Source: https://patents.google.com/patent/US9507782B2/en
Timestamp: 2019-06-16 11:39:20
Document Index: 514056614

Matched Legal Cases: ['§371', 'art 1', 'art 2', 'art 1', 'art 1', 'art 1']

US9507782B2 - Dynamic content preview - Google Patents
Dynamic content preview Download PDF
US9507782B2
US9507782B2 US13/814,654 US201213814654A US9507782B2 US 9507782 B2 US9507782 B2 US 9507782B2 US 201213814654 A US201213814654 A US 201213814654A US 9507782 B2 US9507782 B2 US 9507782B2
US13/814,654
US20140052741A1 (en
2012-08-14 Application filed by Empire Technology Development LLC filed Critical Empire Technology Development LLC
2012-08-14 Assigned to EMPIRE TECHNOLOGY DEVELOPMENT LLC reassignment EMPIRE TECHNOLOGY DEVELOPMENT LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KURABAYASHI, SHUICHI, TAKANO, KOSUKE, YOSHIDA, NAOFUMI
2012-08-14 Priority to PCT/US2012/050726 priority Critical patent/WO2014027999A1/en
2013-02-27 Assigned to EMPIRE TECHNOLOGY DEVELOPMENT LLC reassignment EMPIRE TECHNOLOGY DEVELOPMENT LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KURABAYASHI, SHUICHI, TAKANO, KOSUKE, YOSHIDA, NAOFUMI
2014-02-20 Publication of US20140052741A1 publication Critical patent/US20140052741A1/en
2016-11-29 Publication of US9507782B2 publication Critical patent/US9507782B2/en
This Application is a U.S. National Stage filing under 35 U.S.C. §371 of PCT Application Ser. No. PCT/US12/50726 filed on Aug. 14, 2012. The disclosure of the PCT Application is hereby incorporated herein by reference in its entirety.
Markets for electronic contents such as, for example, electronic books, digital videos, digital audios, games, etc. have experienced phenomenal growth in the last few years. Electronic book purchases, via an online bookstore, are growing in proportion relative to the purchase of physical books. The same is true regarding the purchase of music files via an online music shop.
To promote sales of the electronic contents, content providers provide previews for the electronic contents. For example, the content providers allow potential purchasers to read first few pages of an electronic book, or to listen first few seconds of a digital song.
In an example, a content preview system may include a content feature analysis unit configured to analyze one or more content features of a content file, and a preview type determination unit configured to determine a preview type for the content file based at least in part on the content features analyzed by the content feature analysis unit.
In another example, a content server may include a database configured to store at least one of a content file, content information associated with the content file, and a purchase history of a user; a content feature analysis unit configured to analyze one or more content features of the content file, an information extraction unit configured to extract from the database at least one of the content information associated with the content file and the purchase history of the user; and a preview type determination unit configured to determine a preview type for the content file based at least in part on at least one of the content features analyzed by the content feature analysis unit, the content information extracted by the information extraction unit, and the purchase history extracted by the information extraction unit.
In yet another example, a method performed under control of a content preview system may include analyzing one or more content features of a content file, and determining a preview type for the content file based at least in part on the analyzed content features.
FIG. 1 schematically shows an illustrative example of an environment in which a content server provides an electronic device with a dynamic content preview, arranged in accordance with at least some embodiments described herein;
FIG. 2 shows a schematic block diagram illustrating an example architecture of a content preview system, arranged in accordance with at least some embodiments described herein;
FIG. 3 shows a schematic block diagram illustrating an example architecture of a content server, arranged in accordance with at least some embodiments described herein;
FIG. 4 shows an example flow diagram of a process for providing a dynamic content preview, arranged in accordance with at least some embodiments described herein;
FIG. 5 illustrates an example computer program product that may be utilized to provide a dynamic content preview, arranged in accordance with at least some embodiments described herein; and
FIG. 6 is a block diagram illustrating an example computing device that may be utilized to provide a dynamic content preview, arranged in accordance with at least some embodiments described herein.
This disclosure is generally drawn, inter alia, to methods, apparatuses, systems, devices, and computer program products related to a preview of an electronic content file such as, for example, an electronic book (i.e., e-book).
Technologies are generally described for generating a preview of a content file based at least in part on content features of the content file.
In some examples, a user may access an on-line electronic content store such as, for example, an electronic book store, to buy and/or download electronic contents such as, for example, an electronic book. A content provider may provide the user with previews of the electronic contents to promote sales of the electronic contents. The content store and/or the content provider may operate a content server including a database on which the electronic contents may be stored.
In some examples, a content preview system, which may be a component of the content server or be separate from, but still controlled by, the content server, may determine a preview type appropriate for a content file based at least in part on one or more content features of the content file. By way of example, but not limitation, when the content file is an electronic book, the content features of the content file may indicate whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and/or whether the electronic book is a specialty publication.
In some examples, the content preview system may select the preview type at least among a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book, and/or a shaded view of every page of the electronic book, based at least in part on the content features.
By way of example, but not limitation, when the electronic book has a story structure, the content preview system may determine a preferred preview type for the content file to be the beginning of the electronic book. By way of example, but not limitation, when the electronic book has a dictionary-like structure, the content preview system may determine a preferred preview type for the content file to be a portion of every page of the electronic book such as, for example, a preview of a left-side or right-side portion of every page of the electronic book. By way of example, but not limitation, when the electronic book is a specialty publication, the content preview system may determine a preferred preview type for the content file to be a preview of a portion of sentences in the electronic book.
In some examples, the content preview system may determine an amount of the content file to be previewed to the user based at least in part on content information associated with the content file, such as customer review information regarding the content file and sales information regarding the content file, and/or a purchase history of the user. By way of example, but not limitation, the content preview system may reduce the amount of content previewed to a user as customer reviews of the content file increase. By way of example, but not limitation, the content preview system may increase the amount of content previewed as sales of the content file decrease. Further still, by way of example, but not limitation, the content preview system may increase the amount of content previewed as the user's purchases of other content files increase.
Then, in some examples, the content preview system may generate a preview of the content file based at least in part on the determined preview type and the amount of content to be previewed, and transmit the generated preview to the content server of the content store or provider. Then, the content server may transmit the preview content to a device of the user, so that the user may see the preview to determine whether to buy or not the content file.
FIG. 1 schematically shows an illustrative example of an environment in which a content server provides an electronic device with a dynamic content preview, arranged in accordance with at least some embodiments described herein.
As depicted, a content server 100 may provide content files to an electronic device 110 via a network 120. By way of example, but not limitation, content server 100 may be managed or operated by a content provider. Examples of electronic device 110 may include, but are not limited to, a tablet, a smartphone, a mobile phone, a personal digital assistant (PDA), an electronic book reader, a media player, a game console, a laptop computer, a desktop computer, or any other devices being able to access content server 100. Examples of network 120 may include, but are not limited to, a cellular network or a mobile network, a local area network (LAN), a backbone network, a metropolitan area network (MAN), a wide area network (WAN), a virtual private network (VPN), an internetwork, etc. Examples of the content files may include, but are not limited to, an electronic book file, an audio book file, a video file, a music file, a game file, etc.
For ease of explanation, the content file will be assumed as an electronic book hereinafter. However, those skilled in the art will recognize that the embodiments described herein may not be limited to the implementation of generating a preview of an electronic book, but be applied to any kind of electronic content file.
In some embodiments, when a user of electronic device 110 shows interest in one of electronic books stored in or provided by content server 100, and/or requests to view a preview of the electronic book, a content preview system may generate the preview of the electronic book. The content preview system may be a component of content server 100, or be separate from but still controlled by content server 100.
In some embodiments, the content preview system may analyze one or more content features of the electronic book. By way of example, but not limitation, the content features of the electronic book may indicate whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and/or whether the electronic book is a specialty publication.
In some embodiments, the content preview system may partition the electronic book into respective book elements (e.g., parts, chapters, pages, sentences, words, images and/or tables). In some embodiments, when the electronic book has been rendered by a desktop publishing (DTP) scheme, the content preview system may partition the electronic book using already available text strings. In some embodiments, when the electronic book has been rendered by scanning a paper book, the content preview system may partition the electronic book using digitized text strings extracted by using an optical character recognition (OCR) scheme.
In some embodiments, the content preview system may detect change of a frequency of occurrence of a word throughout the electronic book, and determine a story property of the electronic book based at least in part on the change of frequency of occurrence of the word. The story property may be defined as whether the electronic book has a story structure and/or to what extent the electronic book has such a story structure. By way of example, but not limitation, if the frequency of occurrence of at least a predetermined portion of words in the electronic book increases or decreases as the story develops from beginning to end of the electronic book, the electronic book may be considered as having a story structure. Table 1 below illustrates example frequency of occurrence of words in the electronic book.
Part 1 Part N
Word (beginning) Part 2 . . . (end)
Word 1 22% 15% . . . 5%
Word 2 5% 0% . . . 11%
In such cases, the content preview system may first detect the words the portion of which is greater than a predetermined threshold in Part 1 (i.e., in the beginning of the electronic book). By way of example, but not limitation, when the predetermined threshold is 20%, the content preview system may detect some words including Word 1. The content preview system may then determine whether the frequency of occurrence of each of the detected words decreases as the story develops to Part N (i.e., the end of the electronic book), such as Word 1 in Table 1. Then, the content preview system may calculate a portion of words in the electronic book, the frequency of occurrence of which decreases from Part 1 to Part N. Similarly, the content preview system may calculate a portion of words in the electronic book, the frequency of occurrence of which increases from Part 1 to Part N. Then, the content preview system may use the calculated portions as an indicator for the story property of the electronic book.
In some embodiments, the content preview system may calculate a ratio between text and images in the electronic book and/or a ratio between text and tables in the electronic book, and determine a separability property of the electronic book, which may be defined as a degree of importance of the respective book elements. By way of example, but not limitation, if the images and/or tables take up more than a predetermined portion of the electronic book as in an art book or a statistical book, the images and/or tables may be considered as important portions of the book. In such cases, the content preview system may determine the separability property of the electronic book based at least in part on a portion of images and/or tables in the electronic book.
In some embodiments, the content preview system may detect a repeated structure throughout the electronic book, and determine the separability property of the electronic book based thereon. By way of example, but not limitation, when the electronic book has a dictionary-like structure, the electronic book may have a repeated structure of a headword and a short description thereof. In such cases, the content preview system may determine the separability property of the electronic book based at least in part on an extent of appearance of the repeated structure.
In some embodiments, the content preview system may detect a frequency of occurrence of technical terms in the electronic book, and determine a specialty property of the electronic book based thereon. By way of example, but not limitation, the content preview system may compare the words in the electronic book with terms in glossaries in the fields of several areas of specialties, and determine the specialty property of the electronic book based at least in part on a portion of the words that match the terms in the glossaries.
Table 2 below illustrates example content features analyzed by the content preview system. Although the values of the respective properties are defined in the range of 0% to 100% in Table 2, those skilled in the art will recognize that the values may be scaled in a different way depending on the desired implementation.
Story Separability Specialty
Book ID property property property
Book 1 100 10 20
Book 2 20 95 60
In some embodiments, the content preview system may determine a preview type for the content file based at least in part on the analyzed content features. By way of example, but not limitation, the content preview system may select the preview type at least among a preview of a beginning of the electronic book (e.g., beginning n % of the electronic book), a preview of a portion of every page of the electronic book (e.g., a left-side m % margin of every page of the electronic book), a preview of a portion of sentences in the electronic book (e.g., every s % of sentences in the electronic book), a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book and a shaded view of every page of the electronic book, as shown in FIG. 1. Table 3 below illustrates example weight values for selecting the preview type appropriate for the content features.
Preview Story Separability Specialty
type property property property
Beginning n % 1.0 0.1 0.1
Left-side m % 0.1 1.0 0.0
s % of sentences 0.1 0.5 0.9
Mosaic view 0.5 0.5 0.5
Embossed view 0.1 0.9 0.8
Shaded view 0.7 0.5 0.1
In some embodiments, the content preview system may determine the preview type by calculating relevancies for respective candidate preview types based at least in part on the values of the story property, separability property and specialty property in Table 2, and the weight values in Table 3. In such cases, the content preview system may calculate the relevancies using cross product of a matrix of the weight values in Table 3 and a vector of the values of the story property, separability property and specialty property in Table 2. By way of example, but not limitation, for Book 1 illustrated in Table 2, the relevancy for the beginning of the electronic book may be calculated as 103 (100×1.0+10×0.1+20×0.1), the relevancy for the portion of every page of the electronic book may be calculated as 20 (100×0.1+10×1.0+20×0.0), the relevancy for the portion of sentences in the electronic book may be calculated as 33 (100×0.1+10×0.5+20×0.9), the relevancy for the mosaic view may be calculated as 65 (100×0.5+10×0.5+20×0.5), the relevancy for the embossed view may be calculated as 35 (100×0.1+10×0.9+20×0.8), and the relevancy for the shaded view may be calculated as 77 (100×0.7+10×0.5+20×0.1). Since the relevancy for the beginning of the electronic book is the highest in this example, the content preview system may determine a preferred preview type for Book 1 to be the preview of the beginning of the electronic book.
In some embodiments, the content preview system may extract from a database content information associated with the content file including customer review information regarding the content file and sales information regarding the content file and/or a purchase history of the user. The database may be a component of content server 100, or be separate from but still controlled by content server 100.
Then, in some embodiments, the content preview system may determine an amount of content to be previewed (e.g., values of n, m and s in the above example), based at least in part on the content information and/or the purchase history. By way of example, but not limitation, the content preview system may reduce the amount of content to be previewed as customer reviews for the content file increase. By way of example, but not limitation, the content preview system may increase the amount of content to be previewed as sales of the content file decrease. By way of example, but not limitation, the content preview system may increase the amount of the content file to be previewed as the user's purchases of other content files increases. In some alternative embodiments, the content preview system may set the amount of preview as a constant value regardless of the content information and/or the purchase history.
Then, in some embodiments, the content preview system may generate a preview of the content file based at least in part on the determined preview type and amount of the content file to be previewed, for transmission to electronic device 110.
FIG. 2 shows a schematic block diagram illustrating an example architecture of a content preview system, arranged in accordance with at least some embodiments described herein.
As depicted, a content preview system 200 may include a content feature analysis unit 210, a preview type determination unit 220, an information extraction unit 230 and a preview generation unit 240. Although illustrated as discrete components, various components may be divided into additional components, combined into fewer components, or eliminated while being contemplated within the scope of the disclosed subject matter.
Content feature analysis unit 210 may be configured to analyze one or more content features of a content file such as, for example, an electronic book. By way of example, but not limitation, the content features of the electronic book may indicate at least one of whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and whether the electronic book is a specialty publication.
In some embodiments, content feature analysis unit 210 may partition the electronic book into book elements (e.g., parts, chapters, pages, sentences, words, images and/or tables), and determine at least one of a story property, a separability property and a specialty property of the electronic book based at least in part on the book elements. By way of example, but not limitation, content feature analysis unit 210 may detect change of a frequency of occurrence of a word throughout the electronic book, calculate at least one of a ratio between text and images in the electronic book and a ratio between text and tables in the electronic book, detect a repeated structure throughout the electronic book, and/or detect a frequency of occurrence of technical terms in the electronic book.
Preview type determination unit 220 may be configured to determine a preview type for the content file based at least in part on the content features analyzed by content feature analysis unit 210. In some embodiments, preview type determination unit 220 may select the preview type at least among a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book and a shaded view of every page of the electronic book.
Information extraction unit 230 may be configured to extract from a database at least one of content information associated with the content file and a purchase history of a user. By way of example, but not limitation, the content information may include customer review information regarding the content file and sales information regarding the content file.
In some embodiments, preview type determination unit 220 may be further configured to determine an amount of content to be previewed based at least in part on at least one of the content information and the purchase history, which may be extracted by information extraction unit 230.
Preview generation unit 240 may be configured to generate a preview of the content file based at least in part on the preview type determined by preview type determination unit 220.
FIG. 3 shows a schematic block diagram illustrating an example architecture of a content server, arranged in accordance with at least some embodiments described herein.
As depicted, content server 100 may include content preview system 200 including content feature analysis unit 210, preview type determination unit 220, information extraction unit 230 and preview generation unit 240, the details of which are described above with reference to FIG. 2. Content server 100 may further include a database 300 and a transmitter 310. Although illustrated as discrete components, various components may be divided into additional components, combined into fewer components, or eliminated while being contemplated within the scope of the disclosed subject matter.
Database 300 may be configured to store at least one of a content file, content information associated with the content file, and a purchase history of a user.
Content feature analysis unit 210 may be configured to analyze one or more content features of the content file stored in database 300.
Information extraction unit 230 may be configured to extract from database 300 at least one of the content information associated with the content file and the purchase history of the user.
Preview type determination unit 220 may be configured to determine a preview type for the content file based at least in part on at least one of the content features analyzed by content feature analysis unit 210, the content information extracted by information extraction unit 230, and the purchase history extracted by information extraction unit 230.
Transmitter 310 may be configured to transmit to a device of the user (e.g., electronic device 110) the preview generated by preview generation unit 240.
FIG. 4 shows an example flow diagram of a process for providing a dynamic content preview, arranged in accordance with at least some embodiments described herein.
The process in FIG. 4 may be implemented in a content preview system, such as content preview system 200 including content feature analysis unit 210, preview type determination unit 220, information extraction unit 230 and preview generation unit 240 described above; or a content server, such as content server 100 including content preview system 200, database 300 and transmitter 310. An example process 400 may include one or more operations, actions, or functions as illustrated by one or more blocks 410, 420, 430 and/or 440. Although illustrated as discrete blocks, various blocks may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. Processing may begin at block 410.
At block 410 (Analyze Content Features of Content File), content preview system 200 or content server 100 may analyze one or more content features of a content file. By way of example, but not limitation, when the content file is an electronic book, the content features may indicate at least one of whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and whether the electronic book is a specialty publication. Processing may continue from block 410 to block 420.
At block 420 (Extract Content Information and/or Purchase History from Database), content preview system 200 or content server 100 may extract from a database content information associated with the content file and/or a purchase history of a user. By way of example, but not limitation, the content information may include customer review information regarding the content file and sales information regarding the content file. Processing may continue from block 420 to block 430.
At block 430 (Determine Preview Type for Content File), content preview system 200 or content server 100 may determine a preview type for the content file, as well as an amount of preview to be provided to the user, based at least in part on at least one of the analyzed content features, the extracted content information and the extracted purchase history. By way of example, but not limitation, when the content file is an electronic book, content preview system 200 or content server 100 may select the preview type at least among a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book, and a shaded view of every page of the electronic book, based at least in part on the content features. By way of example, but not limitation, content preview system 200 or content server 100 may determine amount of content to preview based at least in part on the content information and/or the purchase history, or alternatively, set the amount of preview as a constant value regardless of the content information and/or the purchase history. Processing may continue from block 430 to block 440.
At block 440 (Generate Preview of Content File), content preview system 200 or content server 100 may generate a preview of the content file based at least in part on the determined preview type and/or amount of content to preview, for transmission to a client device (e.g., electronic device 110) via a content server (e.g., content server 100).
FIG. 5 illustrates an example computer program product that may be utilized to provide a dynamic content preview, arranged in accordance with at least some embodiments described herein.
Computer program product 500 may include a signal bearing medium 502. Signal bearing medium 502 may include one or more instructions 504 that, when executed by, for example, a processor, may provide the functionality described above with respect to FIGS. 1-4. By way of example, instructions 504 may include: one or more instructions for analyzing one or more content features of a content file; or one or more instructions for determining a preview type for the content file based at least in part on the analyzed content features. Thus, for example, referring to FIGS. 2-3, content preview system 200 or content server 100 may undertake one or more of the blocks shown in FIG. 4 in response to instructions 504.
In some implementations, signal bearing medium 502 may encompass a computer-readable medium 506, such as, but not limited to, a hard disk drive, a CD, a DVD, a digital tape, memory, etc. In some implementations, signal bearing medium 502 may encompass a recordable medium 508, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations, signal bearing medium 502 may encompass a communications medium 510, such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.). Thus, for example, computer program product 500 may be conveyed to one or more modules of content preview system 200 by an RF signal bearing medium 502, where the signal bearing medium 502 is conveyed by a wireless communications medium 510 (e.g., a wireless communications medium conforming with the IEEE 802.11 standard).
Depending on the desired configuration, system memory 606 may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. System memory 606 may include an operating system 620, one or more applications 622, and program data 624. Application 622 may include instructions 626 that may be arranged to perform the functions as described herein including the actions described with respect to the content preview system 200 architecture as shown in FIG. 2 or including the actions described with respect to the flow charts shown in FIG. 4. In some examples, application 622 may be arranged to operate with program data 624 on an operating system 620 such that implementations for instructions for an electronic device as described herein.
1. A content preview system, comprising:
a content feature analysis unit to analyze a content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value; and
a preview type determination unit to:
determine a relevance of respective candidate preview types to the analyzed content file as a product of at least the respective percentage value and a respective weight value of each of the content features, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types,
determine which of the respective candidate preview types has a highest relevance to the analyzed content file, and
select, from among the respective candidate preview types, the preview type having the highest relevance as the preview type for the content file.
2. The content preview system of claim 1, wherein the content file is an electronic book, and
wherein the content feature analysis unit is further to analyze the electronic book, to:
determine whether the electronic book has a story structure,
determine whether the electronic book has a dictionary-like structure,
calculate a ratio between text and images in the electronic book,
calculate a ratio between text and tables in the electronic book, and
determine whether the electronic book is a specialty publication.
3. The content preview system of claim 1, wherein the content file is an electronic book, and
wherein the content feature analysis unit is further to partition content of the electronic book into words, images and tables.
4. The content preview system of claim 3, wherein the content feature analysis unit is further to detect a frequency of occurrence of technical terms in the electronic book.
5. The content preview system of claim 3, wherein the content feature analysis unit is further to detect change of a frequency of occurrence of a word throughout the electronic book.
6. The content preview system of claim 3, wherein the content feature analysis unit is further to detect a repeated structure throughout the electronic book.
7. The content preview system of claim 1, wherein the content file is an electronic book, and
wherein the respective candidate preview types include at least one of a preview of a beginning of the electronic book, a preview of a portion of every page of the electronic book, a preview of a portion of sentences in the electronic book, a mosaic view of every page of the electronic book, an embossed view of every page of the electronic book, and a shaded view of every page of the electronic book.
8. The content preview system of claim 1, further comprising:
an information extraction unit to extract from a database at least one of content information associated with the content file and a purchase history of a user,
wherein the preview type determination unit is further to determine an amount of a preview of the content file based at least in part on at least one of the content information and the purchase history that are extracted by the information extraction unit.
9. The content preview system of claim 8, wherein the content information includes customer review information regarding the content file and sales information regarding the content file.
10. The content preview system of claim 1, further comprising:
a preview generation unit to generate a preview of the content file based at least in part on the preview type determined by the preview type determination unit.
a database to store at least one of a content file, content information associated with the content file, and a purchase history of a user;
a content feature analysis unit to analyze the content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value;
an information extraction unit to extract from the database at least one of the content information associated with the content file and the purchase history of the user; and
determine a relevance of respective candidate preview types to the analyzed content file based at least on the respective percentage value and a respective weight value of each of the content features, the content information extracted by the information extraction unit, and the purchase history extracted by the information extraction unit, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types,
12. The content server of claim 11, further comprising:
13. The content server of claim 12, further comprising:
a transmitter to transmit to a device of the user the preview generated by the preview generation unit.
14. A method performed under control of a content preview system, comprising:
analyzing a content file for content features, wherein a weighted presence of respective ones of the content features in the content file is represented by a respective percentage value;
determining a relevance of each of the respective candidate preview types to the content file as a product of at least the respective percentage value and respective weight value of each of content features, wherein the weight value of each of the content features corresponds to a respective relationship between the content features and candidate preview types;
determining which of the respective candidate preview types has a highest relevance to the analyzed content file; and
selecting, from among the respective candidate preview types, the preview type having the highest relevance as the preview type for the content file.
extracting from a database content information associated with the content file,
wherein the determining of the preview type for the content file is further based on the extracted content information.
16. The method of claim 15, wherein the content information includes customer review information regarding the content file and sales information regarding the content file.
extracting from a database a purchase history of a user,
wherein the determining of the preview type for the content file is further based on the extracted purchase history.
generating a preview of the content file based at least in part on the determined preview type for transmission to a client device via a content server.
19. The method of claim 14, wherein the content file is an electronic book, and
wherein the content features of the content file include at least one of whether the electronic book has a story structure, whether the electronic book has a dictionary-like structure, a ratio between text and images in the electronic book, a ratio between text and tables in the electronic book, and whether the electronic book is a specialty publication.
20. The method of claim 14, wherein the content file is an electronic book, and
US13/814,654 2012-08-14 2012-08-14 Dynamic content preview Active 2033-09-26 US9507782B2 (en)
PCT/US2012/050726 WO2014027999A1 (en) 2012-08-14 2012-08-14 Dynamic content preview
US20140052741A1 US20140052741A1 (en) 2014-02-20
US9507782B2 true US9507782B2 (en) 2016-11-29
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US13/814,654 Active 2033-09-26 US9507782B2 (en) 2012-08-14 2012-08-14 Dynamic content preview
US15/290,110 Pending US20170031902A1 (en) 2012-08-14 2016-10-11 Dynamic content preview
US (2) US9507782B2 (en)
CN (1) CN104520784B (en)
WO (1) WO2014027999A1 (en)
CN105956904A (en) * 2016-04-26 2016-09-21 武汉理工数字传播工程有限公司 Digital reading appending package method, digital reading appending package system, and editing terminal
US20060235855A1 (en) 2003-05-02 2006-10-19 Rousseau David N Digital library system
WO2007022047A2 (en) 2005-08-11 2007-02-22 Microsoft Corporation Single action media playlist generation
WO2008067116A1 (en) 2006-11-30 2008-06-05 Microsoft Corporation Rank graph
US20110043869A1 (en) 2007-12-21 2011-02-24 Nec Corporation Information processing system, its method and program
WO2011162959A2 (en) 2010-06-21 2011-12-29 Microsoft Corporation Generating recommendations for improving a presentation document
2012-08-14 US US13/814,654 patent/US9507782B2/en active Active
2012-08-14 WO PCT/US2012/050726 patent/WO2014027999A1/en active Application Filing
2012-08-14 CN CN201280075221.4A patent/CN104520784B/en active IP Right Grant
2016-10-11 US US15/290,110 patent/US20170031902A1/en active Pending
CN1741004A (en) 2004-08-23 2006-03-01 微软公司 System and method to associate content types in a portable communication device
CN101243428A (en) 2005-08-11 2008-08-13 微软公司 Single action media playlist generation
CN101542486A (en) 2006-11-30 2009-09-23 微软公司 Rank graph
CN103026331A (en) 2010-06-21 2013-04-03 微软公司 Generating recommendations for improving a presentation document
"Blur filters," accessed at http://web.archive.org/web/20120215045802/http://help.adobe.com/en-US/photoshop/cs/using/WSfd1234e1c4b69f30ea53e41001031ab64-796ea.html, accessed on Aug. 10, 2014, 1 page.
"My Voice," accessed at http://web.archive.org/web/20120716031139/http://www.myvoice.co.jp/biz/surveys/15617/index.html, accessed on Aug. 10, 2014, 3 pages.
"Photoshop / Filter effect reference," accessed at http://help.adobe.com/ja-JP/photoshop/cs/using/WSfd1234e1c4b69f30ea53e41001031ab64-7970a.html#WSfd1234e1c4b69f30ea53e41001031ab64-796ea, accessed on Sep. 10, 2014, 8 pages.
"Reasons for not using eBooks," accessed at http://web.archive.org/web/20120510071546/http://bizmakoto.jp/makoto/articles/1006/03/news008.html, accessed on Sep. 10, 2014, 9 pages.
"The survey when buying a book on information source," accessed at http://web.archive.org/web/20130512224248/http://koubo.jp/contents/release/20070801.html, published on Aug. 1, 2007, 5 pages.
Camp, L. J., "First principles of copyright for DRM design," Internet Computing, IEEE, vol. 7, No. 3, May 21, 2003, pp. 59-65.
http://www.realread.com/files/demo-magazines.html. "On-line Magazine Demos-Magazines at Click of a Mouse: RealRead Provides Fast, Affordable Methods for Online Publishing" dated Apr. 4, 2012, pp. 1-3.
International Search Report from International Application No. PCT/US12/50726 mailed Oct. 12, 2012.
Li, H., and Yamanishi, K., "Topic analysis using a finite mixture model," Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora, held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics , vol. 13, pp. 35-44 (2000).
Mitchell, A. A., "The dimensions of advertising involvement," Advance in Consumer Research, vol. 8, pp. 25-30 (1981).
Rice, S., "Own or rent? A survey of eBook licensing models," Against the Grain, 2006, vol. 18, Issue 3, 3 pages (2006).
Shelburne, W. A., "E-book usage in an academic library: User attitudes and behaviors," Library Collections, Acquisitions, and Technical Services, vol. 33, Issues 2-3, pp. 59-72, (2009).
Shen, X. et al. "Analysis of topic dynamics in web search," WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web, May 10-14, 2005, 2 pages.
US20170031902A1 (en) 2017-02-02
CN104520784B (en) 2017-10-31
CN104520784A (en) 2015-04-15
WO2014027999A1 (en) 2014-02-20
US20140052741A1 (en) 2014-02-20
Mustanoja 2016 A middle English syntax
Sonnenburg et al. 2006 ARTS: accurate recognition of transcription starts in human
EP2480987A1 (en) 2012-08-01 Document analysis and association system and method
JP2010529569A (en) 2010-08-26 The determination of a dictionary of words and phrases
JP4251652B2 (en) 2009-04-08 Search apparatus, a search program and search method
US8244773B2 (en) 2012-08-14 Keyword output apparatus and method
Lian 2007 MOST: detecting cancer differential gene expression
CN102349069A (en) 2012-02-08 Visualizing site structure and enabling site navigation for a search result or linked page
Martinez et al. 2008 GenMiner: mining non-redundant association rules from integrated gene expression data and annotations
CN102640175A (en) 2012-08-15 A method for managing a plurality of electronic books on a computing device
US8280829B2 (en) 2012-10-02 Efficient algorithm for pairwise preference learning
CN102141982B (en) 2015-05-20 Providing contextual advertisements for electronic books
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