Remarks
Claims 1-16 and 18-20 are pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/14/2025 has been entered.
Response to Arguments
Applicant's arguments filed 8/14/2025 have been fully considered but they are not persuasive.
Applicant states, with respect to the IDS objection, “We apologize for the oversight. In accordance to Section MPEP 609.02 the examiner of the continuing application will consider information which has been considered by the Office in the parent application. We therefore ask you consider these references.” However, many references on this IDS were not cited in the parent application. Thus, a proper IDS must be filed such that the Examiner can consider such references.
Applicant appears to copy in the analyzing limitation of claim 1 and alleges “that none of the references whether taken singly or in combination teach or suggest such feature, thus the claims are patentable over the cited art of record and should be allowed.” However, Applicant fails to even bring up any specific reference or provide any argument. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
To the contrary, Hellstrom and Amsterdamski each disclose at least a portion of this limitation as follows:
Hellstrom discloses …
…
Analyzing data of dark web content in the structural database to determine and store in a search engine at least one statistic of dark web content of the at least one dark web page and to determine by employing machine learning and storing in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one of a behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, 142, 145-154, and associated figures; analyzing the results, web pages, links, URLs, etc., and storing results, web pages, links, URLs, etc., refining search queries, iterating the above, and the like, for example);
…
Amsterdamski, however, discloses that the calculation is of at least one behavioral pattern of a web surfer engaged with the web content of the at least one web page and an interaction of the web surfer with at least one other web surfer (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; analyze the above to find further relationships, targets, friends, nicknames, associated accounts, associated webpages, etc., as examples). …
Therefore, the references clearly disclose the subject matter for which Applicant provides a general allegation.
Information Disclosure Statement
The information disclosure statement filed 1/29/2023 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because Applicant has provided an erroneous statement. Applicant states “Unless indicated otherwise, to the best of our knowledge and procedures, each item of information contained in the information disclosure statement was first known no more than thirty days prior to the filing of the information disclosure statement, either by the inventor named in the application, Attorney or agent who prepares or prosecutes the application, and every other person who is substantively involved in the preparation or prosecution of the application and who is associated with the inventor, the applicant, an assignee, or anyone to whom there is an obligation to assign the application.” However, this statement is in direct contradiction to the IDS filed 6/27/2018, IDS filed 10/2/2019, IDS filed 1/7/2021, response dated 7/23/2020, response dated 12/30/2020, response dated 11/29/2021, and response dated 7/6/2022 in application #16/066,315. Each of these references at least one reference found in the IDS in the instant application dated 1/29/2023. However, Applicant alleges that none of these references were known to the inventor, attorney, agent, applicant, assignee, etc. more than thirty days prior to the IDS. However, this statement appears to be false based on many responses signed by Martin Moynihan (who also signed the IDS statement in question) referencing at least one document cited in the IDS. Thus, the IDS cannot be considered, due to this statement that appears to be false. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 and 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 states “…determine by employing machine learning and store in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer”. However, the application as originally filed does not appear to contain basis for this subject matter of employing machine learning to determine and store at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer in a knowledge database. In fact, an adjusted calculation is only referenced one time in the application as originally filed and is not related to this claim limitation, since it does not employ machine learning, is not an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer, and the like. Claims 2-16 and 18-20 are rejected at least based on their dependencies.
Claim 1 states “analyzing data of dark web content in the structural database to determine and store in a search engine at least one statistic of dark web content of the at least one dark web page and to determine by employing machine learning and store in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer”. However, the application as originally filed does not appear to contain basis for analyzing data of dark web content to itself include “determine and store in a search engine at least one statistic of dark web content of the at least one dark web page” as well as “determine by employing machine learning and store in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer”. In fact, the analyze and determine and store steps are separate in the application as originally filed. Claims 2-16 and 18-20 are rejected at least based on their dependencies.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 states “…determine by employing machine learning and store in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer”. It is unclear just how there can be an adjusted calculation when there is no initial calculation to be adjusted. As of now, any calculation including normal computer processing (e.g., any calculation by a computer) meets calculations and adjusted calculations. Claims 2-16 and 18-20 are rejected at least based on their dependencies.
Claim 1 states “analyzing data of dark web content in the structural database to determine and store in a search engine at least one statistic of dark web content of the at least one dark web page and to determine by employing machine learning and store in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer”. However, everything after “analyzing data of dark web content in the structural database” appears as though it does not need to occur, since it is simply intended use that does not necessarily occur. Thus, it is indefinite since it does not define anything. Moreover, the analyzing step does not include determining or storing as in the limitation. Claims 2-16 and 18-20 are rejected at least based on their dependencies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 6-15, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hellstrom in view of Amsterdamski (U.S. Patent Application Publication 2013/0151616).
Regarding Claim 1,
Hellstrom discloses a method of providing searchable database and prioritized search user interface for exploring dark web content and surfer activity, comprising:
Obtaining data of dark web content comprising at least one dark web page scanned and collected from the dark web using a repository (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; deep web mining, crawling, collecting, of hidden web pages/sites in the deep web, extracting data therefrom, classifying results, filtering irrelevant results, etc., as examples);
Extracting from the data of dark web content and storing in a structural database at least one structural parameter of a content of the at least one dark web page (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; storing local copies, parameters, etc., for example);
Analyzing data of dark web content in the structural database to determine and store in a search engine at least one statistic of dark web content of the at least one dark web page and to determine by employing machine learning and storing in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one of a behavioral pattern of a dark web surfer engaged with the dark web content of the at least one dark web page and an interaction of the dark web surfer with at least one other dark web surfer (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, 142, 145-154, and associated figures; analyzing the results, web pages, links, URLs, etc., and storing results, web pages, links, URLs, etc., refining search queries, iterating the above, and the like, for example);
For each of one or more search results of a search query to the structural database by the search engine, calculating a score according to a set of defined criteria using data stored in the search engine and the knowledge database (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; determining relevant results, pages with highest word counts, ranking based on word/phrase frequencies, proximity weighting, results provided as in figure 14 or the like, results from a search engine already being prioritized based on relevance, or the like, as examples);
Determining prioritization of the one or more search results using the score calculated for each (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; determining relevant results, pages with highest word counts, ranking based on word/phrase frequencies, proximity weighting, results provided as in figure 14 or the like, results from a search engine already being prioritized based on relevance, or the like, as examples; as above, for example); and
Providing over a communication network to at least one computing device an output of the one or more search results prioritized according to the prioritization (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; displaying results or the like, for example);
But may not explicitly disclose that the calculation is of the behavioral pattern and the interaction.
Amsterdamski, however, discloses that the calculation is of at least one behavioral pattern of a web surfer engaged with the web content of the at least one web page and an interaction of the web surfer with at least one other web surfer (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; analyze the above to find further relationships, targets, friends, nicknames, associated accounts, associated webpages, etc., as examples). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention, which is before any effective filing date of the claimed invention, to incorporate the target profiling techniques of Amsterdamski into the deep web mining system of Hellstrom in order to allow the system to mine target user information, to provide for mining of additional sources of information, improve the likelihood of discovering information related to a particular search, and/or to improve accuracy of mining.
In the alternative, Amsterdamski, however, discloses a method of providing searchable database and prioritized search user interface for exploring dark web content and surfer activity, comprising:
Obtaining data of web content comprising at least one web page scanned and collected from the dark web using a repository (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; following leads, accessing sites, pages, social networks, posts, comments, etc., in order to find information relevant to a particular target, circle of friends, related people, or the like, as examples);
Extracting from the data of web content and storing in a structural database at least one structural parameter of a content of the at least one web page (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; extract data from the above, for example);
Analyzing data of web content in the structural database to determine and store in a search engine at least one statistic of web content of the at least one web page and to determine by employing machine learning and storing in a knowledge database at least one profile characterization obtained as an adjusted calculation of at least one behavioral pattern of a web surfer engaged with the web content of the at least one web page and an interaction of the web surfer with at least one other web surfer (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; analyze the above to find further relationships, targets, friends, nicknames, associated accounts, associated webpages, etc., as examples);
For each of one or more search results of a search query to the structural database by the search engine, calculating a score according to a set of defined criteria using data stored in the search engine and the knowledge database (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; prioritize based on relationship, weights, etc., as examples);
Determining prioritization of the one or more search results using the score calculated for each (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; as above, as examples); and
Providing over a communication network to at least one computing device an output of the one or more search results prioritized according to the prioritization (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; outputs, for example). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention, which is before any effective filing date of the claimed invention, to incorporate the target profiling techniques of Amsterdamski into the deep web mining system of Hellstrom in order to allow the system to mine target user information, to provide for mining of additional sources of information, improve the likelihood of discovering information related to a particular search, and/or to improve accuracy of mining.
Regarding Claim 2,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Hellstrom discloses that the set of defined criteria comprises at least one of source scoring, recency, user reputation, record type scoring, search result relevance scoring, and content analysis scoring (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures); and
Amsterdamski discloses that the set of defined criteria comprises at least one of source scoring, recency, user reputation, record type scoring, search result relevance scoring, and content analysis scoring (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 3,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Hellstrom discloses that the at least one statistic comprises at least one of date in which most of comments were written, number of posts a surfer wrote for a specific search query, distribution of categories in a site, time line trending for a specific search query, and top sites for a specific query (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures); and
Amsterdamski discloses that the at least one statistic comprises at least one of date in which most of comments were written, number of posts a surfer wrote for a specific search query, distribution of categories in a site, time line trending for a specific search query, and top sites for a specific query (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 4,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Hellstrom discloses that determination of the at least one profile characterization comprises at least one of analyzing sentiment of comments on posts to calculate reputation evaluation, classifying posts into categories and summing posts in each category to determine fields of interest, monitoring a number of interactions between surfers and identifying groups having a number of interactions above a predetermined threshold, and analyzing activity times (Exemplary Citations: for example, 38, 43, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; times between downloading pieces of data, for example); and
Amsterdamski discloses that determination of the at least one profile characterization comprises at least one of analyzing sentiment of comments on posts to calculate reputation evaluation, classifying posts into categories and summing posts in each category to determine fields of interest, monitoring a number of interactions between surfers and identifying groups having a number of interactions above a predetermined threshold, and analyzing activity times (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 6,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Amsterdamski, however, discloses that the analyzing further comprises identifying usage of different aliases by the dark (Hellstrom: Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, 142, 145-154, and associated figures) web surfer using identity matching (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; creating unified name, finding different names, nicknames, addresses, aliases, etc., for example).
Regarding Claim 7,
Hellstrom as modified by Amsterdamski discloses the method of claim 6, in addition, Amsterdamski discloses that the identity matching comprises at least one of locating communication information used by more than one surfer, looking for similar aliases excluding common names, locating surfers with similar activity pattern using activity times analysis, locating surfers with similar fields of interest, locating surfers who are active for a certain period and continue being active in other places or by other aliases, locating surfers who post a same content at a same time in different locations, counting most frequent words used by a surfer, and analyzing surfers’ text (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 8,
Hellstrom as modified by Amsterdamski discloses the method of 1, in addition, Hellstrom discloses sending prioritized alerts according to rules defined and stored in an alert rules database responsive to using the search query within monitoring scheduled with relation to at least one alert (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures; alerts may be stopping of a query, displaying results in a user interface, or the like, as examples); and
Amsterdamski discloses sending prioritized alerts according to rules defined and stored in an alert rules database responsive to using the search query within monitoring scheduled with relation to at least one alert (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 9,
Hellstrom as modified by Amsterdamski discloses the method of 8, in addition, Hellstrom discloses that the alert rules database comprises at least one rule selected from the group consisting of define wake up intervals for scheduling of monitoring with relation to the at least one alert, enable search by a key word, enable search by an activity related to a certain surfer, enable search by an activity of a certain group, enable search by a change in trend of a certain key word and enable search by a new phrase or a word that appears more than a predetermined number of times (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures); and
Amsterdamski discloses that the alert rules database comprises at least one rule selected from the group consisting of define wake up intervals for scheduling of monitoring with relation to the at least one alert, enable search by a key word, enable search by an activity related to a certain surfer, enable search by an activity of a certain group, enable search by a change in trend of a certain key word and enable search by a new phrase or a word that appears more than a predetermined number of times (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 10,
Hellstrom as modified by Amsterdamski discloses the method of 1, in addition, Hellstrom discloses providing case management interface configured to enable a user to create a case file in order to manage a research or an investigation (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures); and
Amsterdamski discloses providing case management interface configured to enable a user to create a case file in order to manage a research or an investigation (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 11,
Hellstrom as modified by Amsterdamski discloses the method of 10, in addition, Amsterdamski, however, discloses providing recommendation on adding relevant surfers and/or posts to the case file (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; leads, social circles, suggestions, etc., as examples).
Regarding Claim 12,
Hellstrom as modified by Amsterdamski discloses the method of claim 11, in addition, Amsterdamski discloses that the recommendation being provided according to at least one of building a connection map of existing surfers in the case file, analyzing connections in the connection map and recommending adding surfers that have a strong connection with existing surfers in the case file, identity matching based on similar surfers, surfers that published posts collected in the case file, surfers that are mentioned in existing posts’ content, surfers having similar fields of interest, and posts that have a strong contextual matching that comprises at least one of same classification, same time in a time range of posts in the case file and posts having a words matching up to a certain threshold (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 13,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Hellstrom discloses providing analytical dashboard to enable view of data analysis comprising at least one of categories, number of posts by dates, search results, an option to create an alert from a search, a total number of search results, surfer details, surfer activity analysis, surfer number of posts by dates, surfer categories and surfer connection map (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures); and
Amsterdamski discloses providing analytical dashboard to enable view of data analysis comprising at least one of categories, number of posts by dates, search results, an option to create an alert from a search, a total number of search results, surfer details, surfer activity analysis, surfer number of posts by dates, surfer categories and surfer connection map (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 14,
Hellstrom as modified by Amsterdamski discloses the method of claim 1, in addition, Hellstrom discloses that obtaining the data from the dark web comprises using at least one crawler configured to manage an IP address thereof by at least one of hiding the IP address and changing the IP address, progress from one web page to another using extracted links found in each web page, classify web pages extracted and control operation timing and pace of collection (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures); and
Amsterdamski discloses that obtaining the data from the dark web comprises using at least one crawler configured to manage an IP address thereof by at least one of hiding the IP address and changing the IP address, progress from one web page to another using extracted links found in each web page, classify web pages extracted and control operation timing and pace of collection (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 15,
Hellstrom as modified by Amsterdamski discloses the method of claim 14, in addition, Hellstrom discloses that the at least one crawler being further configured to optimize scanning pace versus secrecy thereof (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, 138, and associated figures; throttling search, for example); and
Amsterdamski discloses that the at least one crawler being further configured to optimize scanning pace versus secrecy thereof (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Regarding Claim 18,
Hellstrom as modified by Amsterdamski discloses the system of claim 17, in addition, Hellstrom discloses finding at least one hidden URL in the dark web, using a hidden service locator connected with the dark web and the at least one crawler, and scanning and collecting from the dark web, using the at least one crawler, at least a portion of the data using the at least one hidden URL (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures; deep web mining, crawling, collecting, of hidden web pages/sites in the deep web, extracting data therefrom, classifying results, filtering irrelevant results, etc., as examples); and
Amsterdamski discloses finding at least one hidden URL in the dark web, using a hidden service locator connected with the dark web and the at least one crawler, and scanning and collecting from the dark web, using the at least one crawler, at least a portion of the data using the at least one hidden URL (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures; following leads, accessing sites, pages, social networks, posts, comments, etc., in order to find information relevant to a particular target, circle of friends, related people, or the like, as examples).
Regarding Claim 19,
Hellstrom as modified by Amsterdamski discloses the system of claim 18, in addition, Hellstrom discloses storing respective of the at least one crawler and a configuration that comprises at least one of at least one initial URL for the at least one crawler to start from, at least one username and at least one password of the at least one crawler, and timing setting of the at least one crawler (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures; at least initial URLs are used to navigate to a URL initially, for example); and
Amsterdamski discloses storing for respective of the at least one crawler and a configuration that comprises at least one of at least one initial URL for the at least one crawler to start from, at least one username and at least one password of the at least one crawler, and timing setting of the at least one crawler (Exemplary Citations: for example, Paragraphs 27, 28, 30-40, 44, 45, 48-57, 80-89, 104-120, and associated figures).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hellstrom in view of Amsterdamski and Begole (U.S. Patent 7,974,849).
Regarding Claim 5,
Hellstrom as modified by Amsterdamski does not appear to explicitly disclose that analyzing activity times comprises calculating a temporal data distribution within a time frame, storing time frame which includes most data, and storing an average and a standard deviation of the temporal data distribution.
Begole, however, discloses that analyzing activity times comprises calculating a temporal data distribution within a time frame, storing time frame which includes most data, and storing an average and a standard deviation of the temporal data distribution (Exemplary Citations: for example, Figures 3, 17-22, and associated written description, Column 3, line 53 to Column 4, line 46; Column 32, lines 3-21, and associated figures; temporal distribution in time frames with averages and standard deviations, for example). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention to incorporate the temporal pattern detection techniques of Begole into the deep web mining system of Hellstrom as modified by Amsterdamski in order to provide for additional means by which to detect patterns, to ensure that patterns are relevant based on times, to use standard mechanisms to determine relevance, and/or to increase extensibility of the system.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hellstrom in view of Amsterdamski and Xia (Yingju Xia et al., “Automatic Wrapper Generation and Maintenance”, pp. 90-99, 2011, obtained from https://aclanthology.org/Y11-1010/).
Regarding Claim 20,
Hellstrom as modified by Amsterdamski discloses the system of claim 17, in addition, Hellstrom discloses analyzing each of the at least one dark web page, finding patterns, creating a wrapper, and saving the wrapped in a wrapper database (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures; analyzing, patterns, and storing in database, for example. Also see rejection of claim 20 in non-final office action dated 12/9/2024, which further describes the structural components that used to be in the claim); and
Extracting data from the respective dark web page according to the wrapper (Exemplary Citations: for example, 38, 46-75, 80-82, 92-101, 109-112, 117-121, 128-135, and associated figures; use of data later, for example. Also see rejection of claim 20 in non-final office action dated 12/9/2024, which further describes the structural components that used to be in the claim);
But does not explicitly use the word “wrapper”.
Xia, however, discloses analyzing each of the at least one web page, finding patterns, creating a wrapper, and saving the wrapped in a wrapper database (Exemplary Citations: for example, Abstract, sections 1, 3-5.2; wrapper generating, wrapper storing, and extraction of data, for example. It is noted that the instant application explicitly references this NPL as providing such functionality, for example, in line 6 of page 17); and
Extracting data from the respective web page according to the wrapper (Exemplary Citations: for example, Abstract, sections 1, 3-5.2). It would have been obvious to one of ordinary skill in the art at the time of applicant’s invention, which is before any effective filing date of the claimed invention, to incorporate the wrapper generation and usage techniques of Xia into the deep web mining system of Hellstrom as modified by Amsterdamski in order to allow the system to make use of wrappers, to allow the system to automatically build wrappers from web pages, and/or to provide additional mechanisms by which to find patterns in data.
Conclusion
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/Jeffrey D. Popham/Primary Examiner, Art Unit 2432
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