Aggregate analysis of text captures performed by multiple users from rendered documents

• US 8,515,816 B2
• Filed: 04/01/2005
• Issued: 08/20/2013
• Est. Priority Date: 02/15/2004
• Status: Active Grant

• Current Assignee: Kyocera Corporation
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: King, Martin T., Grover, Dale L., Kushler, Clifford A., Stafford-Fraser, James Q.
• Primary Examiner(s): ZURITA, JAMES H
• Application Number:
  US11/097,833
• Publication Number:
  US 20060036462A1
• Time in Patent Office: 3,063 Days
• Field of Search: 705/26, 705/37
• US Class Current: 705/26
• CPC Class Codes: G06F 16/316 Indexing structures
• CPC Class Codes: G06F 16/332 Query formulation
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 16/41 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/433 using audio data
• CPC Class Codes: G06F 16/434 using image data, e.g. images, photos, pictures taken by a user
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06F 16/9554 by using bar codes
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 40/117 Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06F 40/14 Tree-structured documents parsing G06F40/205; validation G06F40/226
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/174 Form filling; Merging
• CPC Class Codes: G06K 17/0016 : Selecting or retrieving of images by means of their associated code-marks, e.g. coded microfilm or microfiche microfilm reading G03B21/11; computers therefor G06F16/00; teaching devices G09B5/02; accessing record carriers G11B27/002; static digital stores using optical elements G11C13/04; intermediate storage of facsimile picture signals H04N1/21
• CPC Class Codes: G06Q 10/00 : Administration; Management
• CPC Class Codes: G06Q 10/10 : Office automation; Time management
• CPC Class Codes: G06Q 20/102 : Bill distribution or payments
• CPC Class Codes: G06Q 20/12 : specially adapted for electronic shopping systems
• CPC Class Codes: G06Q 20/4012 : Verifying personal identification numbers [PIN]
• CPC Class Codes: G06Q 30/00 : Commerce
• CPC Class Codes: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 : Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0241 : Advertisements
• CPC Class Codes: G06Q 30/0283 : Price estimation or determination
• CPC Class Codes: G06Q 30/04 : Billing or invoicing
• CPC Class Codes: G06Q 30/06 : Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0601 : Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0603 : Catalogue ordering
• CPC Class Codes: G06Q 30/0623 : Item investigation
• CPC Class Codes: G06Q 30/0641 : Shopping interfaces
• CPC Class Codes: G06Q 30/0643 : Graphical representation of items or shoppers
• CPC Class Codes: G06Q 40/04 : Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06V 30/10 : Character recognition
• CPC Class Codes: G06V 30/142 : using hand-held instruments; Constructional details of the instruments
• CPC Class Codes: G06V 30/1444 : Selective acquisition, locating or processing of specific regions, e.g. highlighted text, fiducial marks or predetermined fields
• CPC Class Codes: G06V 30/1448 : based on markings or identifiers characterising the document or the area
• CPC Class Codes: G06V 30/248 : involving plural approaches, e.g. verification by template match; Resolving confusion among similar patterns, e.g. "O" versus "Q" G06V30/242 takes precedence
• CPC Class Codes: G06V 30/262 : using context analysis, e.g. lexical, syntactic or semantic context
• CPC Class Codes: G06V 30/40 : Document-oriented image-based pattern recognition
• CPC Class Codes: G07F 17/40 : for devices for accepting orders, advertisements, or the like
• CPC Class Codes: G07G 1/00 : Cash registers alarm indicators G07G3/00
• CPC Class Codes: H04N 1/00204 : with a digital computer or a digital computer system, e.g. an internet server programmed control between transmitter and receiver or between image input and image output device H04N1/32561
• CPC Class Codes: H04N 1/00244 : with a server, e.g. an internet server fax-servers or the like for store and forward H04N1/324
• CPC Class Codes: H04N 1/00326 : with a data reading, recognizing or recording apparatus, e.g. with a bar-code apparatus arrangements for the associated working of recording or reproducing apparatus with related apparatus G11B31/00
• CPC Class Codes: H04N 1/0036 : Alphanumeric symbols
• CPC Class Codes: H04N 1/00363 : Bar codes or the like
• CPC Class Codes: H04N 1/00374 : on the same page as at least a part of the image
• CPC Class Codes: H04N 1/00381 : Input by recognition or interpretation of visible user gestures manual input means, e.g. digitisers, writing tablets H04N1/00392; gesture input to computers G06F3/017; recognition algorithms G06V40/20
• CPC Class Codes: H04N 1/107 : with manual scanning
• CPC Class Codes: H04N 1/2166 : for mass storage, e.g. in document filing systems information retrieval G06F16/00
• CPC Class Codes: H04N 1/2187 : with image input from a plurality of different locations or from a non-central location, e.g. from one or more users
• CPC Class Codes: H04N 1/32128 : attached to the image data, e.g. file header, transmitted message header, information on the same page or in the same computer file as the image for information embedded in the image H04N1/32144
• CPC Class Codes: H04N 1/34 : for coin-freed systems ; Pay systems
• CPC Class Codes: H04N 2201/0081 : Image reader H04N2201/0091 - H04N2201/0094 take precedence
• CPC Class Codes: H04N 2201/3215 : of a time or duration
• CPC Class Codes: H04N 2201/3249 : data relating to a linked page or object, e.g. hyperlink
• CPC Class Codes: H04N 2201/3253 : Position information, e.g. geographical position at time of capture, GPS data
• CPC Class Codes: H04N 2201/3263 : of a graphical motif or symbol, e.g. Christmas symbol, logo H04N2201/3211 takes precedence
• CPC Class Codes: H04N 2201/3266 : of text or character information, e.g. text accompanying an image H04N2201/3226 takes precedence
• CPC Class Codes: H04N 2201/3277 : The additional information being stored in the same storage device as the image data
• CPC Class Codes: H04N 2201/3278 : Transmission
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y10S 707/99931 : Database or file accessing
• CPC Class Codes: Y10S 707/99934 : Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99936 : Pattern matching access
• CPC Class Codes: Y10S 707/99943 : Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99945 : Object-oriented database structure processing

Abstract

A facility for analyzing text capture operation traffic is described. The system receives indications of operations for capturing text from rendered documents performed by a plurality of users. The system performs collective analysis on the received indications, and outputs a result produced by the analysis.

Claims

1. A method performed by a processor in a computing system for analyzing text capture operation traffic, the method comprising:

receiving, by the computing system, a plurality of indications of operations for capturing text from rendered documents, each of the plurality of the received indications specifying a text sequence captured as part of the indicated text capture sequence;

for each of the plurality of received indications, identifying, by the processor, an electronic document containing the captured text sequence;

performing, by the processor, collective analysis on the plurality of received indications and the electronic documents; and

outputting, by the processor, a result produced by the performed analysis.

2. The method of claim 1 wherein the outputted result is a list of documents organized in a particular manner.

3. The method of claim 1 wherein the collective analysis comprises identifying behavior patterns of a plurality of users, and wherein the outputted result indicates a plan for marketing to users among the plurality.

4. The method of claim 1 wherein the collective analysis comprises identifying most-frequently read documents, and wherein the outputted result identifies these most-frequently read documents.

5. The method of claim 4 wherein the outputting comprises displaying an identification of the most-frequently read documents in a web page.

6. The method of claim 4 wherein the outputting comprises including an identification of the most-frequently read documents in a marketing report.

7. The method of claim 4 wherein the outputting comprises presenting an identification of the most-frequently read documents to document publishers.

8. The method of claim 1 wherein the collective analysis comprises identifying most-frequently captured documents, and wherein the outputted result identifies these most-frequently captured documents.

9. The method of claim 8, further comprising:

receiving a search query;

generating a search result for the received search query, the search result comprising a plurality of items, at least a portion of the plurality of items being documents; and

before presenting the generated search result, organizing the generated search result in accordance with the capture frequencies of the documents among the items of the search result.

10. The method of claim 9, further comprising marketing additional documents directed to a distinguished one of the most popular topics.

11. The method of claim 8, further comprising:

receiving a request to present a list of documents; and

presenting the requested list of documents in an order consistent with the capture frequencies of the documents among the items of the search result.

12. The method of claim 1 wherein the collective analysis comprises determining a most popular set of topics.

13. The method of claim 12, further comprising:

publishing new rendered documents directed to one or more of the determined most popular topics.

14. The method of claim 12, further comprising:

informing a document publisher of the one or more determined most popular topics.

15. The method of claim 1 wherein the collective analysis comprises identifying most-frequently captured words, and wherein the outputted result solicits advertising subscriptions for one or more of the identified words from one or more potential advertisers.

16. The method of claim 1 wherein the collective analysis comprises determining a frequency of phrases captured by the text captures.

17. The method of claim 1 wherein at least a portion of the received indications indicate a geographic location at which the indicated text capture operation was performed, and wherein the collective analysis comprises determining a geographic distribution for captures from a distinguished document, and wherein the outputted result indicates the determined geographic distribution.

18. The method of claim 1 wherein at least a portion of the received indications indicate a time at which the indicated text capture operation was performed, and wherein the collective analysis comprises determining a chronological distribution for captures from a distinguished document, and wherein the outputted result indicates the determined chronological distribution.

19. The method of claim 1 wherein the collective analysis comprises determining a rate at which a distinguished document is being read, the method further comprising compensating an author of the distinguished document in accordance with the determined rate.

20. The method of claim 1 wherein the collective analysis comprises determining a number of users who complete a purchase after performing a capture from a distinguished document, the method further comprising compensating an author of the distinguished document in accordance with the determined number of users.

21. The method of claim 1 wherein the collective analysis comprises determining a number of users who perform a capture from an advertisement at a time proximate to performing a capture from a distinguished document, the method further comprising compensating an author of the distinguished document in accordance with the determined number of users.

22. The method of claim 1 wherein the collective analysis comprises analyzing interactions with a distinguished document, the method further comprising modifying the distinguished document based upon the analysis of interactions with the distinguished document.

23. The method of claim 22 wherein the collective analysis determines that the frequency of text captures from a distinguished portion of the distinguished document is lower than a pre-determined frequency, and wherein the distinguished document is modified by deleting the distinguished portion.

24. The method of claim 22 wherein the collective analysis determines that the frequency of text captures from a distinguished portion of the distinguished document is higher than a pre-determined frequency, and wherein the distinguished document is modified by expanding the distinguished portion.

25. The method of claim 22 wherein the collective analysis determines that the frequency of text captures from a distinguished portion of the distinguished document is higher than a pre-determined frequency, and wherein the distinguished document is modified by adding advertising to the distinguished document near the distinguished portion.

26. The method of claim 22 wherein the collective analysis determines that the frequency of text captures from a distinguished portion of the distinguished document is higher than a pre-determined frequency, and wherein the distinguished document is modified by revising advertising to the distinguished document near the distinguished portion.

27. The method of claim 1 wherein the collective analysis is performed on indications of text capture operations performed by all of the users of the plurality.

28. The method of claim 1 wherein the analysis is performed on a proper subset of the plurality of received indications of operations.

29. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yields a text sequence that is a proper subset of text contained in a single line of a rendered document.

30. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yields a text sequence that is a proper subset of text contained in a single page of a rendered document.

31. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the yielded text sequence is comprised of words, and wherein the text capture operation involved specific interactions with each of the words of the yielded text sequence.

32. The method of claim 31 wherein one or more of the text captures comprises receiving the yielded text sequence via words of the yielded text sequence spoken into a microphone of a hand-held text capture device.

33. The method of claim 31 wherein one or more of the text captures comprises receiving via an optical sensor of a hand-held text capture device each of the words of the yielded text sequence.

34. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the yielded text sequence is comprised of ordered words, and wherein the text capture operation involved capturing physical phenomenon corresponding to each of the words of the yielded text sequence in the order of the yielded text sequence.

35. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the yielded text sequence is comprised of ordered words, and wherein the text capture operation involved capturing physical phenomena corresponding to each of the words of the yielded text sequence in the reverse order of the yielded text sequence.

36. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the text capture operation involved manually moving an optical sensor across the rendered document.

37. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the text capture operation involved capturing image data from a nonrectangular region of the rendered document.

38. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the text sequence yielded by the text capture operation comprises fewer than ten words.

39. The method of claim 1 wherein at least one of the text capture operations indicated by a received indication yielded a text sequence, and wherein the text capture operation involved capturing an image of fewer than ten words.

40. The method of claim 1, wherein at least one of the plurality of received indications indicates a text capture operation performed with a wireless telephone handset.

41. One or more computer memories storing machine executable instructions which when executed by a computer cause the computer to:

store information formatted in a communal text capture data structure, the communal text capture data structure comprising a plurality of entries, each entry indicating a text sequence captured from a rendered document; and

use the contents of the communal text capture data structure to analyze aggregate text capture behavior.

42. The computer memories of claim 41 wherein each indicated text sequence constitutes a proper subset of text appearing on a page of a rendered document.

43. One or more computer memories storing machine executable instructions which when executed by a computer cause the computer to:

store information formatted in a data structure, the data structure comprising one or more records, each record relating to a different rendered document and indicating a result of a performed analysis of a plurality of indications of text capture operations within the rendered document to capture a sequence of text from the rendered document, the result indicating a frequency of text capture operations from the rendered document.

44. The computer memories of claim 43, wherein each record contains information related to one or more documents identified by the result.

45. The computer memories of claim 43, wherein the result indicates a frequency of text capture operations from a distinguished location in a distinguished rendered document.

46. A method, performed by a processor in a computing system, of making a recommendation, the method comprising:

in response to a first distinguished text capture operation to capture a sequence of text from a distinguished rendered document, determining, by the processor, a result that identifies an electronic document containing the captured sequence of text;

comparing, by the processor, the determined result to a group of text capture operations; and

identifying, by the processor, an action to be performed based on the comparison.

47. The method of claim 46, wherein the comparison determines that the distinguished rendered document has been captured from and that a second distinguished text capture operation has been performed, the identified action comprising:

identifying the distinguished rendered document.

48. The method of claim 46, wherein the comparison determines that the distinguished rendered document has been captured from by the distinguished user and has been captured from by a second distinguished one of the users in the group of users, the identified action comprising:

identifying a distinguished user associated with the second distinguished text operation.

49. A system for determining a result based on data capture operations, comprising:

a memory containing instruction components;

a processor that executes instructions stored in the memory, including;

a text capture component that receives indications of operations for capturing text from rendered documents by users of hand-held data capture devices, each received indication specifying a text sequence captured as part of the indicated text capture operation;

an analysis component that determines a result based on the received indications, the result determined from an analysis of a total of two or more indications of data captures;

an output component that outputs the result.