Method and device for classifying internet objects and objects stored on computer-readable media

• US 7,383,282 B2
• Filed: 10/17/2001
• Issued: 06/03/2008
• Est. Priority Date: 10/19/2000
• Status: Active Grant

• Current Assignee: Anthony David Whitehead, Patrick Ryan Morin
• Original Assignee: Anthony David Whitehead, Patrick Ryan Morin
• Inventors: Morin, Patrick Ryan, Whitehead, Anthony David
• Primary Examiner(s): Wong; Don
• Assistant Examiner(s): Black; Linh
• Application Number:
  US09/978,182
• Publication Number:
  US 20020059221A1
• Time in Patent Office: 2,421 Days
• Field of Search: 707 1-1041
• US Class Current: 707/700
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: Y10S 707/955 Object-oriented
• CPC Class Codes: Y10S 707/959 Network
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method and device for classifying Internet objects, and/or objects stored on computer-readable media. The method includes an automated method that uses a device to gather information from as many sources as possible within an object, and to analyze and combine this information in order to classify the object.

Claims

1. A computer-implemented method of classifying an Internet object comprising the steps of:

(a) computing any combination of coefficients for the Internet object from the group of data type coefficients comprising (i) a descriptor coefficient, (ii) a name coefficient, (iii) a text coefficient, (iv) a image coefficient, (v) a audio coefficient, (vi) a video coefficient, (vii) a plug-in coefficient, and (viii) a relational coefficient;

(b) classifying the Internet object using the any combination of coefficients by automatic comparison of the any combination of coefficients to identified criteria; and

(c) combining and weighting all of said coefficients except for said relational coefficient, and generating a first weighted sum useable in classifying said Internet object.

2. The method as recited in claim 1, further including the steps of:

providing a threshold value;

determining if said first weighted sum is greater than or less than said threshold value;

wherein said classifying step includes the acts of classifying said Internet object, such that if said first weighted sum is greater than said threshold value, said Internet object is classified as adult content.

3. The method as recited in claim 2, further including the steps of:

computing said relational coefficient if said object is not classified as adult content in said classifying step; and

combining and weighting all of said coefficients; and

generating a second weighted sum.

4. The method as recited in claim 1, further including the steps of:

providing a threshold value;

determining if said second weighted sum is greater than or less than said threshold value;

wherein said classifying step includes the acts of classifying said Internet object such that if said second weighted sum is greater than said threshold value, said object is classified as adult content.

5. The method as recited in claim 2, further including the step of blocking or filtering access to Internet objects that exceed the tolerated threshold for adult content by storing the URLS of sites which contain said Internet object in a database, if said first weighted sum is greater than said threshold value such that said sites can be blocked or filtered by referencing said database when the Internet is accessed.

6. The method as recited in claim 2, further including the step of blocking or filtering access to Internet objects that exceed the tolerated threshold for adult content by storing the URLS of sites which contain said Internet object in a database, if said second weighted sum is greater than said threshold value such that said sites can be blocked or filtered by referencing said database when the Internet is accessed.

7. The method as recited in claim 1 wherein said step of computing the descriptor coefficient comprises the acts of:

identifying the PICS code in a metadata of an Internet object;

parsing labels of said PICS code; and

calculating a descriptor coefficient by computing the weighted average of the ratings of said labels.

8. The method as recited in claim 1 wherein said step of computing the name coefficient comprises the acts of:

compiling a keyword list of words that are likely to indicate adult content;

generating a weighted keyword list by assigning each word or phrase in the keyword list a weight, such that a higher weight is given to words or phrases that are more likely to indicate adult content;

providing an automated robot with the keyword list;

computing substrings in the name of the Internet object and comparing each substring to the weighted keyword list; and

adding weights of all weighted keywords found in all substrings to compute the name coefficient.

9. The method as recited in claim 1 wherein said step of computing the text coefficient comprises the acts of:

compiling a keyword and phrases list of words and phrases that are likely to indicate adult content;

assigning each word or phrase in the keyword and phrases list a weight, such that a higher weight is given to words that are more likely to indicate adult content;

providing an automated robot with the keyword and phrases list;

eliminating any formatting from the text of the Internet object to obtain an unformatted text;

reducing each individual word in the unformatted text to a stem, using a stemming algorithm;

comparing each stemmed word to the weighted keyword and phrases list;

calculating the text coefficient by first adding weights of all weighted keywords and phrases found in all stemmed words, and then multiplying that total by a scaling or weighting constant, which is a mathematical function of the total number of words in the Internet object; and

dividing the resulting number by the number of words in an Internet object.

10. The method as recited in claim 1 wherein said step of computing the image coefficient comprises the acts of:

establishing a range of Hue-Saturation-Value (“

HSV”

) colour space values for skin tones;

converting the colour of the Internet object into the HSV colour space;

comparing every pixel of the Internet object image to identify which pixels fall within the colour space values;

dividing the number of skin tone pixels in the Internet object image by the total number of pixels in the image;

disregarding images that have less than a threshold proportion of skin pixels;

assigning each image in an Internet object that meets or exceeds the threshold proportion of skin pixels a nudity coefficient equal to the percentage of skin pixels in the image;

weighting the nudity coefficient of each image in an Internet object using specified weighting;

calculating the image coefficient by first adding together all of the weighted nudity coefficients generated by an Internet object; and

multiplying the total of all weighted nudity coefficients by a scaling or weighting constant, which is a mathematical function of the total number of images in the Internet object.

11. The method as recited in claim 1 wherein said step of computing the audio coefficient comprises the acts of:

converting any audio components in the Internet object into unformatted text using a speech recognition algorithm;

concatenating the text data; and

evaluating the resultant text data using the ads set out in claim 9 (i.e. text coefficient), and assigning a text coefficient using the acts set out in steps (a) to (b) of claim 9 to the resultant text.

12. The method as recited in claim 1 wherein said step of computing the video coefficient comprises the acts of:

dividing the video data into image and audio data as required by the media type;

valuating the resultant image data using the acts set out in claim 10 (i.e. image coefficient), and assigning an image coefficient for each frame of video data using the acts set out in acts (a) to (i) of claim 10 above;

evaluating the resultant audio data using the acts set out in claim 11 above (i.e. audio coefficient [method]), and assigning an audio coefficient for the audio data using the acts set out in steps (a) to (c) of claim 11 above; and

calculating the video coefficient by adding a weighted sum of the image and audio coefficients, with weighting being determined automatically or can be set by the user.

13. The method as recited in claim 1 wherein said step of computing the relational coefficient comprises the acts of:

determining whether the CoAC of an Internet object, using the calculations in the following individual coefficients (i) the descriptor coefficient, (ii) the name coefficient (iii) the text coefficient, (iv) the image coefficient, (v) the audio coefficient (vi) the video coefficient, and (vii) the plug-in coefficient, results in a CoAC greater than a tolerated threshold;

if not engaging in calculation of the relational coefficient by compiling a list of all Internet objects that the Internet object under consideration links to (the “

linked-to objects”

), as well as a list of all Internet objects that the Internet object under consideration is part of (the “

part of objects”

);

checking the database referred to in claim 5 to determine which of the “

linked-to”

objects and “

part of”

objects contain adult content that exceeds the tolerated threshold;

calculating an average CoAC of both the “

linked to”

objects and the “

part of”

objects that exceed the tolerated thresholds for adult content; and

calculating the relational coefficient by calculating a weighted average of the two averages determined in part (d) above, in which weighting can be determined automatically or can be set by the user.

15. A computer-implemented method of classifying an Internet object or an object stored on computer-readable media by identifying objects with adult content on the basis of the amount of adult content found in audio components of the object, the method comprising the steps of:

(a) converting any audio components in the object into unformatted text using any known speech recognition algorithm;

(b) concatenating the text data;

(c) evaluating the resultant text data using the method set out in acts (a) to (h) of claim 9 above (i.e. text coefficient), and assigning a text coefficient using the method set out in steps (a) to (h) of claim 9 to the resultant text;

(d) classifying said object using said text coefficient.

16. A method of classifying an internet object or an object stored on computer-readable media by identifying objects with adult content on the basis of the amount of adult content found in video components of the object, the method comprising the steps of:

(a) dividing the video data into image and audio data as required by the media type;

(b) evaluating the resultant image data using the acts set out in acts (a) to (i) of claim 10 (i.e. image coefficient), and assigning an image coefficient for each frame of video data using the acts set out in acts (a) to (i) of claim 10 above;

(c) evaluating the resultant audio data using the acts set out in acts (a) to (c) of claim 11 (i.e. audio coefficient), and assigning an audio coefficient for the audio data using the acts set out in acts (a) to (c) of claim 11;

(d) calculating the video coefficient by adding a weighted sum of the image and audio coefficients, in which weighting can be determined automatically or can be set by the user; and

(e) providing a classification for the Internet object.

17. The method as recited in claim 13, further comprising the step of calculating a weighted number to describe the coefficient of adult content (“

CoAC”

) in order to identify adult content.

18. The method as recited in claim 13, wherein said act of computing the audio coefficient comprises the acts of:

(a) converting any audio components in the object stored on computer-readable media into unformatted text using any known speech recognition algorithim;

(b) concatenating the text; and

(c) evaluating the resultant text data using the method set out in acts (a) to (h) of claim 9 (i.e. text coefficient), and assigning a text coefficient using the acts set out in acts (a) to (h) of claim 9 to the resultant text.

19. The method as recited in claim 1 wherein the identified criteria comprises adult content criteria.

20. The method as recited in claim 1 wherein the identified criteria comprises criteria other than adult content.

21. The method as recited in claim 1, wherein the identified criteria is predetermined.

22. The method as recited in claim 1, wherein the identified criteria is user-definable.

23. The method as recited in claim 1, further comprising a step of filtering the Internet object whereby the Internet object is filtered based on classification.

24. The method as recited in claim 23, wherein the step of filtering Internet content is carried out as the Internet object passes through a TCP/IP stack.

25. The method as recited in claim 1, further comprising the step of censoring the Internet object.

26. The method as recited in claim 25, wherein the censoring step results in censoring of pictures, movies, television, radio and other media.

27. The method as recited in claim 1, further comprising the step of denying access to the Internet object.

28. The method as recited in claim 1, further comprising the step of allowing access to the Internet object.

29. The method as recited in claim 1, further comprising the step of controlling a component of a computer based on the classification of the Internet object.

30. The method as recited in claim 1, wherein the component of the computer in the controlling step is a printer.

31. The method as recited in claim 29, wherein the component of the computer in the controlling step is a network device.

32. The method as recited in claim 29, wherein the component of the computer in the controlling step is a storage device.

33. The method as recited in claim 29, wherein the component of the computer in the controlling step is a display.

34. A computer readable memory having recorded thereon statements and instructions for execution by a computer to carry out the method of claim 1.

36. A computer readable memory having recorded thereon statements and instructions for execution by a computer to carry out the method of claim 15.

37. A computer readable memory having recorded thereon statements and instructions for execution by a computer to carry out the method of claim 16.

14. A computer-implemented method of classifying an Internet object or an object stored on computer-readable media by identifying adult content by the amount of nudity found in the object, the method comprising the steps of:

(a) establishing a range of Hue-Saturation-Value (“

HSV”

) colour space values that are classified as skin tones;

(b) upon reviewing images in an object, converting the colour of the object into the HSV colour space;

(c) comparing every pixel of the object image to identify which pixels fall within the skin tone ranges established in step (a) above;

(d) dividing the number of skin tone pixels in the object image by the total number of pixels in the image;

(e) disregarding images that have less than a threshold proportion of skin pixels;

(f) assigning each image in an object that meets or exceeds the threshold proportion of skin pixels a nudity coefficient equal to the percentage of skin pixels in the image;

(g) weighting the nudity co-efficient of each image in an object by determining the size of the image;

(h) calculating the image coefficient by first adding together all of the weighted nudity coefficients generated by an object;

(i) multiplying the total of all weighted nudity coefficients by a scaling or weighting constant, which is a mathematical function of the total number of images in the object; and

(j) classifying the object using said weighted nudity coefficients.

35. A computer readable memory having recorded thereon statements and instructions for execution by a computer to carry out the method of claim 14.