EP2715525A2 - Méthode et système de consommation assistée par ordinateur d'informations provenant de fichiers de données d'applications - Google Patents

Méthode et système de consommation assistée par ordinateur d'informations provenant de fichiers de données d'applications

Info

Publication number
EP2715525A2
EP2715525A2 EP12789674.4A EP12789674A EP2715525A2 EP 2715525 A2 EP2715525 A2 EP 2715525A2 EP 12789674 A EP12789674 A EP 12789674A EP 2715525 A2 EP2715525 A2 EP 2715525A2
Authority
EP
European Patent Office
Prior art keywords
highlighting
document
user
users
segments
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP12789674.4A
Other languages
German (de)
English (en)
Other versions
EP2715525A4 (fr
Inventor
Indu M. Anand
Anurag WAKHLU
Pranav ANAND
Ishan Anand
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority claimed from PCT/US2012/039482 external-priority patent/WO2012162572A2/fr
Publication of EP2715525A2 publication Critical patent/EP2715525A2/fr
Publication of EP2715525A4 publication Critical patent/EP2715525A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/166—Editing, e.g. inserting or deleting
    • G06F40/169—Annotation, e.g. comment data or footnotes

Definitions

  • Such use of the marked-up material is one example of "crowd-based intelligence" in its simplest form.
  • Prior readers of academic texts mark the important sections of the document, which leaves later readers with the advantage of more-easily identifying those and other important sections in a particular text. Some readers may choose to ignore a prior reader's notations because the new reader either disagrees with the importance of that highlight or underline, or because that reader is more concerned with another area of the text more pertinent to the particular interest of that reader at that particular time.
  • the repeated marking of important sections by other readers will continually identify further areas of the document that are of interest to the readers. Continued review and marking may lead to multiple highlighting of the same section or area of the document. Multiple markings by multiple viewers will, in turn, indicate to the reader the importance of that section or area of the document for other readers for any specific purpose, and may lead to a reader more closely attempting to comprehend that section.
  • "crowd-based intelligence" model forms the backbone of a method and a system in which a number of reviewers are able to highlight, edit and review content materials and share their highlights, reviews and edits with other users. This method and system operate so that as more users identify important file sections, the more the later users benefit from their collective, synthesized insights.
  • iv. Provide a tool box of, and system support for, commands and actions, which allow the user to quickly generate a coarse (broad level) identification of pages, paragraphs or passages of significance, or to specify the precise, "finer level” identification of segments; these commands/actions may be implemented with graphic icons;
  • Coarse page/paragraph passage/etc. identifiers may be "color coded" or otherwise differentiated to signify parameter differentiation, for example, the significance level, nature of comment etc. Sound/Audio:
  • the editor reading a given document, or listening to an audio file or viewing an image or video file may mark-up an important segment, and may share what the editor views as "important" with all other users according to a definition of "important" or "significant” that is published to editors, consumers of content and other users.
  • the highlighting of the data file may be accomplished in some embodiments by simply selecting the appropriate material and submitting it, either through express commands or implicitly as a result of marking-up. The editor may also have the opportunity to add further comments with or without sharing the highlighted segments.
  • editor could also rate the comments. This would be considered in the calculation of comment relevance.
  • editors could also comment on comments, to further levels of recursion.
  • the combination is made using a matrix.
  • the system would refer to a stored matrix to determine how the highlight will appear to the reader. If the two editors mark the same selection identically, then the matrix calculation may tell the system to display the highlight in their common, preset way. If the two editors disagree in classifying the highlighted section, then the matrix would calculate which of the two distinct highlights will be displayed (and how), or calculate and display a third classification synthesized from the two distinct classifications. This may be accomplished in several ways, including providing different weights to their respective highlights, optionally in combination with reverse-diagonal calculations from the matrix illustrated in Figure 11.
  • the general method of using weights would allow, as a special case, for the integration of one editor's highlights into the collection of highlights created by previous editors.
  • the weight for the collectivized highlighting would generally be higher than that of a single editor because it represents the collective opinions of many. In most cases, if a single editor adds a highlight that agrees with the collective, the weight of the collective increases. If the editor disagrees, then the weight decreases. As the weight increases or decreases through integration of the highlights by several editors, the display to the user reflects the change in the relevance or meaning of a highlight.
  • an editor's expertise level is provided by the editor himself or herself.
  • the system may include a registration system where the editor enters the fields of their expertise. This may be compared to the category of document the editor is marking-up. For example, if the editor's profile indicates a Ph.D. in Physics T for their edits of an article in the Physics category, the system may recognize them as an expert.
  • the editor could also indicate his or her expertise in the sub-field of the document (nuclear reactor, e.g.). Thus, an expert with a Ph.D. in Physics may claim is no expertise in nuclear reactors.
  • Another embodiment allows a user to select their role as either a reader or an editor.
  • read mode the user acts as a reader and views the document with accompanying highlights. In that mode the reader can add his or her own comments or highlights for their own personal use, but may choose not to share them with others.
  • the reader can also view a list of all comments for each highlight in a separate pane of the display. The reader may be allowed at any time to switch to "editor/reviewer mode,” where they can add highlights with any accompanying comments, and then share them with other users.
  • LDA Latent Dirichlet Allocation
  • LDA LDA
  • the system when an editor implements a new comment, the system would create a probabilistic vector for the new comment. By then multiplying the vector with already existing comment vectors, the system can compare the similarity of comments. If the product is within a certain threshold, the system will group the comments together. The grouped comments may allow a reader to see what the "crowd" believes is the significance of a highlighted segment. For example, if multiple editors labeled a segment as "important,” “vital,” “essential” and “fundamental,” then the system would group these together. This would make the purpose of the highlighting immediately recognizable by the reader. This is particularly useful when the highlighted segments are long, complicated or unwieldy.
  • the editor may input connections between highlights. This could be in the form of comments, for example, that allow the editor to specify references to multiple highlights. For instance, if the underlying content was a legal case, an editor could highlight a segment where the court recites the facts and then connect it to a segment where the court applies those facts to the rule of law. These connections may also allow a reader to track certain aspect of a document, such as a particular character or theme.
  • the central idea behind this invention is that a consumer of information will benefit from the knowledge and expertise of all previous consumers of same or similar information, guided by their collective intelligence as to the most important sections of the data file, thereby improving consumption efficiency and comprehension, and user experience.
  • the parameters may also include the amount of time a reader wishes to spend on a document ⁇ or the number of words he wishes to read. Other such parameters may also be introduced and employed. For example, a reader may wish to see the sections of a document above a specified level of relevance compared to another document previously perused by the same reader.
  • a paper document that has highlights physically written on it could be scanned into the system.
  • the system would convert the scanned image into one it could read, and then convert it to the same format as similar documents.
  • the same concept can be applied to inputting audio, visual, or audiovisual works into the system.
  • the system allows editors to compare two or more documents through highlighting.
  • This comparative highlighting allows a reader to efficiently view the differences and similarities of the documents. This could be useful, for example, if presented with two long contracts from two different cell phone companies. Much of the boiler-plate may be the same, but comparative highlighting allows a reader to quickly see the differences in key-terms, allowing him or her to make a more informed decision without having to wade through the complex legal terms. This could also be accomplished automatically by the system.
  • the system could produce a tabulation of the key differences in a chart or spreadsheet.
  • Coarse identification of related segments may be particularly useful for relating of the objects/segments across data files.
  • FIG. 1 illustrates the basic structure of the invention.
  • a document When a document is retrieved by the server, it first checks to ensure that it is in the proper format, and converts accordingly. Then if the document has not been reviewed before, it is sent to the review process. If it has been previously reviewed, then the user can either choose to read or review the document themselves. The reader may also use a combination of the two processes. In this Read/Review process, the user can seamlessly switch between the editor and reader roles, which are described in detail below.
  • FIG. 2 demonstrates the review process.
  • the user may first retrieve a document in 10, he or she may first enter their level of expertise etc., 20, or proceed straight to editing the document, 30.
  • the editor may then mark-up segments and add comments to the document, which will be saved and sent to the server periodically, 40.
  • the user is asked if they want to submit the document for integration with the collective document, 50. Depending on the user input it can go to box 60, Submit for integration, or 70, Review or revise document.
  • the system may also calculate an Expert Level rating, when submitted.
  • the highlights and comments will be uploaded and available to all other users.
  • the editor may also publish the mark-ups at any time during his or her session.
  • the system displays the stored document with its associated highlights and comments, 25.
  • the reader may then enter parameters at 27, to customize the mark-ups displayed. This may include only mark-ups from users above a specified Expert Level Threshold at 28, highlights and comments from a particular subject, 29, or a certain number of mark-ups that will allow the reader to read the document at a particular speed, or within a set number of words at 30.
  • system backend When the system backend receives a request for a document, in addition to retrieving the document itself, it also acquires the associated integrated highlights and comments from the
  • the "relevance" level of overlapping highlighting in this case is determined by a simple reverse- diagonal algorithm. Also, this example assumes that equal weightage is given to the one user's highlighting as the collective, which generally will not be true.
  • tag refers to a qualitative descriptor of a segment of a document.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • User Interface Of Digital Computer (AREA)
  • Document Processing Apparatus (AREA)

Abstract

L'invention concerne un procédé et un système de collecte et d'agrégation de données générées par des utilisateurs en fonction de paramètres produits par les utilisateurs et de mesures de pertinence du contenu sous-jacent. Des données d'utilisateur sont combiné à des données collectives déjà existantes pour générer des balises pertinentes pour un document ou un autre fichier de données consommable, audio ou vidéo par exemple. La version balisée du document ou du fichier de données est ensuite affichée aux utilisateurs pour, inter alia, permettre d'augmenter l'efficacité et aider à la compréhension.
EP12789674.4A 2011-05-24 2012-05-24 Méthode et système de consommation assistée par ordinateur d'informations provenant de fichiers de données d'applications Withdrawn EP2715525A4 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201161519578P 2011-05-24 2011-05-24
US201161572826P 2011-07-23 2011-07-23
PCT/US2012/039482 WO2012162572A2 (fr) 2011-05-24 2012-05-24 Méthode et système de consommation assistée par ordinateur d'informations provenant de fichiers de données d'applications

Publications (2)

Publication Number Publication Date
EP2715525A2 true EP2715525A2 (fr) 2014-04-09
EP2715525A4 EP2715525A4 (fr) 2016-05-18

Family

ID=50185365

Family Applications (1)

Application Number Title Priority Date Filing Date
EP12789674.4A Withdrawn EP2715525A4 (fr) 2011-05-24 2012-05-24 Méthode et système de consommation assistée par ordinateur d'informations provenant de fichiers de données d'applications

Country Status (1)

Country Link
EP (1) EP2715525A4 (fr)

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7779347B2 (en) * 2005-09-02 2010-08-17 Fourteen40, Inc. Systems and methods for collaboratively annotating electronic documents
US7925993B2 (en) * 2006-03-30 2011-04-12 Amazon Technologies, Inc. Method and system for aggregating and presenting user highlighting of content
JP2008278088A (ja) * 2007-04-27 2008-11-13 Hitachi Ltd 動画コンテンツに関するコメント管理装置

Also Published As

Publication number Publication date
EP2715525A4 (fr) 2016-05-18

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