Abstract:
The present invention is directed towards systems and methods for providing multi-level classification of a network packet. In some embodiments, network performance may be enhanced and optimized by providing QoS and acceleration engines with packet- or data-specific information. In addition to source and destination IP addresses and port numbers, packet- or data-specific information can include direction of traffic (client to host or server; server or host to client; or both), Virtual LAN (VLAN) ID, source or destination application or associated application, service class, ICA priority, type of service, differentiated service code point (DSCP), or other information. Some or all of this information may be used to classify the network packet at a plurality of layers of a network stack, allowing for deep inspection of the packet and multiple levels of granularity of classification.
Abstract:
A computing system includes a server comprising email policy rules to be applied to emails containing sensitive information, a mail server to provide the emails, and a client computing device enrolled with the server to access the mail server. An email privacy filter is to be applied to emails from the mail server intended for the client computing device. The email privacy filter interfaces with the server to receive the email policy rules therefrom. The email privacy filter identifies sensitive information within the email. The email privacy filter then applies the email policy rules, in response to identification of sensitive information within the email, to determine if the email is to be hidden from view on the client computing device so as to prevent display of the sensitive information to an unauthorized viewer.
Abstract:
Methods and systems for providing files of variable sizes based on device and/or network conditions are described herein. The system may determine a plurality of bandwidth ranges. The system may also determine a plurality of file classes, and each different file class may be associated with a different bandwidth range. In some scenarios, the system may convert a file into a plurality of modified files. Each modified file may have a different file size and correspond to a different file class. The file and/or modified files may be provided to other devices based on various factors, such as bandwidth, available storage space, and/or display capabilities of user devices.
Abstract:
Techniques are disclosed for integrated booking of rooms and media resources, such as display devices. An example methodology implementing the techniques includes responsive to an activation of an access Uniform Resource Locator (URL) on a computing device, receiving information associated with a booking of a room and a display device, generating a token for accessing the display device and providing to the computing device the token and an address of the display device. The method also includes, responsive to receiving the token from the display device, authenticating the token and, responsive to authenticating the token, allowing use of the display device.
Abstract:
Methods and systems for augmenting communications using input data from mobile devices are described herein. A computing device may establish a connection with a mobile device having one or more input devices. The computing device may display a barcode that, when scanned by a mobile device, causes the mobile device to access a web page. The web page may be configured to cause the mobile device to transmit, e.g., via a web browser executing on the mobile device and to the computing device, input data from the input devices. The input data may be used by the computing device to replicate a physical input device connected to the computing device. The computing device may transmit the input data to a different computing device.
Abstract:
Disclosed embodiments describe systems and methods for predicting health of a link. A device in communication with a link can identify profile information of a stream of network traffic traversing the link. The device can determine a first prediction of health of the link by applying one or more rules to the plurality of parameters of the profile information. The device can determine a second prediction of health of the link by applying a classifier to one or more timed sequences of the plurality of parameters of the profile information. The device can establishes a respective weight for each of the first prediction of health and the second prediction of heath. The device can select, using the respective weight, between the first prediction of health and the second prediction of health to provide a predictor of the health of the link.
Abstract:
Aspects of the disclosure relate to wrapping continuation tokens to support paging for multiple servers across different geolocations. An enterprise server may receive a first request for a plurality of records, and the first request for the plurality of records may request a quantity of records exceeding a page size limit. In response to receiving the first request, the enterprise server may retrieve a first set of records comprising a first number of records equal to the page size limit. The enterprise server may generate a first wrapped continuation token comprising location information identifying a geographic location of a first server where a next set of records is to be retrieved. Finally, the enterprise server may send, to the client device, the first set of records and the first wrapped continuation token, which may cause the client device to process the first set of records.
Abstract:
A computing system includes a client device and a form template server. The client device has a display associated therewith to display an application page from an application, and generate a screenshot of the form. The application page includes a form requiring data to be filled in by a user. The form template server compares a form template extracted from the screenshot to a private form template database for a match. The private form template database includes private form templates from different applications, with each private form template having user data associated therewith previously filled in for the user. The client device then populates the form on the display with the data from the matched private form template.
Abstract:
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property (1) Organization11111111111111111111111I1111111111111i1111liiiii International Bureau (10) International Publication Number (43) International Publication Date W O 2021/255484 Al 23 December 2021 (23.12.2021) W IPO I PCT (51) International Patent Classification: KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, G06F40/247(2020.01) G06F 40/30 (2020.01) MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, G06F 40/279 (2020.01) G06F40/232 (2020.01) OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (21) International Application Number: SC, SD, SE, SG, SK, SL, ST, SV, SY, TH, TJ, TM, TN, TR, PCT/GR2020/000030 TT, TZ, UA, UG, US, UZ, VC, VN, WS, ZA, ZM, ZW. (22) International Filing Date: (84) Designated States (unless otherwise indicated, for every 18 June 2020 (18.06.2020) kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (25) Filing Language: English UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, (26)PublicationLanguage: English EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, (71) Applicant: CITRIX SYSTEMS, INC. [US/US]; 851 West MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, Cypress Creek Road, Ft. Lauderdale, Florida 33309 (US). TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, (72) Inventor; and KM, ML, MR, NE, SN, TD, TG). (71) Applicant (for MG only): DOUNIS, Lampros [GR/GR]; Published: Patras Innovation Hub, 4 Kato-Ano Kastritsiou, 26504 Kas- - with international search report (Art. 21(3)) tritsi, Peloponnese (GR). (74) Agent: KILIMIRIS, Constantinos; Patrinos & Kilimiris, 7, Hatziyianni Mexi Str., 11528 Athens (GR). (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, I, IS, JO, JP, KE, KG, KH, KN, KP, (54) Title: AUTONOMOUS LEARNING OF ENTITY VALUES IN ARTIFICIAL INTELLIGENCE CONVERSATIONAL (57) Abstract: A computer system configured for autonomous learn 606 ing of entity values is provided. The computer system includes a 604A Server Environment memory that stores associations between entities and fields of re 600 Server ot Skill Serice sponse data. The computer system also includes a processor config 1Computer Sevce nH ured to receive a request to process an intent; generate a request to Fulfillment erm ) fulfill the intent; transmit the request to a fulfillment service; receive, / 602 1e cgent from the fulfillment service, response data specifying values of the Client Computer 608 fields; identify the values of the fields within the response data; iden tiHuman tifythe entities via the associations using the fields; store, within the Language Network API | memory, the values of the fields as values of the entities; and retrain 10eace 116 a natural language processor using the values of the entities. Natural Language Spellchecker T uProcessor 122 Thesaurus j 118 Service Sewer Ditinayr Computer Language 124 Model 604B 012 NLP System
Abstract:
At least one target hash value is generated for a target version of a Web product from contents of a Web page displayed by the target version of the Web product. The target hash value is compared to at least one corresponding baseline hash value generated from a Web page displayed by a baseline version of the Web product. A difference between the target hash value and the baseline hash value indicates a difference between a user interface of the target version of the Web product and a user interface of the baseline version of the Web product. The user interface of the target version of the Web product is generated in response to the comparison between the target hash value and the baseline hash value.