Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements submitted on 12/14/2022, 05/10/2023, 10/30/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 10/07/2025.
The present application is being examined as a continuation of application 17/845,249, titled "SYSTEMS AND METHODS FOR CATEGORIZING DOMAINS USING ARTIFICIAL INTELLIGENCE", and filed on 06/21/2022.
Claims 1-3, 5-10, 12-17, 19, 20 are rejected.
Claims 1-3, 5-10, 12-17, 19, 20 are pending.
Claims 4, 11, 18 are canceled.
Response to Arguments
Applicant remarks:
“Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed to an abstract idea. While Applicant does not agree, Applicant has amended the claims to overcome the rejections. Applicant respectfully submits that the amended claims are not directed to an abstract idea, and in the alternative, integrate any alleged abstract idea into a practical application for assigning categories to new domains using classifiers. Applicant respectfully requests that the Examiner withdraw the rejections and allow the remaining claims.” (pg. 9)
Examiner response:
Applicant’s arguments have been fully considered but they are not persuasive. Refer to the updated 35 U.S.C. 101 section of this document.
Applicant remarks:
“While Applicant does not agree, Applicant has amended claim 1 to include [additional] features […] Applicant has reviewed the cited references and can find no teaching or suggestion of such features. Applicant therefore respectfully requests that the Examiner withdraw the rejections and allow claims 1, 8, and 15.” (pg. 9-10)
Examiner response:
Applicant’s arguments have been fully considered and they are persuasive. The amended claim language overcomes Matic. Upon further search and consideration, the claims formerly rejected under 35 U.S.C. 102 as being anticipated by Matic are now rejected under 35 U.S.C. 103 as being obvious over Matic in view of Klinkott. Refer to the updated 35 U.S.C. 103 section of this document.
Prior Art References
The short names that are used to identify the references of prior art in the analysis that follows are:
Short Name
Reference
Matic
Matic, S., Iordanou, C., Smaragdakis, G. and Laoutaris, N., 2020, October. Identifying sensitive urls at web-scale. In Proceedings of the ACM Internet Measurement Conference (pp. 619-633).
Persson
Persson, E., 2019. Evaluating tools and techniques for web scraping.
Klinkott
US 20100121790 A1 - METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR CATEGORIZING WEB CONTENT
Additional References
Short Name
Reference
Hobbs
Hobbs, R., Jaszi, P. and Aufderheide, P., 2009. How media literacy educators reclaimed copyright and fair use. International Journal of Learning and Media, 1(3), pp.33-48.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-10, 12-17, 19, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. This judicial exception is not integrated into a practical application as outlined in the 2-step analyses for each claim that follows.
In reference to claim 1.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - “1. (Currently Amended) A method for categorizing new domains using artificial intelligence comprising:”
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“for each webpage of the set of webpages:”
“extracting a plurality of features from the webpage of the set of webpages by the computing device;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, a feature could be the count of a certain visual element on the page, which may be performed by a human.
“and associating one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage by the computing device;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“for each category of the plurality of categories:”
“determining the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier by the computing device;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determining the threshold percentage set for the category, wherein each category was set with a different threshold percentage by the computing device;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determining that the percentage of webpages is greater than the threshold percentage for the category by the computing device;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, associating the category with the new domain;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and adding the new domain and any associated categories to the list of domains by the computing device.” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
“receiving a list of domains by the computing device, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“receiving an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“in response to the indication, retrieving a set of webpages published at the new domain by the computing device;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
“receiving a list of domains by the computing device, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“receiving an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“in response to the indication, retrieving a set of webpages published at the new domain by the computing device;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Thus, the claim is subject matter ineligible.
In reference to claim 2.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - "2. The method of claim 1,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "wherein the plurality of features comprises text features and script features." which only provides further details regarding the structure of the features to be extracted and thus is still a mental process based on the parent claim.
Step 2A Prong 2 and Step 2B:
No additional elements are recited. Thus, the claim is subject matter ineligible.
In reference to claim 3.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - "3. The method of claim 1,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the claim is subject matter ineligible.
In reference to claim 5.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - "5. The method of claim 1, further comprising:".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "for each webpage of the training set of webpages, extracting one or more features from the webpage by the computing device;" which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “receiving indications of a training set of webpages by the computing device, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
- "and for each webpage of the training set of webpages, training the classifier using the one or more extracted features and the one or more categories associated with the webpage by the computing device." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “receiving indications of a training set of webpages by the computing device, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
- "and for each webpage of the training set of webpages, training the classifier using the one or more extracted features and the one or more categories associated with the webpage by the computing device." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
Thus, the claim is subject matter ineligible.
In reference to claim 6.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - "6. The method of claim 1,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "further comprising using the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains" which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
Step 2A Prong 2 and Step 2B:
No additional elements are recited. Thus, the claim is subject matter ineligible.
In reference to claim 7.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a method - "7. The method of claim 6, further comprising:".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “receiving one or more access rules;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
- “and controlling user access to the webpages associated with the domains in the list of domains according to the received one or more access rules.” which amounts to insignificant extra-solution activity per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “receiving one or more access rules;” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
- “and controlling user access to the webpages associated with the domains in the list of domains according to the received one or more access rules.” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by Hobbs (Hobbs 36, “Internet filtering is common in most districts in the United States,”).
Thus, the claim is subject matter ineligible.
In reference to claim 8.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - “8. (Currently Amended) A system for categorizing new domains using artificial intelligence comprising: at least one processor; and a computer-readable medium storing computer executable instructions stored therefore that when executed by the at least one processor cause the system to:”
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“for each webpage of the set of webpages:”
“extract a plurality of features from the webpage of the set of webpages;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, a feature could be the count of a certain visual element on the page, which may be performed by a human.
“and associate one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“for each category of the plurality of categories:”
“determine the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determine the threshold percentage set for the category, wherein each category was set with a different threshold percentage;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determine that the percentage of webpages is greater than the threshold percentage for the category;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, associate the category with the new domain;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and add the new domain and any associated categories to the list of domains.” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“in response to the indication, retrieve a set of webpages published at the new domain;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“in response to the indication, retrieve a set of webpages published at the new domain;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Thus, the claim is subject matter ineligible.
In reference to claim 9.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "9. The system of claim 8,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "wherein the plurality of features comprises text features and script features." which only provides further details regarding the structure of the features to be extracted and thus is still a mental process based on the parent claim.
Step 2A Prong 2 and Step 2B:
No additional elements are recited. Thus, the claim is subject matter ineligible.
In reference to claim 10.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "10. The system of claim 8,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the claim is subject matter ineligible.
In reference to claim 12.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "12. The system of claim 8, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "for each webpage of the training set of webpages, extract one or more features from the webpage;" which, but for the inclusion of generic computing equipment (i.e., processor), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “receive indications of a training set of webpages, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
- "and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “receive indications of a training set of webpages, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
- "and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
Thus, the claim is subject matter ineligible.
In reference to claim 13.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "13. The system of claim 8,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to use the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains." which is an evaluation that may be performed mentally by a human with the aid of pen and paper. Thus, the limitation recites a mental process and therefore an abstract idea.
Step 2A Prong 2 and Step 2B:
No additional elements are recited. Thus, the claim is subject matter ineligible.
In reference to claim 14.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "14. The system of claim 13, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “receive one or more access rules;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
- “and control user access to the webpages associated with the domains in the list of domains according to the received one or more access rules.” which amounts to insignificant extra-solution activity per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “receive one or more access rules;” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
- “and control user access to the webpages associated with the domains in the list of domains according to the received one or more access rules.” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by (Hobbs 36, “Internet filtering is common in most districts in the United States,”.
Thus, the claim is subject matter ineligible.
In reference to claim 15.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - “15. (Currently Amended) A non-transitory computer-readable medium storing computer executable instructions stored therefore that when executed by at least one processor cause the at least one processor to:”
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“for each webpage of the set of webpages:”
“extract a plurality of features from the webpage of the set of webpages;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, a feature could be the count of a certain visual element on the page, which may be performed by a human.
“and associate one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“for each category of the plurality of categories:”
“determine the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determine the threshold percentage set for the category, wherein each category was set with a different threshold percentage;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“determine that the percentage of webpages is greater than the threshold percentage for the category;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, associate the category with the new domain;” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer). For example, if the feature is the count of a certain visual element on the page, the classifier may categorize by the value of that count, which may be performed by a human.
“and add the new domain and any associated categories to the list of domains.” which, but for the inclusion of generic computing equipment (i.e., computing device), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
“in response to the indication, retrieve a set of webpages published at the new domain;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
“in response to the indication, retrieve a set of webpages published at the new domain;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
Thus, the claim is subject matter ineligible.
In reference to claim 16.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "16. The computer-readable medium of claim 8,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "wherein the plurality of features comprises text features and script features." which only provides further details regarding the structure of the features to be extracted and thus is still a mental process based on the parent claim.
Step 2A Prong 2 and Step 2B:
No additional elements are recited. Thus, the claim is subject matter ineligible.
In reference to claim 17.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "17. The computer-readable medium of claim 8,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- "wherein the classifier is a neural network." which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
Thus, the claim is subject matter ineligible.
In reference to claim 19.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "19. The computer-readable medium of claim 15, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
- "for each webpage of the training set of webpages, extract one or more features from the webpage;" which, but for the inclusion of generic computing equipment (i.e., processor, computer-readable medium), is an evaluation that may be performed mentally by a human with the aid of pen and paper (refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer).
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “receive indications of a training set of webpages, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering or outputting per MPEP2106.05(g).
- "and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “receive indications of a training set of webpages, wherein each webpage in the training set is associated with one or more categories of the plurality of categories;” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
- "and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage." which amounts to merely linking the use of a judicial exception to a particular technological environment or field of use. Per MPEP 2106.05(h), the employment of computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more.
Thus, the claim is subject matter ineligible.
In reference to claim 20.
(Step 1) Is the claim to a process, machine, manufacture or composition of matter?
Yes, a machine - "20. The computer-readable medium of claim 15,".
(Step 2A Prong 1) Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, it inherits the abstract idea recited by the parent claim.
(Step 2A Prong 2) Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, consider the following elements:
- “further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the at least one processor to use the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains.” which amounts to insignificant extra-solution activity per MPEP2106.05(g).
(Step 2B) Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, consider the following elements:
- “further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the at least one processor to use the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains.” which amounts to insignificant extra-solution activity mere data gathering per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by (Hobbs 36, “Internet filtering is common in most districts in the United States,”.
Thus, the claim is subject matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 18, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matic in view of Klinkott.
Claims 2, 9, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Matic in view of Klinkott in further view of Persson.
Claims 3, 10, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Matic in view of Klinkott in further view of a separate embodiment of Matic.
In reference to claim 1.
Matic teaches:
“1. (Currently Amended) A method for categorizing new domains using artificial intelligence comprising:” (preamble)
“receiving a list of domains by the computing device, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology. […] Figure 1 illustrates how we blend crowd-sourcing (done by Curlie) with automated and a manual steps (done by us) on the thin-waist of an overall methodology that can identify the sensitive part of the Web (Section. 4).”, The data from Curlie are classified manually in addition to automatically by a machine learning classifier)
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“receiving an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” (Matic 628, “Classifying the Common Crawl corpus. The 3 Billion web pages of the October 2019 snapshot are partitioned into 56k zipped archives, totaling 10 Terabytes of disk space. After the classification, we manually assessed the classifier accuracy by sampling around one hundred URLs for each category and verified that the average accuracy of the classifier was above 90%”, The Common Crawl dataset consists of domains that were not in the original list i.e. the Curlie dataset)
“in response to the indication, retrieving a set of webpages published at the new domain by the computing device;” (Matic 628, “The dispatcher iterates over all the archives, loads them in memory in their uncompressed format, and assigns each pointer in memory to a worker. The worker extracts the file, removes error pages, non-English documents and any content with less than 1,000 characters.”)
Examiner notes that a dataset of URLs and their corresponding webpages necessarily teaches at least one domain at which the webpages are published because a domain is a component of a URL. Thus, the reception of 1,525,865 URLs as in Figure 1 of Matic teaches the reception of at least one domain. Matic thus teaches “retrieving a set of webpages” and the URLs associated with those webpages teach the at least one “new domain” that the webpages are “published” at.
“for each webpage of the set of webpages:”
“extracting a plurality of features from the webpage of the set of webpages by the computing device;” (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”)
“and associating one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage by the computing device;” (Matic Table 6, The table depicts the categories and domains assigned to each one)
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“for each category of the plurality of categories:”
“determining the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier by the computing device;” (Matic Table 7, The table depicts the percentage of webpages associated with each category)
“associating the category with the new domain” (Matic 622, “As final assessment we study the differences among per-URL and per-domain categorization. Our goal is to understand the possible benefits of having categories assigned to individual URLs instead of using a the same category for all the elements under an [Effective Second Level Domain].”, Matic draws a comparison between categorizing individual URLs and domains)
“and adding the new domain and any associated categories to the list of domains by the computing device.” (Matic Table 6, “URLs”, “ADDED”)
Klinkott teaches:
“determining the threshold percentage set for the category, wherein each category was set with a different threshold percentage by the computing device; determining that the percentage of webpages is greater than the threshold percentage for the category by the computing device; and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, [associating the category with the new domain];” (Klinkott [0039], “In an exemplary embodiment, the categorizer 72 may be configured to use any one of multiple possible techniques for completing categorizations of web content based on existing categorizations. In this regard, for example, one technique that may be employed includes the categorization of a particular web page based on the categorizations of web pages to which the particular web page links. As such, for example, if a threshold number (e.g., two or more, a majority, a fixed percentage, etc.) of web pages to which the particular web page links have the same category, the particular web page may be assigned to the category that is shared between the web pages to which the particular web page links. Meanwhile, if several (or most) of the web pages to which the particular web page links do not have the same category, but have similar categories, then a broader category that may encompass all or a threshold percentage of the similar categories may be assigned to the particular web page.”)
Motivation to combine Matic with Klinkott.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Matic and Klinkott. Matic discloses a method for applying Naïve Bayesian learning to the categorization and identification of sensitive website URLs. Klinkott discloses a method and system for categorizing web content based on page-to-page linkages. One would be motivated to combine these references because the existing parsed-content described by Matic could be expanded by further considering the page links as discussed in Klinkott. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (E) Obvious to try – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In reference to claim 2.
“2. The method of claim 1,” (preamble)
Matic teaches:
“wherein the plurality of features comprises text features (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C).”) [and script features].”
Persson teaches:
“[wherein the plurality of features comprises text features] and script features (Persson 75, “For example, a very important feature that could make or break a web scraper is the support of scraping JavaScript pages. Even though a webpage might not rely on JavaScript dynamically loading its data at the time of development, it is possible that it might migrate to this form in the future. If a scraper supports JavaScript, it could still be possible to change the scraper to a working version. If it does not support JavaScript at all, a brand new, different, web scraper would have to be developed.”).”
Motivation to combine Matic, Klinkott with Persson.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Matic, Klinkott and Persson. Matic discloses a method for applying Naïve Bayesian learning to the categorization and identification of sensitive website URLs that utilizes a massive databank of archived website HTML. Persson discloses methodologies for scraping websites of HTML textual information and JavaScript functionality. One would be motivated to combine these references because Persson offers a method of both gathering the data necessary for Matic, and additionally discusses at length tooling for processing any existing data as would be required in the feature extraction step of Matic. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (A) Combining prior art elements according to known methods to yield predictable results.
In reference to claim 3.
Matic teaches:
“3. The method of claim 1,” (preamble)
A separate embodiment of Matic teaches:
“wherein the classifier is a neural network (Matic 624, “Classification algorithms. There is a wide range of popular algorithms that are suitable for classifying web pages. Examples are K-Nearest-Neighbors [19, 41, 49], Naïve Bayes [32, 33, 48], Support Vector Machines [21, 22, 77, 90], Decision Trees [35, 80, 85], Neural Networks [42, 56] and different variations [59], maximum entropy [23, 49]. Given the scope of this work, we choose the Naïve Bayes classification algorithm for the reasons explained in Section 3.2.”).”
Motivation to combine Matic, Klinkott with an additional embodiment within Matic.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Matic, Klinkott and an additional embodiment of Matic. Matic discloses a method for applying Naïve Bayesian learning to the categorization and identification of sensitive website URLs. An additional embodiment of Matic discloses an additional machine learning algorithm which may be substituted with the Naïve Bayesian algorithm utilized by the author. One would be motivated to combine these references because it would have been obvious to one of ordinary skill in the art to try as a substitute because the source lists a number of algorithms that are cited as being useful for carrying out the task at hand. Further, MPEP 2143 sets forth the Supreme Court rationales for obviousness including: (E) Obvious to try – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success.
In reference to claim 5.
“5. The method of claim 1, further comprising:” (preamble)
“receiving indications of a training set of webpages by the computing device (Matic 624, “During this process, we reserve 70% of the input for the training phase and the remaining 30% for testing.”), wherein each webpage in the training set is associated with one or more categories of the plurality of categories (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology.”);”
“for each webpage of the training set of webpages, extracting one or more features from the webpage by the computing device (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”);”
“and for each webpage of the training set of webpages, training the classifier using the one or more extracted features and the one or more categories associated with the webpage by the computing device (Matic 624, “We train the classifier using the training set described in Section 2.4. Such set contains 221,712 web pages, with the corresponding GDPR category as label. From each web page we extract both the human readable text and the meta-data information. Next, we filter the content by applying the preprocessing steps described in Section 3.1. This procedure generates a final set of 218,696 URLs with content. Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”).”
In reference to claim 6.
“6. The method of claim 1,” (preamble)
“further comprising using the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains (Matic 631, “Independently of the legal dimension of the matter, being able to identify such URLs programmatically in real time, opens up the road for additional proactive measures such as warning users, blocking third-parties, or even automatically filing complaints.”, Warning users and blocking sites are a means of controlling access to a domain).”
In reference to claim 7.
“7. The method of claim 6, further comprising:” (preamble)
“receiving one or more access rules; and controlling user access to the webpages associated with the domains in the list of domains according to the received one or more access rules (Matic 631, “Independently of the legal dimension of the matter, being able to identify such URLs programmatically in real time, opens up the road for additional proactive measures such as warning users, blocking third-parties, or even automatically filing complaints.”, Warning users and blocking sites are a means of controlling access to a domain, the URL Identified and blocked is the rule that is received).”
In reference to claim 8.
Matic teaches:
“8. (Currently Amended) A system for categorizing new domains using artificial intelligence comprising: at least one processor; and a computer-readable medium storing computer executable instructions stored therefore that when executed by the at least one processor cause the system to:”
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology. […] Figure 1 illustrates how we blend crowd-sourcing (done by Curlie) with automated and a manual steps (done by us) on the thin-waist of an overall methodology that can identify the sensitive part of the Web (Section. 4).”, The data from Curlie are classified manually in addition to automatically by a machine learning classifier);”
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” (Matic 628, “Classifying the Common Crawl corpus. The 3 Billion web pages of the October 2019 snapshot are partitioned into 56k zipped archives, totaling 10 Terabytes of disk space. After the classification, we manually assessed the classifier accuracy by sampling around one hundred URLs for each category and verified that the average accuracy of the classifier was above 90%”, The Common Crawl dataset consists of domains that were not in the original list i.e. the Curlie dataset);”
“in response to the indication, retrieve a set of webpages published at the new domain;” (Matic 628, “The dispatcher iterates over all the archives, loads them in memory in their uncompressed format, and assigns each pointer in memory to a worker. The worker extracts the file, removes error pages, non-English documents and any content with less than 1,000 characters.”);”
“for each webpage of the set of webpages:”
“extract a plurality of features from the webpage of the set of webpages;” (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”);”
“and associate one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage;” (Matic Table 6, The table depicts the categories and domains assigned to each one);”
“for each category of the plurality of categories:”
“determine the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier;” (Matic Table 7, The table depicts the percentage of webpages associated with each category)
“associate the category with the new domain” (Matic 622, “As final assessment we study the differences among per-URL and per-domain categorization. Our goal is to understand the possible benefits of having categories assigned to individual URLs instead of using a the same category for all the elements under an [Effective Second Level Domain].”)
“and add the new domain and any associated categories to the list of domains.” (Matic Table 6, “URLs”, “ADDED”)
Klinkott teaches:
“determine the threshold percentage set for the category, wherein each category was set with a different threshold percentage; determine that the percentage of webpages is greater than the threshold percentage for the category; and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, [associate the category with the new domain];” (Klinkott [0039], “In an exemplary embodiment, the categorizer 72 may be configured to use any one of multiple possible techniques for completing categorizations of web content based on existing categorizations. In this regard, for example, one technique that may be employed includes the categorization of a particular web page based on the categorizations of web pages to which the particular web page links. As such, for example, if a threshold number (e.g., two or more, a majority, a fixed percentage, etc.) of web pages to which the particular web page links have the same category, the particular web page may be assigned to the category that is shared between the web pages to which the particular web page links. Meanwhile, if several (or most) of the web pages to which the particular web page links do not have the same category, but have similar categories, then a broader category that may encompass all or a threshold percentage of the similar categories may be assigned to the particular web page.”)
In reference to claim 9.
“9. The system of claim 8,” (preamble)
Matic teaches:
“wherein the plurality of features comprises text features (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C).”) [and script features].”
Persson teaches:
“[wherein the plurality of features comprises text features] and script features (Persson 75, “For example, a very important feature that could make or break a web scraper is the support of scraping JavaScript pages. Even though a webpage might not rely on JavaScript dynamically loading its data at the time of development, it is possible that it might migrate to this form in the future. If a scraper supports JavaScript, it could still be possible to change the scraper to a working version. If it does not support JavaScript at all, a brand new, different, web scraper would have to be developed.”).”
In reference to claim 10.
Matic teaches:
“10. The system of claim 8,” (preamble)
A separate embodiment of Matic teaches:
“wherein the classifier is a neural network (Matic 624, “Classification algorithms. There is a wide range of popular algorithms that are suitable for classifying web pages. Examples are K-Nearest-Neighbors [19, 41, 49], Naïve Bayes [32, 33, 48], Support Vector Machines [21, 22, 77, 90], Decision Trees [35, 80, 85], Neural Networks [42, 56] and different variations [59], maximum entropy [23, 49]. Given the scope of this work, we choose the Naïve Bayes classification algorithm for the reasons explained in Section 3.2.”).”
In reference to claim 12.
“12. The system of claim 8, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:” (preamble)
“receive indications of a training set of webpages (Matic 624, “During this process, we reserve 70% of the input for the training phase and the remaining 30% for testing.”), wherein each webpage in the training set is associated with one or more categories of the plurality of categories (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology.”);”
“for each webpage of the training set of webpages, extract one or more features from the webpage (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”);”
“and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage (Matic 624, “We train the classifier using the training set described in Section 2.4. Such set contains 221,712 web pages, with the corresponding GDPR category as label. From each web page we extract both the human readable text and the meta-data information. Next, we filter the content by applying the preprocessing steps described in Section 3.1. This procedure generates a final set of 218,696 URLs with content. Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”).”
In reference to claim 13.
“13. The system of claim 8,” (preamble)
“further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to use the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains (Matic 631, “Independently of the legal dimension of the matter, being able to identify such URLs programmatically in real time, opens up the road for additional proactive measures such as warning users, blocking third-parties, or even automatically filing complaints.”, Warning users and blocking sites are a means of controlling access to a domain).”
In reference to claim 14.
“14. The system of claim 13, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:” (preamble)
“receive one or more access rules; and control user access to the webpages associated with the domains in the list of domains according to the received one or more access rules (Matic 631, “Independently of the legal dimension of the matter, being able to identify such URLs programmatically in real time, opens up the road for additional proactive measures such as warning users, blocking third-parties, or even automatically filing complaints.”, Warning users and blocking sites are a means of controlling access to a domain, the URL Identified and blocked is the rule that is received).”
In reference to claim 15.
Matic teaches:
“15. (Currently Amended) A non-transitory computer-readable medium storing computer executable instructions stored therefore that when executed by at least one processor cause the at least one processor to:”
“receive a list of domains, wherein each domain in the list of domains was associated with a category of the plurality of categories by a classifier;” (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology. […] Figure 1 illustrates how we blend crowd-sourcing (done by Curlie) with automated and a manual steps (done by us) on the thin-waist of an overall methodology that can identify the sensitive part of the Web (Section. 4).”, The data from Curlie are classified manually in addition to automatically by a machine learning classifier)
“receive an indication of a new domain by the computing device, wherein the new domain is not in the list of domains;” (Matic 628, “Classifying the Common Crawl corpus. The 3 Billion web pages of the October 2019 snapshot are partitioned into 56k zipped archives, totaling 10 Terabytes of disk space. After the classification, we manually assessed the classifier accuracy by sampling around one hundred URLs for each category and verified that the average accuracy of the classifier was above 90%”, The Common Crawl dataset consists of domains that were not in the original list i.e. the Curlie dataset)
“in response to the indication, retrieve a set of webpages published at the new domain;” (Matic 628, “The dispatcher iterates over all the archives, loads them in memory in their uncompressed format, and assigns each pointer in memory to a worker. The worker extracts the file, removes error pages, non-English documents and any content with less than 1,000 characters.”)
“for each webpage of the set of webpages:”
“extract a plurality of features from the webpage of the set of webpages;” (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”)
“and associate one or more categories of the plurality of categories with the webpage using the classifier and the plurality of features extracted from the webpage;” (Matic Table 6, The table depicts the categories and domains assigned to each one)
“for each category of the plurality of categories:”
“determine the percentage of webpages of the set of webpages published at the new domain that were associated with the category by the classifier;” (Matic Table 7, The table depicts the percentage of webpages associated with each category)
“associate the category with the new domain” (Matic 622, “As final assessment we study the differences among per-URL and per-domain categorization. Our goal is to understand the possible benefits of having categories assigned to individual URLs instead of using a the same category for all the elements under an [Effective Second Level Domain].”)
“and add the new domain and any associated categories to the list of domains.” (Matic Table 6, “URLs”, “ADDED”)
Klinkott teaches:
“determine the threshold percentage set for the category, wherein each category was set with a different threshold percentage; determine that the percentage of webpages is greater than the threshold percentage for the category; and in response to determining that the percentage of webpages is greater than the threshold percentage for the category, [associate the category with the new domain];” (Klinkott [0039], “In an exemplary embodiment, the categorizer 72 may be configured to use any one of multiple possible techniques for completing categorizations of web content based on existing categorizations. In this regard, for example, one technique that may be employed includes the categorization of a particular web page based on the categorizations of web pages to which the particular web page links. As such, for example, if a threshold number (e.g., two or more, a majority, a fixed percentage, etc.) of web pages to which the particular web page links have the same category, the particular web page may be assigned to the category that is shared between the web pages to which the particular web page links. Meanwhile, if several (or most) of the web pages to which the particular web page links do not have the same category, but have similar categories, then a broader category that may encompass all or a threshold percentage of the similar categories may be assigned to the particular web page.”)
In reference to claim 16.
“16. The computer-readable medium of claim 8,” (preamble)
Matic teaches:
“wherein the plurality of features comprises text features (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C).”) [and script features].”
Persson teaches:
“[wherein the plurality of features comprises text features] and script features (Persson 75, “For example, a very important feature that could make or break a web scraper is the support of scraping JavaScript pages. Even though a webpage might not rely on JavaScript dynamically loading its data at the time of development, it is possible that it might migrate to this form in the future. If a scraper supports JavaScript, it could still be possible to change the scraper to a working version. If it does not support JavaScript at all, a brand new, different, web scraper would have to be developed.”).”
In reference to claim 17.
Matic teaches:
“17. The computer-readable medium of claim 8,” (preamble)
A separate embodiment of Matic teaches:
“wherein the classifier is a neural network (Matic 624, “Classification algorithms. There is a wide range of popular algorithms that are suitable for classifying web pages. Examples are K-Nearest-Neighbors [19, 41, 49], Naïve Bayes [32, 33, 48], Support Vector Machines [21, 22, 77, 90], Decision Trees [35, 80, 85], Neural Networks [42, 56] and different variations [59], maximum entropy [23, 49]. Given the scope of this work, we choose the Naïve Bayes classification algorithm for the reasons explained in Section 3.2.”).”
In reference to claim 19.
“19. The computer-readable medium of claim 15, further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the system to:” (preamble)
“receive indications of a training set of webpages (Matic 624, “During this process, we reserve 70% of the input for the training phase and the remaining 30% for testing.”), wherein each webpage in the training set is associated with one or more categories of the plurality of categories (Matic 621, “Curlie contains 3.3 millions annotated web pages, that cover 1 million different categories organized as a hierarchical ontology.”);”
“for each webpage of the training set of webpages, extract one or more features from the webpage (Matic 624, “We extract all the text from the visible content, we call this input source web page content (C). Similarly, we refer to the content obtained from the <META> tag as meta-data (M). […] Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”);”
“and for each webpage of the training set of webpages, train the classifier using the one or more extracted features and the one or more categories associated with the webpage (Matic 624, “We train the classifier using the training set described in Section 2.4. Such set contains 221,712 web pages, with the corresponding GDPR category as label. From each web page we extract both the human readable text and the meta-data information. Next, we filter the content by applying the preprocessing steps described in Section 3.1. This procedure generates a final set of 218,696 URLs with content. Finally we use Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec [60] & Doc2Vec [50] to extract the features.”).”
In reference to claim 20.
“20. The computer-readable medium of claim 15,” (preamble)
“further comprising computer executable instructions stored therefore that when executed by the at least one processor cause the at least one processor to use the list of domains and associated one or more categories to control user access to webpages associated with the domains in the list of domains (Matic 631, “Independently of the legal dimension of the matter, being able to identify such URLs programmatically in real time, opens up the road for additional proactive measures such as warning users, blocking third-parties, or even automatically filing complaints.”, Warning users and blocking sites are a means of controlling access to a domain).”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CODY RYAN GILLESPIE/Examiner, Art Unit 2147 /NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147