DETAILED ACTION
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 .
Status of Claims
Claims 1-3, 5-13, 15-21 are pending of which claims 1, 10 and 11 are in independent form.
Claims 1-3, 5-13, 15-21 rejected on the ground of nonstatutory double patenting.
Claims 1-3, 5-13, 15-21 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 5-13, 15-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s Argument:
Applicant argues, on pages 11-17 of the “Remarks”, that neither one of the prior teaches “applying a machine learning algorithm on the crawled webpage data to generate machine learning-derived URL parameters, wherein the machine learning algorithm analyzes the crawled webpage data including the HTML layer, a visual layer and associated metadata to identifies a identify core content of the crawled webpage data and exclude tracking components of the-crawled webpage data unrelated to the contents of the requested webpage”.
Examiner’s Response:
Examiner respectfully disagrees, the combination of Pogrebezky, and Drapeau clearly teaches, applying a machine learning algorithm on the crawled webpage data (Pogrebezky: a pre-trained random forest machine learning model ¶ [0049], [0145], [0146], support vector machines (SVMs) are supervised learning models ¶ [0187]) wherein the machine learning algorithm analyzes the crawled webpage data including the HTML layer (Pogrebezky: extracting identifiers/tags from HTML ¶ [0109]-[0110], HTML/CSS parsing ¶ [0166]-[0167]), a visual layer (Pogrebezky: extract candidate images ¶ [0166]-[0168], SVM image analysis ¶ [0186]-[0188]) and associated metadata (Pogrebezky: HTML meta tags ¶ [0110]) to identify core content of the crawled webpage data (Pogrebezky: clustering extracted company name/support indicators ¶ [0043], selecting highest ranked clusters ¶ [0115], weighted similarity scoring/ranking of extracted attributes ¶ [0151]-[0157], selecting top remaining candidate content/images ¶ [0186]-[0188]) and exclude tracking components of the-crawled webpage data unrelated to the contents of the requested webpage (Pogrebezky: eliminating redundant information…keeping the best information ¶ [0084], filtering/removing bad image patterns ¶ [0187], ranking/selecting best extracted candidate ¶ [0043], [0115]);
Drapeau discloses, to generate machine learning-derived URL parameters (a URL component part parser 224, and a URL feature generator 228 … to perform one or more types of machine learning model analysis ¶ [0038], URL feature generator 228 is responsible for generating one or more features for performing machine learning based fraud detection. Such features include the selection of specific hash values and/or sets of hash values indicative of various levels of detail of a web page address. That is, a feature may include differentiation between top level domains, another feature may combine the hashes of the sub domain, domain, and top level domain for aggregating fraud across all merchant transactions, and any other set of web page address features and/or combinations ¶ [0048]);
[wherein the structured dataset is indexed] using the machine learning-derived URL parameters (generated URL features used in ML analysis ¶ [0048]).
The arguments regarding the combination is simply not persuasive because the claim no longer relies on four references and only relies on two. Furthermore, examiner specifies that although the references ascend in different application context, both references address similar technological concepts involving: extracting and processing of webpage derived information; analysis of URL related features, classification of extracted information, and indexing of structured data. A person ordinary skilled in the art would recognize that URL driven feature generation techniques, such as those taught by Drapeau, could be applied within Pogrebezky’s crawler/enrichment framework to improve webpage analysis, feature extraction, indexing and dataset generation.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-3, 5-13, 15-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. US 12131351 B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
Claims 1-3, 5-13, 15-21 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of co-pending Application No. 17807267 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
Claim(s) 1-3, 5-13, 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Pogrebezky; Eli et al. (US 20200242170 A1) [Pogrebezky] in view of Drapeau; Ryan et al. (US 20230026121 A1) [Drapeau].
Regarding claims 1, 10 and 11, Pogrebezky discloses, a method for centralized crawling and extracting data points of a webpage using a centralized crawler system (collected seed is processed, via a web crawler of the seed enricher module…web crawler crawls a home webpage ¶ [0044], also see ¶ [0046], [0123]-[0124]), comprising: crawling requested webpage data (webpages can be crawled ¶ [0042], crawling webpages associated with a seed/company ¶ [0123], also see [Abstract], ¶ [0044], [0124]), wherein the requested webpage data is selected based on a plurality of rules (selection rules are the applied ¶ [0043], seed enricher module scores each cluster using a heuristic formula based on one or more features derived from that cluster…seed enricher module scores each cluster…selected cluster having the highest score ¶ [0115]-[0116], the seed enricher module verifies or validates the new information that was used to enrich that seed ¶ [0127], also see ¶ [0102]) and includes a hypertext markup language (HTML) layer (HTML extracted from home webpages ¶ [0042], downloads in HTML source file ¶ [0109], meta tags (html) ¶ [0110], parsing html/CSS files ¶ [0166]-[0167]) and metadata of a requested webpage (metadata that is stored at one or more database systems ¶ [0242], [0254], [0260] and [0115]);
extracting at least one data point that indicates a main element that describes contents of the requested webpage (extracts from the html source file: (1) candidate company names, and (2) support indicators ¶ [0109]-[0113], the extracted attributes that are extracted from a company seed for a particular website are scored ¶ [0151]-[0157], images having the highest scores … were extracted ¶ [0186]-[0188]);
generating at least one enriched data point that provides additional information on the at least one extracted data point (enrich company seed data…additional company information ¶ [0044]-[0049], enriched company seed ¶ [0075], also see ¶ [0078]-[0081], [0126]-[0130]), wherein the additional information is collected from a plurality of data analysis systems (DASs) (search engine…third party APIs, structured information sources ¶ [0045]-[0046]; this teaches collecting information from multiple external data/information systems);
applying a machine learning algorithm on the crawled webpage data (a pre-trained random forest machine learning model ¶ [0049], [0145], [0146], support vector machines (SVMs) are supervised learning models ¶ [0187]) wherein the machine learning algorithm analyzes the crawled webpage data including the HTML layer (extracting identifiers/tags from HTML ¶ [0109]-[0110], HTML/CSS parsing ¶ [0166]-[0167]), a visual layer ( extract candidate images ¶ [0166]-[0168], SVM image analysis ¶ [0186]-[0188]) and associated metadata (HTML meta tags ¶ [0110]) to identify core content of the crawled webpage data (clustering extracted company name/support indicators ¶ [0043], selecting highest ranked clusters ¶ [0115], weighted similarity scoring/ranking of extracted attributes ¶ [0151]-[0157], selecting top remaining candidate content/images ¶ [0186]-[0188]) and exclude tracking components of the-crawled webpage data unrelated to the contents of the requested webpage (eliminating redundant information…keeping the best information ¶ [0084], filtering/removing bad image patterns ¶ [0187], ranking/selecting best extracted candidate ¶ [0043], [0115]);
creating and caching a structured dataset including the crawled requested webpage data, the based on the at least one extracted data point, and the at least one generated enriched data point (generating structured information ¶ [0046], enriched structured company seed information ¶ [0075])
wherein the structured dataset is indexed (indexing, at a search engine of the clusterer and company profile generator module ¶ [0049]).
that define the core content while excluding the tracking components (selecting highest ranked extracted attributes ¶ [0151]-[0157], eliminating redundant information…keeping the best information ¶ [0084], filtering/removing bad image patterns ¶ [0187]).
However, Pogrebezky does not explicitly facilitates to generate machine learning-derived URL parameters; [wherein the structured dataset is indexed] using the machine learning-derived URL parameters.
Drapeau discloses, to generate machine learning-derived URL parameters (a URL component part parser 224, and a URL feature generator 228 … to perform one or more types of machine learning model analysis ¶ [0038], URL feature generator 228 is responsible for generating one or more features for performing machine learning based fraud detection. Such features include the selection of specific hash values and/or sets of hash values indicative of various levels of detail of a web page address. That is, a feature may include differentiation between top level domains, another feature may combine the hashes of the sub domain, domain, and top level domain for aggregating fraud across all merchant transactions, and any other set of web page address features and/or combinations ¶ [0048]);
[wherein the structured dataset is indexed] using the machine learning-derived URL parameters (generated URL features used in ML analysis ¶ [0048]).
It would have been obvious to one ordinary skilled in the art at the time of the filing of the present invention to combine the teachings of the cited references because Drapeau's system would have allowed Pogrebezky to facilitate to generate machine learning-derived URL parameters; [wherein the structured dataset is indexed] using the machine learning-derived URL parameters. The motivation to combine is apparent in the Pogrebezky’s reference, because there is a need to improve technical approach to detecting fraud originating from specific web addresses during online and remote transactions, while preserving the privacy of a user's browsing activity.
Regarding claims 2 and 12, the combination of Pogrebezky, and Drapeau discloses, providing the at least one enriched data point of the webpage to an external entity (Pogrebezky: search engine(s) and third-party APIs can also be used to collect additional company information that can be added to each collected seed. Each collected seed is then enriched by adding all of the additional company information to the original seed data [abstract]. Also see ¶ [0041], [0046], [0047], [0053]. Once a seed has been enriched and reaches the end of the seed enricher pipeline 132-136, the seed enricher module 130 sends the enriched company seeds 139 to the verification module 138. The verification module 138 validates or verifies seed data for each of the enriched company seeds prior to sending them back to the seed master module 112. Each enriched company seed that is successfully validated/verified can then be sent to the seed master module 112, and the seed master module 112 stores or “persists” the enriched company seeds 139 at the repository 124 ¶ [0081]).
Regarding claims 3 and 13, the combination of Pogrebezky, and Drapeau discloses, distributing a subset of the at least one extracted data point to a first DAS of the plurality of DASs, wherein the subset of the at least one extracted data point is determined based on a first filtering rule of the first DAS (Pogrebezky: search engine(s) and third-party APIs can also be used to collect additional company information that can be added to each collected seed. Each collected seed is then enriched by adding all of the additional company information to the original seed data [abstract]. Also see ¶ [0041], [0046], [0047], [0053]. Once a seed has been enriched and reaches the end of the seed enricher pipeline 132-136, the seed enricher module 130 sends the enriched company seeds 139 to the verification module 138. The verification module 138 validates or verifies seed data for each of the enriched company seeds prior to sending them back to the seed master module 112. Each enriched company seed that is successfully validated/verified can then be sent to the seed master module 112, and the seed master module 112 stores or “persists” the enriched company seeds 139 at the repository 124 ¶ [0081]); and
causing generation of the additional information on the subset of the at least one extracted data point (Pogrebezky: Each collected seed is then enriched by adding the additional company information to the original seed data for each collected seed to generate an enriched company seed. The additional company information added to each collected seed can include the missing seed data and the other instances of the original seed data that were fetched by the crawler ¶ [0045]-[0047]).
Regarding claims 4 and 14, (Canceled).
Regarding claims 5 and 15, the combination of Pogrebezky, and Drapeau discloses, wherein the structured dataset includes at least one attribute, wherein the attribute is at least one of: content type, topic, language, sentiment, safety information, and domain information (Pogrebezky: The http protocol is a language that is used on the Internet in order to transfer data and communicate ¶ [0032], domain name/information ¶ [0035], [0038], [0054], A webpage can embed a variety of different types of resources such as: style information which controls a webpage's look-and-feel; scripts which add interactivity to the page; media such as images, sounds, and videos, etc ¶ [0039], original seed data that includes a plurality of attributes each having a type and an associated value ¶ [0044], [0047]. Also see ¶ [0068], [0069], [0074]).
Regarding claims 6 and 16, the combination of Pogrebezky, and Drapeau discloses, subsequently selecting to request the requested webpage data; and retrieving portions of the structured dataset from the cache (Pogrebezky: A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s) ¶ [0274]).
Regarding claims 7 and 17, the combination of Pogrebezky, and Drapeau discloses, wherein the at least one enriched data point is at least one first enriched data point, further comprising: distributing a second subset of the at least one extracted data point to a second DAS of the plurality of DASs, wherein the second subset of the at least one extracted data point is determined based on a second filtering rule of the second DAS; and generating at least one second enriched data point collected from the second DAS, wherein the second DAS is caused to generate the at least one second enriched data point; and adding the at least one second enriched data point to the structured dataset and the cache of the requested webpage (Pogrebezky: In accordance with one embodiment, a company seed enrichment method and pipeline system are provided for finding and validating enhancement information to be added to company seed data to enrich company seed data ¶ [0044]-[0047]. Each of the independent seed source services 106-1 . . . 106-n can output the collected seeds to the seed master module 112. The seed master module 112 can store the seeds temporarily and then persist them at the repository 124 ¶ [0075], [0077], [0081]).
Regarding claims 8 and 18, the combination of Pogrebezky, and Drapeau discloses, wherein the plurality of rules is defined by at least one of: a user demand, a web server, each DAS of the plurality of DASs, a schedule, a domain, and a network traffic (Pogrebezky: A web server provides support for Hypertext Transfer Protocol (HTTP) that specifies how to transfer hypertext (i.e., linked web documents) between two computers. HTTP provides clear rules for how a client and server communicate ¶ [0034]. Also see selection rules ¶ [0043], [0116]).
Regarding claims 9 and 19, the combination of Pogrebezky, and Drapeau discloses, wherein extracting the at least one data point further comprises: identifying contents of the requested webpage from the crawled webpage data; applying an algorithm to identify at least one main element and at least one attribute, wherein the at least one main element is identified as the at least one extracted data point; and generating the at least one attribute by classifying the at least one main element (Pogrebezky: In accordance with the disclosed embodiments, to address the problems and challenges mention above, methods, systems and related technologies are provided that automatically build a repository of company profiles by crawling the Internet to find company information from various sources (referred to as company seeds), enrich those company seeds, assemble the enriched company seeds into clusters, pick the best information from each cluster to generate a corresponding company profile for a particular company, and validate the attributes of each company profile. The company profiles can then be stored within a repository, and the repository can eventually be published (if it meets certain quality control measures) for use by users, applications and services ¶ [0041], [0047], [0127]).
Regarding claim 20, the combination of Pogrebezky, and Drapeau discloses, receiving a request for the webpage data from a plurality of external entities; and distributing at least a portion of the structured dataset of the webpage data to the plurality of external entities (Pogrebezky: the seed enricher module fetches desired company information using external sources ¶ [0078], as well as other databases or sources of information that are external to the multi-tenant database system ¶ [0243], [0271]).
Regarding claim 21, the combination of Pogrebezky, and Drapeau discloses, wherein the crawling of the requested webpage is only performed by the centralized crawler system (Pogrebezky: The web crawler crawls ¶ [0044]-[0046], [0079]-[0080]).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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5/27/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
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