IP Library Granted Patent US 9,086,825
Granted Patent B2
US 9,086,825 · App. 13/747,827 · Granted Jul 21, 2015

Providing supplemental content based on a selected file

Inventors: Georgia Koutrika (Palo Alto, CA); Qian Lin (Palo Alto, CA); Jerry J. Liu (Palo Alto, CA)
Assignee: Hewlett-Packard Development Company, L.P.
G06F3/1203G06F3/1242G06F3/1244G06F3/1285G06Q30/0207G06Q30/0251G06Q30/0282
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Quick Facts
Patent No.
US 9,086,825
App. No.
13/747,827
Granted
Jul 21, 2015
Kind
B2
Abstract

A system can include a print type classifier to receive a print message that indicates that a file has been selected to be printed. The print type classifier can also determine a print type of the file. The system can also include a print content analyzer to assign a category to the file characterizing a topic of the file based on the content of the file. The print type analyzer can also generate a content representation of the file that characterizes a summary of content of the file. The system can further include a recommendation engine to provide supplemental content based on at least one of the print type, the category and the content representation of the file, wherein the supplemental content is in a printable format.

Claims (46)

1. A non-transitory computer readable medium having machine executable instructions comprising:

a print type classifier to:

receive a print message that indicates that a file has been selected to be at least one of printed and stored; and

determine a print type of the file;

a print content analyzer to:

assign a category to the file characterizing a topic of the file based on the print type of the file; and

generate a content representation of the file that characterizes a summary of content of the file; and

a recommendation engine to provide supplemental content based on at least one of the print type, the category and the content representation of the file.

2. The non-transitory computer readable medium of claim 1 , wherein the content representation of the file is generated based on the category of the file.

3. The non-transitory computer readable medium of claim 1 , wherein the print type characterizes a reason for printing the file.

4. The non-transitory computer readable medium of claim 3 , wherein the print type of the file is archive, read-it-later or use-it-later.

5. The non-transitory computer readable medium of claim 1 , wherein the print content analyzer comprises:

a print content classifier to assign the category to the file based on at least one of a source of the file that is included in the print message and content of the file; and

a print content extractor to generate the content representation of the file, wherein the content representation of the file includes at least one of a keyword or a phrase determined based on at least one of the content and the source of the file.

6. The non-transitory computer readable medium of claim 5 , wherein the print content classifier and the print content extractor are to operate in concert to refine the category of the file and the content representation of the file.

7. The non-transitory computer readable medium of claim 1 , wherein the recommendation engine comprises:

a print type contextual filter to set a context of the supplemental content, wherein the context of the supplemental content includes a combination of item types for the supplemental content and the context of the supplemental content includes a factor for selecting the supplemental content, wherein the factor is based on the print type of the file and the factor comprises at least one of a time constraint, a location constraint, a recommendation strategy and a layout constraint;

a category contextual filter to:

assign a context category to each item type included in the context of the supplemental content to generate a combination of categories; and

assign a category score to each context category in the combination of categories;

a candidate recommender to:

retrieve material based on the context of the supplemental content to generate a plurality of content candidates;

assign a relevancy score to each candidate in the plurality of content candidates based on the print type, category and content representation of the file; and

select a subset of the plurality of content candidates to generate the supplemental content.

8. The non-transitory computer readable medium of claim 7 , wherein the category score is based on a business relationship of a provider of material corresponding to a category in the combination of categories.

9. The non-transitory computer readable medium of claim 7 , wherein the subset of the plurality of content candidates is selected to achieve a level of diversity between the plurality of content candidates in the subset of the plurality of candidates.

10. The non-transitory computer readable medium of claim 9 , wherein the supplemental content is printable on a single page.

11. The non-transitory computer readable medium of claim 1 , wherein the supplemental content is in a printable format.

12. The non-transitory computer readable medium of claim 1 , wherein the supplemental content includes at least two of an advertisement, a map, an article and a coupon.

13. A method comprising:

determining a print type of a file that has been selected to be at least one of: printed and stored, wherein the print type of the file characterizes a reason that the file has been selected to be printed;

assigning a category to the file characterizing a topic of the file based on the print type of the file;

generating a content representation of the file that characterizes a summary of content of the file, wherein the content representation comprises a keyword and a phrase extracted from the content of the file;

providing supplemental content based on the print type, the category and the content representation of the file; and

storing the supplemental content in a memory.

14. The method of claim 13 , wherein the supplemental content comprises at least two of an advertisement, a map, an article and a coupon that are each related to the content of the file.

15. A system comprising:

a memory resource to store machine readable instructions; and

a processing resource to execute the machine readable instructions, the machine readable instructions comprising:

a print type classifier to:

receive a print message that includes a source of a file that has been selected to be printed; and

determine a print type of the file, wherein the print type characterizes a reason for printing the file;

a print content analyzer comprising:

a print content classifier to assign a content category to the file that characterizes a topic of the file, wherein the content category is assigned based on at least one of the source of the file and content of the file; and

a print content extractor to generate a content representation of the file that characterizes a summary of content of the file, wherein the content representation of the file includes at least one of a keyword or a phrase included in the content of the file; and

a recommendation engine to provide supplemental content related to the content of the file.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 24, 2013
From: KOUTRIKA, GEORGIA; LIN, QIAN; LIU, JERRY J.
To: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.
Reel/Frame 029684/0059 →
Continuity (1)
Related Publication 20140204423A1 · Jul 24, 2014