Media Content Assessment and Control Systems

• US 20090177463A1
• Filed: 02/25/2009
• Published: 07/09/2009
• Est. Priority Date: 08/31/2006
• Status: Active Grant

• Current Assignee: Waggener Edstrom Worldwide, Inc.
• Original Assignee: Waggener Edstrom Worldwide, Inc.
• Inventors: Lin, Jia, Stoffregen, Marc, Gallagher, Daniel Gerard
• Granted Patent: US 8,340,957 B2
• Time in Patent Office: Days
• US Class Current: 704/10
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing

Abstract

Computer implemented methods, computing devices, and computing systems, wherein relationships of words or phrases within a textual corpus are assessed via frequencies of occurrence of particular words or phrases and via frequencies of co-occurrence of particular pairs of words or phrases within defined tracts of text from within the textual corpus.

Claims

1. A computer-implemented method of adapting a characterized textual corpus state to a target state comprising:

deriving from a textual corpus an assessed textual corpus state on a physical computing device comprising;

parsing the textual corpus and filtering the parsed textual corpus yielding the assessed textual corpus state, the assessed textual corpus comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words; and

generating on the physical device a difference based on differencing at least one of;

(a) the derived keyword frequency subset and the initial keyword frequency subset;

(b) the set of within-sentence co-occurrence of pairs of derived keywords and the set of within-sentence co-occurrence of pairs of initial keywords; and

(c) the set of within-paragraph co-occurrence of pairs of derived keywords and the set of within-paragraph co-occurrence of pairs of initial keywords.

2. The computer-implemented method of claim 1 further comprising receiving a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

3. The computer-implemented method of claim 1 further comprising receiving the target state of the textual corpus comprising:

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus.

4. The computer-implemented method of claim 1 further comprising receiving a set of initial text publishers comprising at least one text publisher.

5. The computer-implemented method of claim 4 further comprising transmitting a textual input for the selected set of initial text publishers based on the difference.

6. A computer-implemented method of adapting a characterized textual corpus state to a target state comprising:

deriving from a textual corpus an assessed textual corpus state on a first physical computing device comprising;

parsing the textual corpus and filtering the parsed textual corpus yielding the assessed textual corpus state, the assessed textual corpus comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words; and

generating on a second physical device a difference based on differencing at least one of;

(a) the derived keyword frequency subset and the initial keyword frequency subset;

(b) the set of within-sentence co-occurrence of pairs of derived keywords and the set of within-sentence co-occurrence of pairs of initial keywords; and

(c) the set of within-paragraph co-occurrence of pairs of derived keywords and the set of within-paragraph co-occurrence of pairs of initial keywords.

7. The computer-implemented method of claim 6 further comprising receiving, on at least one of:

the first physical computing device and the second physical computing device, a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

8. The computer-implemented method of claim 6 further comprising receiving, on at least one of:

the first physical computing device and the second physical computing device, the target state of the textual corpus comprising;

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus;

9. The computer-implemented method of claim 6 further comprising receiving, on at least one of:

the first physical computing device and the second physical computing device, a set of initial text publishers comprising at least one text publisher.

10. The computer-implemented method of claim 9 further comprising transmitting, by at least one of:

the first physical computing device and the second physical computing device, a textual input for the selected set of initial text publishers based on the difference.

11. A computing device comprising:

a processing unit and addressable memory,

wherein the processing unit is configured to execute one or more instructions to;

derive from a textual corpus an assessed textual corpus state on a physical computing device comprising;

the execution of one or more instructions to parse the textual corpus, filter the parsed textual corpus, and yield the assessed textual corpus state, the assessed textual corpus comprising;

a set of derived keywords;

each derived keyword including a subset comprising an associated derived keyword frequency of occurrence within the defined textual corpus;

a set of high-frequency words;

each high-frequency word including an associated high-frequency word frequency of occurrence within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from a combined set of words comprising the set of derived keywords and the set of high-frequency words; and

a set of frequencies of within-paragraph co-occurrence of pairs of words within the defined textual corpus, the pairs of words selected from the combined set of words; and

generate a difference based on differencing at least one of;

(a) the derived keyword frequency subset and the initial keyword frequency subset;

(b) the set of within-sentence co-occurrence of pairs of derived keywords and the set of within-sentence co-occurrence of pairs of initial keywords; and

(c) the set of within-paragraph co-occurrence of pairs of derived keywords and the set of within-paragraph co-occurrence of pairs of initial keywords.

12. The computing device of claim 11 wherein the processing unit is further configured to receive a defined textual corpus comprising at least one textual output from a publisher of a selected set of initial publishers.

13. The computing device of claim 11 wherein the processing unit is further configured to receive the target state of the textual corpus, the target state comprising:

a set of initial keywords;

each initial keyword including a subset comprising an associated initial keyword frequency of occurrence from within the defined textual corpus;

a set of frequencies of within-sentence co-occurrence of pairs of initial keywords from within the defined textual corpus; and

a set of frequencies of within-paragraph co-occurrence of pairs of initial keywords from within the defined textual corpus.

14. The computing device of claim 11 wherein the processing unit is further configured to receive a set of initial text publishers comprising at least one text publisher.

15. The computing device of claim 14 wherein the processing unit is further configured to transmit a textual input for the selected set of initial text publishers based on the difference.