Detecting so-called “navigation bars” (or “nav bars”) in a (Web) document by determining whether or not nodes of a parse tree of the (Web) document are “anchor-heavy”. Generally, a navigation bar can be thought of as text, such as a hyper-text link or anchor text for example, without any immediate content. Once a navigation bar is detected, objectionable navigation bars (i. e. , navigation bars, the rendering of which would be objectionable to users without special re-authoring), can be distinguished from non-objectionable navigation bars (i. e. , navigation bars which would not be objectionable to users with no special re-authoring). Objectionable navigation bars may be distinguished from non-objectionable navigation bars by: (a) determining whether the navigation bar is so small that normal rendering would not be objectionable; (b) determining whether the navigation bar presumably conveys meaningful content; and/or (c) determining whether the navigation bar is a component of a non-objectionable navigation bar (where all components of the non-objectionable navigation bar are navigation bars themselves).
Daniel Dulitz - Mountain View CA, US Sanjay Ghemawat - Mountain View CA, US Keith H. Randall - Mountain View CA, US Anurag Acharya - Campbell CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 15/173
US Classification:
709226
Abstract:
A method of distributing files operates in a system having a master and a plurality of slaves, interconnected by a communications network. Each slave determines a current file length for each of a plurality of files and sends slave status information to the master, the slave status information including the current file length for each file. The master schedules copy operations based on the slave status information. The master stores bandwidth capability information indicating data transmission bandwidth capabilities for the resources required to transmit data between the slaves, and also stores bandwidth usage information indicating a total allocated bandwidth for each resource. For each schedule copy operation, an amount of data transmission bandwidth is allocated and the stored bandwidth usage information is updated accordingly. The master only schedules copy operations that do not cause the total allocated bandwidth of any resource to exceed the bandwidth capability of that resource.
Duplicate Document Detection In A Web Crawler System
Daniel Dulitz - Mountain View CA, US Alexandre A. Verstak - San Jose CA, US Sanjay Ghemawat - Mountain View CA, US Jeffrey A. Dean - Menlo Park CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 12/00 G06F 17/30
US Classification:
707203, 707 1, 707 2, 707 5, 707100
Abstract:
Duplicate documents are detected in a web crawler system. Upon receiving a newly crawled document, a set of documents, if any, sharing the same content as the newly crawled document is identified. Information identifying the newly crawled document and the selected set of documents is merged into information identifying a new set of documents. Duplicate documents are included and excluded from the new set of documents based on a query independent metric for each such document. A single representative document for the new set of documents is identified in accordance with a set of predefined conditions.
System For Reclassification Of Electronic Messages In A Spam Filtering System
Daniel Wesley Dulitz - Mountain View CA, US Seth Golub - Sunnyvale CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 15/16
US Classification:
709206, 709224, 709207, 7071041
Abstract:
A method for indicating probability of spam for email comprises tracking network traffic characteristics for the email, and comparing the tracked characteristics for the email to characteristics for email from trusted or known spam sources.
Representative Document Selection For Sets Of Duplicate Documents In A Web Crawler System
Daniel Dulitz - Mountain View CA, US Alexandre A. Verstak - San Jose CA, US Sanjay Ghemawat - Mountain View CA, US Jeffrey A. Dean - Menlo Park CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00 G06F 17/30
US Classification:
707736, 707737, 707758
Abstract:
Duplicate documents are detected in a web crawler system. Upon receiving a newly crawled document, a set of documents, if any, sharing the same content as the newly crawled document is identified. Information identifying the newly crawled document and the selected set of documents is merged into information identifying a new set of documents. Duplicate documents are included and excluded from the new set of documents based on a query independent metric for each such document. A single representative document for the new set of documents is identified in accordance with a set of predefined conditions.
Karen Padham Taylor - Los Gatos CA, US Manish Gupta - Santa Clara CA, US Daniel Dulitz - Mountain View CA, US Steve Okamoto - San Jose CA, US Rajas Moonka - San Ramon CA, US Susan Wojcicki - San Ramon CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06Q 30/00
US Classification:
705 144, 705 11
Abstract:
A query including one or more keywords is received. An advertisement associated with the one or more keywords is identified, and display data for displaying the advertisement is generated and a determination is made as to whether the advertisement is associated with a video. Video selection data is generated for displaying the video proximate to the advertisement if the advertisement is determined to be associated with the video, and the display data and the video selection data are provided.
Representative Document Selection For Sets Of Duplicate Documents In A Web Crawler System
Daniel Dulitz - Mountain View CA, US Alexandre A. Verstak - San Jose CA, US Sanjay Ghemawat - Mountain View CA, US Jeffrey A. Dean - Menlo Park CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00 G06F 17/30
US Classification:
707736, 707737, 707741, 707748
Abstract:
Duplicate documents are detected in a web crawler system. Upon receiving a newly crawled document, a set of documents, if any, sharing the same content as the newly crawled document is identified. Information identifying the newly crawled document and the selected set of documents is merged into information identifying a new set of documents. Duplicate documents are included and excluded from the new set of documents based on a query independent metric for each such document. A single representative document for the new set of documents is identified in accordance with a set of predefined conditions.
Trusted Participants Of Social Network Providing Answers To Questions Through On-Line Conversations
Emily K. Moxley - San Francisco CA, US Josh T. Wills - San Francisco CA, US Daniel Dulitz - Mountain View CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 17/00
US Classification:
705 714
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for facilitating online conversation. In one aspect, a method includes determining that a user has submitted a question to be viewed by trusted participants of an online conversation, wherein the trusted participants include the user, one or more advertisers and one or more social network friends of the user, obtaining a reply to the question from one of the trusted participants, and providing the reply to the trusted participants.
Name / Title
Company / Classification
Phones & Addresses
Daniel Dulitz President
BRAINDANCE FOUNDATION Membership Organizations, Nec, Nsk
548 Market St, San Francisco, CA 94104 501 Silverside Rd, Wilmington, DE 19809
Daniel Dulitz
El Centuron, LLC Real Estate Management
548 Market St, San Francisco, CA 94104 917 Alma St, Palo Alto, CA 94301 3995 Page Ml Rd, Los Altos, CA 94022
Daniel Dulitz Principal
Spinbox Mill, LLC Manage Residential Real Estate · Real Estate · Business Services at Non-Commercial Site · Nonclassifiable Establishments
181 Ctr St, Mountain View, CA 94041 3995 Page Ml Rd, Los Altos, CA 94022
Googleplus
Daniel Dulitz
Lived:
Mountain View, CA South Dakota Austin, TX New York City Ithaca, NY State College, PA
Work:
Google - Product Manager (2006-2012) Google - Software Engineer (2000-2006) Motorola - Software Engineer
Education:
Cornell University
Tagline:
Go fast and hang on tight.
Youtube
Introducing Google Squared
Product Manager Daniel demonstrates Google Squared, a new experimental...
Category:
Science & Technology
Uploaded:
03 Jun, 2009
Duration:
37s
Google I/O 2012 - Getting Started with Google...
Timothy Jordan, Daniel Dulitz Google+ history presents new opportuniti...
Duration:
33m 56s
Mat 6 132 Carson Dulitz Team Kansas Vs Mateo ...
2019 16U Cadet Duals - Mat 6 132 Carson Dulitz Team Kansas Vs Mateo De...
Duration:
4m 8s
LISZT Rminiscences de Don Juan - Daniel Hsu -...
Performed May 26, 2017 at the Fifteenth Van Cliburn International Pian...
Duration:
17m 44s
10U 67 Kamari Magana HotShots Wrestling Vs No...
2019 Bigfoot Battle - 10U 67 Kamari Magana HotShots Wrestling Vs Noah ...
Duration:
2m 29s
High School (11th - 12th Grade) 138 Carson Du...
2019 Preseason Nationals - High School (11th - 12th Grade) 138 Carson ...
Duration:
5m 12s
High School (11th - 12th Grade) 138 Carson Du...
2019 Preseason Nationals - High School (11th - 12th Grade) 138 Carson ...
Duration:
4m 22s
Mat 5 126 Riley Davis Oregon Red Vs Carson Du...
2019 16U Cadet Duals - Mat 5 126 Riley Davis Oregon Red Vs Carson Duli...
In a post to Google's webmaster blog, group product manager Daniel Dulitz notes that the updated +1 button code also allows inline annotations--the presentation of pictures of Google+ friends--on pages that have implemented the +1 button.