and methods for synthetic file scanning

The subject matter of this specification can be implemented in, among other things, a method that includes determining, by a first computing device, a set of remote applications hosted by a second computing device including file type associations for each remote application. The first computing device determines a set of local applications and their file type associations. The each file type associated with a remote application, the first computing device determines whether the file type is also associated with a local application. Responsive to determining that the file type is associated with both a remote and local application, the first computing device configures a proxy component with parameters specifying the remote application and the local application. The proxy component is configured to determine whether to open a requested file of the file type with the remote application or the local application based upon the current client/server environment.

Method to determine use of local and remote applications in a distributed multiuser environment for shared file resources

Systems and methods for file sharing over secure connections. An example method comprises: receiving a client request identifying a file sharing host and a file residing on the file sharing host; establishing a secure client connection; responsive to identifying a management connection with the file sharing host, transmitting an identifier and a parameter of the secure client connection via the management connection; receiving a host request to establish a secure host connection, the host request comprising the identifier of the secure client connection; establishing the secure host connection using the parameter of the secure client connection identified by the received identifier; forwarding, over the secure host connection, a first data packet received over the secure client connection, the first data packet comprising at least part of the client request; and forwarding, over the secure client connection, a second data packet received over the secure host connection, the second data packet comprising at least part of the file identified by the client request.

File sharing over secure connections

Disclosed are systems and methods for data archiving using machine learning techniques. The system collects statistical information and event data and processes them using machine learning techniques to classify data and/or predict data access demands. The system receives statistical information related to user access of a plurality of files, which can effectively 'train' the system to archive data that is not needed at a certain moment and extract it at other moments. The system identifies, using a machine learning module, a pattern of access in the plurality of files based on the received statistical information. The system modifies, using the identified pattern of access, a threshold value related to file access, and assigns a set of files from the plurality of files an access classification based on the modified threshold value. The system migrates the set of files between hot and cold data areas based on the assigned access classification.

System and method for data classification using machine learning during archiving

A new timer object that is associated with a timer event may be identified. A virtual processor that is to be provided the timer event that is associated with the new timer object may be determined. One or more current timer objects that are associated with one or more timer events provided to the virtual processor may also be identified. A new element for the new timer object may be created in a list identifying the one or more current timer objects that are associated with the one or more timer events that are provided to the virtual processor.

Timer object management for a multiprocessor virtual environment

Disclosed are systems and methods for managing notifications from published applications. A method for managing notifications from one or more published applications includes detecting, by a server, a notification from the one or more published applications launched by a user at a client device, forming a notification packet containing information from the notification, and causing the notification packet to be provided to the client device.

System and method for managing notifications from published applications

Disclosed herein are systems and methods for securing cloud meetings using containers. In one aspect, an exemplary system comprises, a device comprising a processor, an OS operable in a user mode and a kernel mode, and a kernel driver for performing operations while in kernel mode, the kernel driver having a kernel driver interceptor configured to: register for a process notification callback for user applications used for web-based meetings, monitor to determine when a process notification callback is received, receive a process notification callback and a command line in the callback, and analyze and transmit the command line to a service that secures the meeting, wherein the securing is performed by: configuring a container for executing the user application in an isolated virtual environment, transferring, to the container, all resources needed to run the user application, and executing the user application in the container.

Systems and methods for securing cloud meetings using containers

A method for creating a collection with optimized family-specific signatures for protecting from malware includes collecting statistics of potential signatures for chosen sample attribute vectors, the statistics of potential signatures being collected for clean files and malware files, estimating a probability to find a potential signature in the clean files, grouping malware files with the same signature in clusters (families), choosing the most optimal signature for the malware family files based on a predefined target function, and exporting a collection with optimized family-specific signatures configured to be implemented by scan engines.

Optimal antimalware signatures database

The present disclosure relates to a system and method for creating a backup and restoring the exact clean system state prior to malware detection. The system includes a security system, in communication with one or more applications of a computing system, and a backup unit. The security system detects malware during execution of the applications or events based on a memory dump analysis. The backup unit creates a backup copy of the system state corresponding to each event, labels each copy and creates an index. When the security system detects presence of the malware at a particular event, the backup system parses the index, and with use of the labels, retrieves the exact backup copy that belongs to the event preceding the other event that caused the malware attack.

Exact restoration of a computing system to the state prior to infection

Growing community of inventors

Singapore, Singapore

Serguei M Beloussov