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 .
DETAILED CORRESPONDENCE
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 10, 2025 has been entered.
Status of Claims
Claims 1, 3, 6, 7, 8, 9, 11, 12, 15, 16, 17, 18 have been amended.
No claims have been cancelled.
No claims have been added.
Claim Rejections - 35 USC § 112(a)
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 – 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In regards to claims 1 – 20, the Examiner asserts that the specification fails the written description requirement because the specification fails to how the following limitation is achieved:
“wherein analyzing the page content for each web page in the first plurality of web pages utilizes few compute resources than analyzing page content for each web page in the second plurality of web pages”
Specifically, the specification relies what the applicant terms “poorly crafted” (see ¶ 31, 60), however, the specification fails to establish what is considered a “poorly crafted” search query and how using a long list of terms and a plurality of Boolean operators would result in fewer compute resources. Referring to ¶ 60 of the applicant’s specification, which states “This can also reduce the amount of computing resources that would otherwise be expended if a poorly crafted search was used, which could result in the identification and analysis of numerous irrelevant web pages,” and ¶ 61, 64 regarding a list of terms that the applicant believes are the “best” search terms, the Examiner asserts that a “poorly crafted search” is broad enough to encompass a search query that is comprised of a single term, e.g., “Nike”, and a better, less resource dependent search query would comprise the search query disclosed in ¶ 64.
However, the Examiner questions how adding more terms and Boolean operators would result in fewer compute resources. Specifically, one of skill in the art would question that if a user is submitting a search query for the Nike trademark and enters the singular term “Nike” into (for example) the Google search box and clicks enter, how is this using more computing resources when compared to requiring the computing system to expend additional computing resources to execute the additional functions of first receiving the term “Nike”, searching a database of templates, determining which template to select/use, populating the template with the term “Nike” and a list of additional search terms, and then executing the template-based query. As can be seen, the latter would require far more resources than a user simply typing in “Nike” and “hitting” “Enter”. Further still, requiring the system to search for a plurality of terms with a plurality of Boolean operators would require far more compute resources in order to satisfy the plurality of search query rules it has been given than a singular search term.
Moreover, since a “poorly crafted search” can encompass a single search string, e.g., “Nike sneakers”, how is the search string in ¶ 65, which recites a similar search string, use fewer compute resources, e.g., GOOGLE| “clean break”? Further still, ¶ 62 recites that a plurality of templates can be used. As a result, if multiple templates are being used, which, in turn, results in multiple search strings being carried out, how are fewer compute resources being used. Although ¶ 62 recites that multiple templates can be used so that they are directed to specific sub-parameters, e.g., country, how is this using any fewer compute resources than a user performing the same search using Google.co.uk, Google.com.au, or etc.? Moreover, how is this using fewer compute resources when ¶ 62 is explicitly stating that the same search, but with additional searching parameters, is being performed multiple times? ¶ 60 also recites that multiple searches can be performed by experimentation, thereby resulting in more compute resources being used because the invention explicitly states that it is relying on experimentation.
Finally, how is the invention using fewer compute resources when it is reliant on existing search engine technology? That is to say, the invention is not creating a new more efficient or better search engine nor is it improving upon existing search engines. The invention is still relying on the same underlying technology to perform the search and, consequently, one of skill in the art would not know what the applicant was in possession of to demonstrate that the invention is utilizing fewer compute resources (see at least ¶ 60 – 67).
Therefore, the Examiner asserts that the specification fails the written description requirement as one of skill in the art would not be able to identify what it is that the applicant was in possession of to result in a computing system to utilize fewer compute resources simply because it is using a template or because the template is directed to a single search engine because it was desired to search a single search engine in as much the same way that a user who provided a poorly crafted search decided to go to Google to perform their search; or alternatively, what was in possession by the applicant to differentiate how using multiple templates to experiment with different search strategies would result in using any less compute resources than a user performing multiple searches?
The Examiner asserts that the examples provided by the specification are not directed towards computing techniques that reduce the number of computing resources needed, but techniques that reduces the physical/mental burden of a user because the system has been programmed with a plurality of templates that can be utilized in order to leverage the available and increased computing resources. The Examiner asserts that reducing the physical/mental load of a human searching for a particular topic is not the same as actually reducing the amount of computing power that a computing system requires to run a particular search. The Examiner asserts that the searching process itself, wherein the applicant explicitly admits to using existing searching technology, e.g. Google, remains the same and is not being improved upon and that the invention is, in fact, simply attempting to run less searches because they are attempting to write a search query that includes every term under the sun that could be associated with the search query, which, again, would only result in an increase of computing resources to run that particular search query. The Examiner asserts that the same, if not, more computing resources are being utilized to run a single search string due to the complexity of the search string increasing and the number of terms that it must now search for. Could this be a more efficient use of the user’s time? Probably? Are fewer computing resources being utilized to execute this more complex search query versus a user simply performing a search on the term “Nike”? In light of the specification, this is not the case. The specification simply recites a goal or objective without providing sufficient evidence or disclosure of how this goal or objective is achieved and, on the contrary, provides evidence that more computing resources are being used.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
receiving information about the trademark, the information about the trademark including at least a trademark name, an owner name and goods/services information;
selecting a template based at least on the information about the trademark, the template designed to capture web pages that utilize a provided trademark name as a brand or in a commercial context;
automatically generating search queries by populating the selected template with at least a portion of the information about the trademark;
submitting the search queries to obtain a first set of search results pertaining to a first plurality of web pages, the first set of search results comprising at least a Uniform Resource Locator (URL) associated with each web page in the first plurality of web pages, the first plurality of web pages having fewer web pages than a second plurality of web pages pertaining to a second set of search results that would result in submitting, search queries generated from the portion of the information without populating the selected template;
for each web page in the plurality of web pages:
obtaining page content of the web page by accessing the URL associated with the web page;
analyzing the page content based at least the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
generating a commercial score based at least on one or more commerce-related elements within the page content of the web page, the commercial score indicative of a degree of confidence that the web page relates to commerce; and
presenting information about each web page in a subset of web pages of the first plurality of web pages, each web page in the subset of web pages having a respective brand score that exceeds a threshold, the information about each web page in the subset of web pages including at least the URL of the web page and a representation of the respective brand score and the commercial score associated with the web page
The invention is directed towards the abstract idea of collecting and comparing information and, based on a rule, identify options for the purpose of identifying misuse of a trademark, which corresponds to “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by a human, e.g., receiving trademark information, selecting a template and populating, i.e. writing, the template with the received information, searching various sources of information, comparing the sources of information with trademark information and, if there is a match, referring to a rule to determine if the source of information is allowed use of the trademark and, if not, notify another user, e.g., owner of the trademark.
The limitations of:
receiving information about the trademark, the information about the trademark including at least a trademark name, an owner name and goods/services information;
selecting a template based at least on the information about the trademark, the template designed to capture web pages that utilize a provided trademark name as a brand or in a commercial context;
automatically generating search queries by populating the selected template with at least a portion of the information about the trademark;
submitting the search queries to obtain a first set of search results pertaining to a first plurality of web pages, the first set of search results comprising at least a Uniform Resource Locator (URL) associated with each web page in the first plurality of web pages, the first plurality of web pages having fewer web pages than a second plurality of web pages pertaining to a second set of search results that would result in submitting, search queries generated from the portion of the information without populating the selected template;
for each web page in the plurality of web pages:
obtaining page content of the web page by accessing the URL associated with the web page;
analyzing the page content based at least the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
generating a commercial score based at least on one or more commerce-related elements within the page content of the web page, the commercial score indicative of a degree of confidence that the web page relates to commerce; and
presenting information about each web page in a subset of web pages of the first plurality of web pages, each web page in the subset of web pages having a respective brand score that exceeds a threshold, the information about each web page in the subset of web pages including at least the URL of the web page and a representation of the respective brand score and the commercial score associated with the web page
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium. That is, other than reciting a generic processor executing computer code stored on a computer medium nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium in the context of this claim encompasses a human receiving trademark information, searching various sources of information, comparing the sources of information with trademark information and, if there is a match, referring to a rule to determine if the source of information is allowed use of the trademark and, if not, notify another user, e.g., owner of the trademark.
In regards to, “wherein analyzing the page content for each web page in the first plurality of web pages utilizes few compute resources than analyzing page content for each web page in the second plurality of web pages” or that the use of a template results in this goal being achieved, the Examiner asserts that the limitation is broad with respect to how this is specifically being performed or how simply populating a template, i.e. writing information into a form, would result in using less computing resources, especially because there are a wide range of factors that can affect the execution and required resources of a search query, such as, but not limited to, length, complexity, additional parameters, how terms have been indexed by a search engine, Boolean operators used, and etc. Moreover, what makes up the characteristics of a query so that it differs from a non-template-based query or how it would use fewer computing resources. As a non-limiting example, if a user is submitting a search query for the Nike trademark and enters the singular term “Nike” into (for example) the Google search box and clicks enter, how is this using more computing resources when compared to requiring the computing system to expend additional computing resources to execute the additional functions of first receiving the term “Nike”, searching a database of templates, determining which template to select/use, populating the template, and then executing the template-based query. As can be seen, the latter would require far more resources than a user simply typing in “Nike” and “hitting” “Enter”.
The Examiner asserts that this is no different than a human writing down information in a template form to allow a second user to “zero-in” on the particulars of the first user’s query as this would result in the standardization of information, allow the second user to quickly glance over the form and extract the information that is being requested, and not have to enter in a back-and-forth conversation with the first user. The Examiner asserts that this is similar to a librarian receiving a filled-out form for a particular book or books directed to a subject, wherein requesting users fill out their respective forms indicating that they desire books about subject matter X, during a particular time, with at least Y number of pages, and/or etc., thereby allowing the librarian to waste less of their time speaking with each requesting user, writing down the information each of them desires, requesting clarification, and so forth because the librarian can simply pick up each form, quickly identify what is being requested, and go find the desired subject matter.
Additionally, in view of MPEP § 2106.05(f) 1 and 2, the Examiner asserts that the limitation is directed towards an idea of a solution or outcome that is based on what the applicant believes are the best search terms (¶ 60 of the applicant’s specification, “This can also reduce the amount of computing resources that would otherwise be expended if a poorly crafted search was used, which could result in the identification and analysis of numerous irrelevant web pages.”; ¶ 61, 64 regarding a list of terms that the applicant believe are the best search terms) and that the specification simply states that using a template with terms that the applicant believes, in their mind, are “better” search terms will result in fewer compute resources and since a computer is being used the search for information will be performed faster, more efficient, and etc.
Further still, and as was discussed in the rejection under 35 USC 112(a), the Examiner also asserts that due to the generic and high-level nature in which this idea of a solution has been presented, the Examiner questions whether fewer resources are actually being used. Specifically, the basis of the applicant’s specification and aforementioned limitation is based on a user not writing/typing a “good” search query, e.g., using only a single term/phrase, and, therefore, their invention produces better results because, in their opinion/mind, better search terms are being used, e.g., ¶ 64. However, as evidenced by the applicant’s own example, i.e. ¶ 64, how would using more search terms result in using fewer compute resources compared to a user who simply typed in a single term? That is to say, the specification discloses that because the invention is using more search terms and more Boolean operators, fewer resources are being used because the query would result in better results compared to a bad search query created by a user, but, again, ¶ 64 is an example where more resources would be used because, in the applicant’s opinion, a “bad” search query was used, i.e. a single term/phrase comprised the search query.
The claimed invention is doing nothing more than reciting generic technology at a high level of generality and applying it to the abstract idea, wherein the selection of a template and its population of information are activities that can be performed by a human and/or with the aid of pen and paper, while also generically reciting a goal, i.e. using fewer compute resources because a template is being used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium to communicate and present information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. collecting and comparing information and, based on a rule, identify options, as was discussed above. The generic processor executing computer code stored on a computer medium in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium can perform the insignificant extra solution steps of communicate and present information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium are merely being applied to perform the steps that can be performed in the human mind or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium.
Although the claim recites “training a machine learning algorithm,” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a trained machine learning model, or multiple models, is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards collecting and comparing information and, based on a rule, identify options for the purpose of identifying misuse of a trademark. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and re-training are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor executing computer code stored on a computer medium to perform the steps of:
receiving information about the trademark, the information about the trademark including at least a trademark name, an owner name and goods/services information;
selecting a template based at least on the information about the trademark, the template designed to capture web pages that utilize a provided trademark name as a brand or in a commercial context;
automatically generating search queries by populating the selected template with at least a portion of the information about the trademark;
submitting the search queries to obtain a first set of search results pertaining to a first plurality of web pages, the first set of search results comprising at least a Uniform Resource Locator (URL) associated with each web page in the first plurality of web pages, the first plurality of web pages having fewer web pages than a second plurality of web pages pertaining to a second set of search results that would result in submitting, search queries generated from the portion of the information without populating the selected template;
for each web page in the plurality of web pages:
obtaining page content of the web page by accessing the URL associated with the web page;
analyzing the page content based at least the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
generating a commercial score based at least on one or more commerce-related elements within the page content of the web page, the commercial score indicative of a degree of confidence that the web page relates to commerce; and
presenting information about each web page in a subset of web pages of the first plurality of web pages, each web page in the subset of web pages having a respective brand score that exceeds a threshold, the information about each web page in the subset of web pages including at least the URL of the web page and a representation of the respective brand score and the commercial score associated with the web page
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 2 is directed towards the recitation of generic technology and applying it to the abstract idea (See also 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, Example 47, Claim 2).
Claim 3 is directed towards human activities and basic filtering of information based on a comparison against known information.
Claims 4, 5 are directed to the collection and comparison of information to determine a score.
Claim 6 is directed to the collection and organization of information and the extra solution activity of presenting (displaying) information while also reciting generic technology and applying it to the abstract idea.
Claims 7, 8 are directed towards the collection and comparison of information and the extra solution activity of presenting (displaying) information while also reciting generic technology and applying it to the abstract idea.
The remaining claims are similar to what has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for identifying misuse of a trademark. Accordingly, the claims are not patent eligible.
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 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, 2, 4 – 11, 13 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rozhnov (US PGPub 2021/0279743 A1) in view of Hess et al. (US PGPub 2014/0330866 A1).
In regards to claim 1, Rozhnov discloses a computer-implemented method performed by a server for obtaining evidence of online commercial use of a trademark, comprising:
receiving, by the server, information about the trademark, the information about the trademark including at least a trademark name, an owner name and goods/services information (Fig. 3A; ¶ 61, 62, 66, 70, 73, 92, 113, 114, 115 wherein the system receives information about a trademark, which includes the trademark (brand) name, owner name, and goods/services information);
[…] capture web pages that utilize a provided trademark name as a brand or in a commercial context (Fig. 3A; ¶ 61, 62, 66, 73, 92, 112, 113, 114, 115 wherein the system captures web pages that contain information about a trademark, which includes the trademark (brand) name, owner name, and goods/services information);
In regards to:
automatically generating, by the server, search queries […] with at least a portion of the information about the trademark;
submitting the search queries to an Internet search engine to obtain a set of search results pertaining to a first plurality of web pages the first set of search results comprising at least a Uniform Resource Locator (URL) associated with each web page in the first plurality of web pages […]
(Fig. 3A; ¶ 64, 65, 66, 67, 70, 74, 80 wherein search queries are generated and submitted to at least one Internet search engine to obtain search results pertaining to web pages and websites concerning the trademark.);
In regards to:
for each web page in the plurality of web pages:
obtaining, by the server, page content of the web page by accessing the URL associated with the web page;
analyzing, by the server, the page content based at least on the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
generating, by the server, a commercial score based at least on one or more commerce-related elements within the page content of the web page, the commercial score indicative of a degree of confidence that the web page relates to commerce
(Fig. 1; ¶ 59, 61, 68, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources, which further allows for determining the brand score for a web page
Fig. 3A; ¶ 66, 67, 70, 78, 80, 85, 87 – 104, 111 – 118 wherein the system obtains the contents of a webpage by accessing its website, analyzes the contents of the webpage based on the trademark, owner, and goods/services to generate a brand score that is indicative of the degree of confidence that the web page includes the trademark, e.g., relevancy, illegitimate/illegal use of the trademark, presence of a domain associated with legitimate or illegitimate domain, as well as generate a commercial score that is indicative of a degree of confidence that the web page relates to commerce, e.g., online shop, counterfeiting, and etc., and determination that the web page is distributing and/or selling counterfeit items, is an fake online shop, is not allowed to distribute/sell the brand name product),
wherein the analyzing the page content for each web page in the first plurality of web pages utilizes fewer compute resources than analyzing page content for each web page in the second plurality of web pages
(In regards to, “wherein the analyzing the page content for each web page in the first plurality of web pages utilizes fewer compute resources than analyzing page content for each web page in the second plurality of web pages” the Examiner asserts that the limitation is broad with respect to how this is specifically being performed or how simply populating a template would result in using less computing resources, especially because there are a wide range of factors that can affect the execution and required resources of a search query, such as, but not limited to, length, complexity, additional parameters, how terms have been indexed by a search engine, Boolean operators used, and etc. Moreover, what makes up the characteristics of a query so that it differs from a non-template-based query or how it would use fewer computing resources. As a non-limiting example, if a user is submitting a search query for the Nike trademark and enters the singular term “Nike” into (for example) the Google search box and clicks enter, how is this using more computing resources when compared to requiring the computing system to expend additional computing resources to execute the additional functions of first receiving the term “Nike”, searching a database of templates, determining which template to select/use, populating the template, and then executing the template-based query. As can be seen, the latter would require far more resources than a user simply typing in “Nike” and “hitting” “Enter”.
Alternatively, the limitation is also being interpreted to simply cover basic filtering. Referring to the search example provided above, since the results are based on a filtered set of information, i.e. populated template, and are being compared to non-filtered set of information, i.e. non-populated template, then the Examiner asserts that one can argue that less resources are being utilized because less information or a smaller pool of information is being searched through. In this case, because Nike sneakers is a smaller pool of information versus Nike sneakers, pants, shirts, and etc., less resources are being utilized. However, again, this is entirely dependent on the particulars of the search functionality.
As a result, the Examiner asserts that Rozhnov discloses a computer system that has been specifically programmed to receive a search request, analyze the request, generate a search query that is tailored to the specifics of the user’s search request, and performing a search that is designed to provide results that are specifically tailored to the user’s search request, wherein the search request is associated with (as a non-limiting example) a category, specific information about the trademark, information on legitimate and illegitimate domains, and fraudulent web resource information, thereby narrowing the pool of information that needs to searched and custom tailoring the search query with the populated information associated with trademark and requiring less computing resources as less and more tailor information is being used.
However, in the interest of compact prosecution, the Examiner has provided an additional analysis in view of Hess to more explicitly teach this aspect of the invention in the event that the applicant does not agree with this analysis.); and
presenting information about each web page in a subset of web pages of the first plurality of web pages via a user interface (UI), each web page in the subset of web pages having a respective brand score that exceeds a threshold, the information about each web page in the subset of web pages including at least the URL of the web page and a representation of the respective brand score and the commercial score associated with the web page (¶ 66, 69 wherein users are notified about a fraudulent web resource (see ¶ 111 – 118), which is a representation of a brand score and commercial score, i.e. that the web resource is illegitimately or illegally using a trademark and is a fake online shop or selling counterfeit products;
¶ 62, 72, 87, 105, 107, 109, 110, 111 – 118 wherein the system performs a search for online web sources, e.g., websites, that contain the trademark and will categorize the search results into a group that are officially allowed to use the trademark and a group that is not officially allowed to use the trademark (i.e. a first example of a threshold) and then the system performs an analysis to calculate a harmfulness coefficient to determine the level of harm that a website containing the trademark provides (i.e. another example of a threshold)).
Rozhnov discloses a system and method of utilizing a machine learning model to identify and extract keywords from associated with good/services to assist with generating search queries, which, in turn, allows for the identification of online sources that are utilizing intellectual property without permission. Although, Rozhnov discloses techniques that can be used to use fewer computing resources and the categorization of information to allow the system to generate a search query that corresponds to the specific search request, Rozhnov fails to explicitly disclose using templates and, as discussed above, an alternate analysis has been provided to more explicitly teach, “the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results.”
To be more specific, Rozhnov fails to explicitly disclose:
selecting, by the server, a template based at least on the information about the trademark, the template designed to capture web pages that utilize a provided trademark name as a brand or in a commercial context;
automatically generating, by the server, search queries by populating the selected template with at least a portion of the information about the trademark.
submitting the search queries to an Internet search engine to obtain a set of search results pertaining to a first plurality of web pages the first set of search results comprising at least a Uniform Resource Locator (URL) associated with each web page in the first plurality of web pages, the first plurality of web pages having fewer web pages than a second plurality of web pages pertaining to a second set of search results that would result in submitting, to the Internet search engine, search queries generated from the portion of the information without populating the selected template
However, Hess, which is also directed towards search query generation and execution, further teaches that it is well-known in the art to store and select a search query that is the best suited for specifically searching for the particular content that a user is interested in (¶ 4). Hess teaches that users have become more reliant on online search engines to submit queries and find desired information as there are a variety of different search engines for finding desired information from a large pool of available search engines. As non-limiting examples, Hess teaches:
“For example, Google™ and Bing™ provide web sites for conducting generalized web searches. Specialized search engines are available for searching within particular websites or content categories. For example, search engines are available for searching for news, products, jobs, events, entertainment, legal information, medical information, geographic or map information, recipes, friends, real estate and much more. There are also specialized search engines for searching for particular types of content. For example, search engines are available for searching for audio files, video files, local content, and other types of specific information or content.” (¶ 5)
However, Hess further teaches that there are many difficulties that a user may face when determining how to formulate the “best” search query that would produce the most desired results, such as, but not limited to, what terms to use, how to formulate the query, Boolean operators, false positives, and so forth, as well as being computationally intensive (¶ 6 – 8). As a result, Hess teaches a system and method that generates and stores a list of query templates and selects the best query template that can be populated with additional information associated with the content that the user desires that would produce best search results for the user. Hess teaches that one of the benefits of using a template is that users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query. This, in turn, allows the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue (¶ 24, 26). Further, query templates may then be used by web server(s) or computer system(s) to more quickly and accurately parse future search queries (¶ 41). Moreover, query templates may then be used in a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search (¶ 64). Finally, the search results that were obtained by populating and applying a template would be fewer than those that did not utilize a template because the template allows the system to apply specific filters that narrow the search results to those desired by the user rather than a broad query that would provide desired and undesired search results. One of ordinary skill in the art would find this beneficials as many trademark owners apply their trademark to a wide range of products, e.g., Nike applies their trademark to sneakers, pants, shirts, and etc. As a result, applying a template that has been populated with “Nike” and “sneakers” would provide less results than simply submitting a search query that only states “Nike” because the latter would provide search results that includes sneakers, pants, shirts, and etc. that include the Nike branding on them rather than the former only providing search results that include sneakers with the Nike branding on them.
(See also: ¶ 29, 39, 40)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the search query generation and processing system and method of Rozhnov with the ability to utilize templates to generate a search query, as taught by Hess, because users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query, as well as allow for the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue, in addition to, having web server(s) or computer system(s) to more quickly and accurately parse future search queries and allowing a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search, thereby also providing the benefit of using fewer computing resources.
In regards to claim 2, the combination of Rozhnov and Hess discloses the method of claim 1, wherein the automatically generating the search queries based at least on the information about the trademark comprises:
providing the goods/services information to a machine learning model to do one or more of:
extract a set of keywords from the goods/services information; or
identify a set of equivalent terms; and
generating the search queries based in part on one or more of the set of keywords or the set of equivalent terms
(Fig. 1; ¶ 59, 61, 64, 68, 78, 80, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources.).
In regards to claims 4, 13, 19, the combination of Rozhnov and Hess discloses the method of claim 1 (the system of claim 9; the computer-readable storage medium of claim 18), wherein analyzing the page content to generate the brand score comprises:
providing the goods/services information and the page content of the web page to a machine learning model that is trained on descriptions of goods/services of registered trademarks and trademark classes associated therewith to identifying a measure of similarity between the goods/services information and the page content of the web page; and
(Claim 4) determining the brand score for the web page based in part on the measure of similarity
(Claims 13, 19) generating the brand score for the web page based in part on the measure of similarity
(Fig. 1; ¶ 59, 61, 68, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources, which further allows for determining the brand score for a web page. In other words, the model has been trained to identify similarities, which, in turn, allows the system to obtain search results pertaining to web pages and websites concerning the trademark, e.g., relevancy, illegitimate/illegal use of the trademark, presence of a domain associated with legitimate or illegitimate domain, as well as generate a commercial score that is indicative of a degree of confidence that the web page relates to commerce, e.g., online shop, counterfeiting, and etc., and determination that the web page is distributing and/or selling counterfeit items, is an fake online shop, is not allowed to distribute/sell the brand name product.).
In regards to claims 5, 14, 20, the combination of Rozhnov and Hess discloses the method of claim 1 (the system of claim 9; the computer-readable storage medium of claim 18), wherein generating the commercial score comprises:
comparing one or more commerce-related elements within the page content of the web page to a predefined set of expected commercial web page elements; and
determining the commercial score for the web page based at least in part on the results of the comparing
(Fig. 3A; ¶ 62, 66, 67, 72, 78, 80, 85, 87 – 104, 111 – 118 wherein the system obtains the contents of a webpage and compares them to expected commercial web page elements, e.g., online shop, by accessing its website, analyzes the contents of the webpage based on the trademark, owner, and goods/services to generate a brand score that is indicative of the degree of confidence that the web page includes the trademark, e.g., relevancy, illegitimate/illegal use of the trademark, presence of a domain associated with legitimate or illegitimate domain, as well as generate a commercial score that is indicative of a degree of confidence that the web page relates to commerce, e.g., online shop, counterfeiting, and etc., to notify a user of fake online shops, websites illegally using brands or IP, websites distributing illegitimate copies of products/counterfeits, websites leading to other websites. The system is able to differentiate an illegal site from a legitimate site as it stores and refers to information of a list of official web resources of partners of the brand and illegitimate domains, websites, fake online shops, unofficial copies, and the like, i.e. the system compares the commerce-related elements within a page to a predefined set of expected commercial web page elements.).
In regards to claims 6, 15, the combination of Rozhnov and Hess discloses the method of claim 1 (the system of claim 9), wherein presenting the information about each web page in the subset of web pages via the UI comprises:
ranking each web page in the subset of web pages based on one or more of the brand score or the commercial score associated therewith; and
presenting the information about each web page in the subset of web pages via the UI in an order determined by the rankings of the web pages
(Fig. 1; ¶ 59, 61, 68, 85, 87 – 106, 110 – 118, 140 wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources, which further allows for determining the brand score/commercial score for a web page.; ¶ 66, 69 wherein users are notified about a fraudulent web resource (see ¶ 111 – 118), which is a representation of a brand score and commercial score, i.e. that the web resource is illegitimately or illegally using a trademark and is a fake online shop or selling counterfeit products).
In regards to claims 7, 16, the combination of Rozhnov and Hess discloses the method of claim 1 (the system of claim 9), further comprising:
for each web page in the subset of web pages:
obtaining an excerpt of text from the web page; and
identifying one or more words within the excerpt that contributed to the generation of one or more of the brand score or the commercial score for the web page;
wherein the information about each web page that is presented via the UI includes the excerpt of text from the web page with the identified one or more words highlighted
(Fig. 3A, 3C; ¶ 66 wherein the system takes a snapshot identifying the excerpt of words, e.g., trademark/brand name “JBL”, that contributed to the generation of the brand/commercial score as it is 1) highlighting “JBL”; and 2) it is modifying the relevant portions by singling them out via the snapshot, which can be presented via a UI (Fig. 3C)).
In regards to claims 8, 17, the combination of Rozhnov and Hess discloses the method of claim 1 (the system of claim 9), further comprising:
for each web page in the subset of web pages:
identifying one or more of the one or more commerce-related elements or other elements within the page content of the web page that contributed to the generation of one or more of the brand score or the commercial score for the web page; and
modifying the page content of the web page to highlight the identified one or more of the one or more commerce-related elements or other elements;
wherein the information about each web page that is presented via the UI includes a rendering of the modified page content of the web page
(Fig. 3A, 3C; ¶ 66 wherein the system takes a snapshot highlighting the elements, e.g., trademark/brand name “JBL”, that contributed to the generation of the brand/commercial score and where the snapshot is a modification of a webpage as it is 1) highlighting “JBL”; and 2) it is modifying the relevant portions by singling them out via the snapshot, which can be presented via a UI (Fig. 3C)).
In regards to claim 9, Rozhnov discloses a system for obtaining evidence of online commercial use of a trademark, comprising:
In regards to:
at least one processor; and
at least one memory that stores program code that is executed by the at least one processor, the program code comprising
(Fig. 1):
a query generator that:
receives information about the trademark and, the information about the trademark including at least a trademark name, an owner name, and goods/services information (Fig. 3A; ¶ 61, 62, 66, 73, 92, 113, 114, 115 wherein the system receives information about a trademark, which includes the trademark (brand) name, owner name, and goods/services information),
[…]; and
automatically generates search queries […] with at least a portion of the information about the trademark, the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results (Fig. 3A; ¶ 64, 65, 66, 67, 70, 74, 80 wherein search queries are generated and submitted to at least one Internet search engine to obtain search results pertaining to web pages and websites concerning the trademark. In regards to, “the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results” the Examiner asserts that the limitation is broad with respect to how this is specifically being performed or how simply populating a template would result in using less computing resources, especially because there are a wide range of factors that can affect the execution and required resources of a search query, such as, but not limited to, length, complexity, additional parameters, how terms have been indexed by a search engine, Boolean operators used, and etc. Moreover, what makes up the characteristics of a query so that it differs from a non-template-based query or how it would use fewer computing resources. As a non-limiting example, if a user is submitting a search query for the Nike trademark and enters the singular term “Nike” into (for example) the Google search box and clicks enter, how is this using more computing resources when compared to requiring the computing system to expend additional computing resources to execute the additional functions of first receiving the term “Nike”, searching a database of templates, determining which template to select/use, populating the template, and then executing the template-based query. As can be seen, the latter would require far more resources than a user simply typing in “Nike” and “hitting” “Enter”.
As a result, the Examiner asserts that Rozhnov discloses a computer system that has been specifically programmed to receive a search request, analyze the request, generate a search query that is tailored to the specifics of the user’s search request, and performing a search that is designed to provide results that are specifically tailored to the user’s search request, wherein the search request is associated with (as a non-limiting example) a category, specific information about the trademark, information on legitimate and illegitimate domains, and fraudulent web resource information, thereby narrowing the pool of information that needs to searched and custom tailoring the search query with the populated information associated with trademark and requiring less computing resources as less and more tailor information is being used.
However, in the interest of compact prosecution, the Examiner has provided an additional analysis in view of Hess to more explicitly teach this aspect of the invention in the event that the applicant does not agree with this analysis.);
a search results collector that submits the search queries to the Internet search engine to obtain a set of search results pertaining to a plurality of web pages while expending fewer compute resources, the search results pertaining to the plurality of web pages comprising at least a Uniform Resource Locator (URL) associated with each web page in the plurality of web pages(Fig. 3A; ¶ 64, 65, 66, 67, 74, 80 wherein search queries are generated and submitted to at least one Internet search engine to obtain search results pertaining to web pages and websites concerning the trademark;
See the statement provided above with regards to utilizing fewer compute resources);
In regards to:
a web page content analyzer that, for each web page in the plurality of web pages:
obtains page content of the web page by accessing the URL associated with the web page;
analyzes the page content based at least on the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
analyzes the page content of the web page to generate a commercial score indicative of a degree of confidence that the web page relates to commerce
(Fig. 1; ¶ 59, 61, 68, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources, which further allows for determining the brand score for a web page
Fig. 3A; ¶ 66, 67, 78, 80, 85, 87 – 104, 92, 111 – 118 wherein the system obtains the contents of a webpage by accessing its website, analyzes the contents of the webpage based on the trademark, owner, and goods/services to generate a brand score that is indicative of the degree of confidence that the web page includes the trademark, e.g., relevancy, illegitimate/illegal use of the trademark, presence of a domain associated with legitimate or illegitimate domain, as well as generate a commercial score that is indicative of a degree of confidence that the web page relates to commerce, e.g., online shop, counterfeiting, and etc.);
a user interface (UI) manager that presents information about each web page in the plurality of web pages via a UI, the information about each web page including at least the URL of the web page and a representation of the brand score and the commercial score associated with the web page (¶ 66, 69 wherein users are notified about a fraudulent web resource (see ¶ 111 – 118), which is a representation of a brand score and commercial score, i.e. that the web resource is illegitimately or illegally using a trademark and is a fake online shop or selling counterfeit products).
Rozhnov discloses a system and method of utilizing a machine learning model to identify and extract keywords from associated with good/services to assist with generating search queries, which, in turn, allows for the identification of online sources that are utilizing intellectual property without permission. Although, Rozhnov discloses techniques that can be used to use fewer computing resources and the categorization of information to allow the system to generate a search query that corresponds to the specific search request, Rozhnov fails to explicitly disclose using templates and, as discussed above, an alternate analysis has been provided to more explicitly teach, “the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results.”
To be more specific, Rozhnov fails to explicitly disclose:
selects a template based at least on the information about the trademark;
automatically generates search queries by populating the selected template with at least a portion of the information about the trademark, the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results;
However, Hess, which is also directed towards search query generation and execution, further teaches that it is well-known in the art to store and select a search query template that is the best suited for specifically searching for the particular content that a user is interested in (¶ 4). Hess teaches that users have become more reliant on online search engines to submit queries and find desired information as there are a variety of different search engines for finding desired information from a large pool of available search engines. As non-limiting examples, Hess teaches:
“For example, Google™ and Bing™ provide web sites for conducting generalized web searches. Specialized search engines are available for searching within particular websites or content categories. For example, search engines are available for searching for news, products, jobs, events, entertainment, legal information, medical information, geographic or map information, recipes, friends, real estate and much more. There are also specialized search engines for searching for particular types of content. For example, search engines are available for searching for audio files, video files, local content, and other types of specific information or content.” (¶ 5)
However, Hess further teaches that there are many difficulties that a user may face when determining how to formulate the best search query that would produce the most desired results, such as, but not limited to, what terms to use, how to formulate the query, Boolean operators, false positives, and so forth, as well as being computationally intensive (¶ 6 – 8). As a result, Hess teaches a system and method that generates and stores a list of query templates and selects the best query template that can be populated with additional information associated with the content that the user desires that would produce best search results for the user. Hess teaches that one of the benefits of using a template is that users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query. This, in turn, allows the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue (¶ 24, 26). Further, query templates may then be used by web server(s) or computer system(s) to more quickly and accurately parse future search queries (¶ 41). Finally, query templates may then be used in a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search (¶ 64).
(See also: ¶ 29, 39, 40)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the search query generation and processing system and method of Rozhnov with the ability to utilize templates to generate a search query, as taught by Hess, because users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query, as well as allow for the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue, in addition to, having web server(s) or computer system(s) to more quickly and accurately parse future search queries and allowing a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search, thereby also providing the benefit of using fewer computing resources.
In regards to claim 10, Rozhnov discloses the system of claim 9, wherein the query generator provides the goods/services information to a machine learning model to extract a set of keywords from the goods/services information and generates the search queries based in part on the set of keywords (Fig. 1; ¶ 59, 61, 68, 78, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources).
In regards to claim 11, Rozhnov discloses the system of claim 9, wherein the query generator provides the goods/services information to a machine learning model to identify a set of equivalent terms and generates the search queries based in part on the set of equivalent terms (Fig. 1; ¶ 59, 61, 68, 78, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources).
In regards to claim 18, Rozhnov discloses a computer-readable storage medium having computer program logic recorded thereon that when executed by at least one processor causes the at least one processor to perform a method comprising:
receiving information about a trademark, the information about the trademark including at least a trademark name, an owner name and goods/services information (Fig. 3A; ¶ 61, 62, 66, 73, 92, 113, 114, 115 wherein the system receives information about a trademark, which includes the trademark (brand) name, owner name, and goods/services information);
[…];
In regards to:
automatically generating, by the server, search queries […] with at least a portion of the information about the trademark, the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results;
submitting the search queries to the Internet search engine to obtain a set of search results pertaining to a plurality of web pages while expending fewer compute resources, the search results pertaining to the plurality of web pages comprising at least a Uniform Resource Locator (URL) associated with each web page in the plurality of web pages
(Fig. 3A; ¶ 64, 65, 66, 67, 70, 74, 80 wherein search queries are generated and submitted to at least one Internet search engine to obtain search results pertaining to web pages and websites concerning the trademark. In regards to, “the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results” the Examiner asserts that the limitation is broad with respect to how this is specifically being performed or how simply populating a template would result in using less computing resources, especially because there are a wide range of factors that can affect the execution and required resources of a search query, such as, but not limited to, length, complexity, additional parameters, how terms have been indexed by a search engine, Boolean operators used, and etc. Moreover, what makes up the characteristics of a query so that it differs from a non-template-based query or how it would use fewer computing resources. As a non-limiting example, if a user is submitting a search query for the Nike trademark and enters the singular term “Nike” into (for example) the Google search box and clicks enter, how is this using more computing resources when compared to requiring the computing system to expend additional computing resources to execute the additional functions of first receiving the term “Nike”, searching a database of templates, determining which template to select/use, populating the template, and then executing the template-based query. As can be seen, the latter would require far more resources than a user simply typing in “Nike” and “hitting” “Enter”.
As a result, the Examiner asserts that Rozhnov discloses a computer system that has been specifically programmed to receive a search request, analyze the request, generate a search query that is tailored to the specifics of the user’s search request, and performing a search that is designed to provide results that are specifically tailored to the user’s search request, wherein the search request is associated with (as a non-limiting example) a category, specific information about the trademark, information on legitimate and illegitimate domains, and fraudulent web resource information, thereby narrowing the pool of information that needs to searched and custom tailoring the search query with the populated information associated with trademark and requiring less computing resources as less and more tailor information is being used.
However, in the interest of compact prosecution, the Examiner has provided an additional analysis in view of Hess to more explicitly teach this aspect of the invention in the event that the applicant does not agree with this analysis.);
In regards to:
for each web page in the plurality of web pages:
obtaining page content of the web page by accessing the URL associated with the web page;
analyzing, by the server, the page content based at least on the trademark name, the owner name and the goods/services information to generate a brand score indicative of a degree of confidence that the web page includes the trademark; and
analyzing the page content of the web page to generate a commercial score indicative of a degree of confidence that the web page relates to commerce
(Fig. 1; ¶ 59, 61, 68, 85, 87 – 106, 110 – 118, 140 wherein a machine learning algorithm is trained based on data previously accumulated by the computer system, wherein the computer system includes, at least, the data collection module, harmfulness analysis module, damage assessment module, and brand protection manager, to allow for the analysis of web pages/websites and extract relevant words that include or do not include the brand name. The system also retrieves the search results and will rank the fraudulent web resources, which further allows for determining the brand score for a web page
Fig. 3A; ¶ 66, 67, 78, 80, 85, 87 – 104, 92, 111 – 118 wherein the system obtains the contents of a webpage by accessing its website, analyzes the contents of the webpage based on the trademark, owner, and goods/services to generate a brand score that is indicative of the degree of confidence that the web page includes the trademark, e.g., relevancy, illegitimate/illegal use of the trademark, presence of a domain associated with legitimate or illegitimate domain, as well as generate a commercial score that is indicative of a degree of confidence that the web page relates to commerce, e.g., online shop, counterfeiting, and etc.);
presenting information about each web page in the plurality of web pages via a user interface (UI), the information about each web page including at least the URL of the web page and a representation of the brand score and the commercial score associated with the web page (¶ 66, 69 wherein users are notified about a fraudulent web resource (see ¶ 111 – 118), which is a representation of a brand score and commercial score, i.e. that the web resource is illegitimately or illegally using a trademark and is a fake online shop or selling counterfeit products).
Rozhnov discloses a system and method of utilizing a machine learning model to identify and extract keywords from associated with good/services to assist with generating search queries, which, in turn, allows for the identification of online sources that are utilizing intellectual property without permission. Although, Rozhnov discloses techniques that can be used to use fewer computing resources and the categorization of information to allow the system to generate a search query that corresponds to the specific search request, Rozhnov fails to explicitly disclose using templates and, as discussed above, an alternate analysis has been provided to more explicitly teach, “the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results.”
To be more specific, Rozhnov fails to explicitly disclose:
selecting, by the server, a template based at least on the information about the trademark;
automatically generating, by the server, search queries by populating the selected template with at least a portion of the information about the trademark, the search queries configured to cause a computing device to expend fewer compute resources in utilizing an Internet search engine to obtain search results.
However, Hess, which is also directed towards search query generation and execution, further teaches that it is well-known in the art to store and select a search query template that is the best suited for specifically searching for the particular content that a user is interested in (¶ 4). Hess teaches that users have become more reliant on online search engines to submit queries and find desired information as there are a variety of different search engines for finding desired information from a large pool of available search engines. As non-limiting examples, Hess teaches:
“For example, Google™ and Bing™ provide web sites for conducting generalized web searches. Specialized search engines are available for searching within particular websites or content categories. For example, search engines are available for searching for news, products, jobs, events, entertainment, legal information, medical information, geographic or map information, recipes, friends, real estate and much more. There are also specialized search engines for searching for particular types of content. For example, search engines are available for searching for audio files, video files, local content, and other types of specific information or content.” (¶ 5)
However, Hess further teaches that there are many difficulties that a user may face when determining how to formulate the best search query that would produce the most desired results, such as, but not limited to, what terms to use, how to formulate the query, Boolean operators, false positives, and so forth, as well as being computationally intensive (¶ 6 – 8). As a result, Hess teaches a system and method that generates and stores a list of query templates and selects the best query template that can be populated with additional information associated with the content that the user desires that would produce best search results for the user. Hess teaches that one of the benefits of using a template is that users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query. This, in turn, allows the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue (¶ 24, 26). Further, query templates may then be used by web server(s) or computer system(s) to more quickly and accurately parse future search queries (¶ 41). Finally, query templates may then be used in a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search (¶ 64).
(See also: ¶ 29, 39, 40)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the search query generation and processing system and method of Rozhnov with the ability to utilize templates to generate a search query, as taught by Hess, because users often enter search queries in the same manner and by using pre-stored templates of common query forms, a search provider can better determine a user’s intent when parsing a search query, as well as allow for the search provider to better classify terms or phrases of a search query and provide more relevant search results, increase customer satisfaction, stimulate additional use of these search services, and may result in higher revenue, in addition to, having web server(s) or computer system(s) to more quickly and accurately parse future search queries and allowing a search engine for quickly and accurately categorizing terms or phrases of a search query for focusing or otherwise further refining a search, thereby also providing the benefit of using fewer computing resources.
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Claims 3, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rozhnov (US PGPub 2021/0279743 A1) in view of Hess et al. (US PGPub 2014/0330866 A1) in further view of Keren et al. (US PGPub 20210248624 A1).
In regards to claims 3, 12, the combination of Rozhnov and Hess discloses a system and method for performing an online search for illegal or illegitimate uses of a trademark, brand name, or the like. The combination of Rozhnov and Hess discloses that the system maintains a record of online sources that it can analyze to determine if there is any illegal or illegitimate use of a trademark, brand name, or the like and will refer to this list to determine if an online resource is an official resource or not, i.e. basic filtering is performed. Although one of ordinary skill in the art would have found it obvious that this is a form of filtering that is done by the system to filter out irrelevant web resources and can be construed that an initial set of search results is obtained and filtered out to analyze what is left over to determine if there is any illegal or illegitimate use of a trademark, brand name, or the like (¶ 62), the combination of Rozhnov and Hess fails to explicitly disclose whether this list and filter process is used to obtain an initial set of search results and comparing it to a blacklist of web resources that are found to be irrelevant to the search or, to put it another way, the act of filtering.
To be more specific, the combination of Rozhnov and Hess fails to explicitly disclose:
(Claim 3)
the method of claim 1, wherein submitting the search queries to the Internet search engine to obtain a first set of search results comprises:
submitting the search queries to the at least one Internet search engine to obtain an initial set of search results; and
filtering the initial set of search results to eliminate web pages that correspond to irrelevant web pages identified in a blacklist.
(Claim 12)
the system of claim 9, wherein the search results collector obtains the first set of search results pertaining to the plurality of web pages by:
submitting the search queries to the Internet search engine to obtain an initial set of search results; and
filtering the initial set of search results to eliminate web pages that correspond to irrelevant web pages identified in a blacklist, resulting in the first set of search results.
However, Keren, which is also directed towards searching for and protecting against the illegal or illegitimate use of a trademark, brand name, or the like, further teaches that it would have been obvious to filter search results to obtain an initial list of search results based on information provided in a list. The Examiner asserts that the fact that Keren teaches “whitelists” and the claimed invention recites “blacklists” is directed towards simply describing a preferred label to give the list as both the whitelist of Keren and the blacklist of the claimed invention are the same, i.e. both Keren and the claimed invention refer to a list of search results to filter out search results that are found to be irrelevant to the search query. Keren teaches that the list that the system refers to allows for the filtering out of irrelevant search results because these search results are permitted to use the trademark. Keren teaches that this ensures that a focus and useful list is presented to the user as well as allowing the system to priority the risk level of websites.
(For support see: 285, 453, 455)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the trademark protection system and method of the combination of Rozhnov and Hess with the ability to refer to a list, e.g., blacklist/whitelist, of irrelevant websites, as taught by Keren, so that an initial list of search results can be filtered to remove those sites that are irrelevant, thereby ensuring that a focus and useful list is presented to the user as well as allowing the system to priority the risk level of websites.
Response to Arguments
Applicant's arguments filed 11/10/2025 have been fully considered but they are not persuasive.
Rejection under 35 USC 112(a)
The rejection under 35 USC 112(a) for reciting new matter has been maintained, but modified due to amendments.
The Examiner asserts that the amendments still recite the issues that were previously raised in the rejection and that it has been updated to further expand and explain why the amendments are insufficient to overcoming the rejection. The applicant does not provide any arguments to explain why the rejection should be withdrawn other than stating that the amendments overcome the rejection and providing citations that support the amendments.
The Examiner asserts that the examples provided by the specification are not directed towards computing techniques that reduce the number of computing resources needed, but techniques that reduces the physical/mental burden of a user because the system has been programmed with a plurality of templates that can be utilized in order to leverage the available and increased computing resources. The Examiner asserts that reducing the physical/mental load of a human searching for a particular topic is not the same as actually reducing the amount of computing power that a computing system requires to run a particular search. The Examiner asserts that the searching process itself, wherein the applicant explicitly admits to using existing searching technology, e.g. Google, remains the same and is not being improved upon and that the invention is, in fact, simply attempting to run less searches because they are attempting to write a search query that includes every term under the sun that could be associated with the search query, which, again, would only result in an increase of computing resources to run that particular search query. The Examiner asserts that the same, if not, more computing resources are being utilized to run a single search string due to the complexity of the search string increasing and the number of terms that it must now search for. Could this be a more efficient use of the user’s time? Probably? Are fewer computing resources being utilized to execute this more complex search query versus a user simply performing a search on the term “Nike”? In light of the specification, this is not the case. The specification simply recites a goal or objective without providing sufficient evidence or disclosure of how this goal or objective is achieved and, on the contrary, provides evidence that more computing resources are being used.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
First, with regards to the applicant’s reliance to Enfish, the Examiner has already provided an in-depth analysis in the Final Rejection mailed on 12/20/2024 as to why Enfish is not applicable or not persuasive. Accordingly, the Examiner refers to and incorporates the response herein. Additionally, the Examiner has expanded upon the rejection under 35 USC 101 to address the applicant’s arguments, which are based upon the amendments that have been provided. As a result, Enfish continues to not apply to the instant invention because reciting an idea of a solution, especially when there are issues of how the solution is being attained (see rejection under 35 USC 112(a)), is not a demonstration to an improvement in technology, resolving an issue that arose in technology, or deeply rooted in technology. Moreover, as discussed in the rejection, the claimed invention continues to recite steps that can be performed by a human with the aid of pen and paper. Simply stating a solution without specifically identifying how the technology is being improved upon to obtain that solution is insufficient to overcome the rejection (See MPEP § 2106.05(f)) 1 and 2, where the Examiner asserts that the limitation is directed towards an idea of a solution or outcome that is based on what the applicant believes are the best search terms (¶ 60 of the applicant’s specification, “This can also reduce the amount of computing resources that would otherwise be expended if a poorly crafted search was used, which could result in the identification and analysis of numerous irrelevant web pages.”; ¶ 61, 64 regarding a list of terms that the applicant believe are the best search terms) and that the specification simply states that using a template with terms that the applicant believes, in their mind, are “better” search terms will result in fewer compute resources and since a computer is being used the search for information will be performed faster, more efficient, and etc.
Moreover, BASCOM would not be applicable to the claimed invention because the claimed invention is relying on existing searching technology (e.g., Google) and only applying a basic filter, whereas BASCOM improved searching techniques, identified an issue that arose in searching, and provides a solution that resulted in fewer computing resources being required in order to conduct a search. Specifically, as an example, instead of a central computing system receiving queries from computers 1 – 26, with computer 1 requesting information found in File A, computer 2 requesting information found in File B, and so on and the central computing system carrying out computer 1’s request by searching through all of Files A – Z, computer 2’s request by searching through all of Files A – Z, and etc., thereby requiring a lot of computing resources, BASCOM provided a solution that went beyond basic filtering. Specifically, BASCOM would receive query 1 and provide computer 1 with File A, receive query 2 and provide computer 2 with File B, and so forth so that computer 1 can perform a local search for data that is within File A, computer 2 performs a local search that is within File B, and etc. This resulted in fewer resources and increased time efficiency because the central system did not have to search through all the files over and over again and was able to free up resources by sending data to a respective computer to conduct its own search, thereby allowing the central system to move through queries faster and more efficiently.
The Examiner asserts that the claimed invention does not rise to this level and not only is more directed towards basic filtering techniques and relying on known technology, but actually provides evidence that shows the opposite of what the goals and objectives of the claimed invention is attempting to achieve, as has been discussed above.
As was discussed in the interview held on March 18, 2025, and reiterated in the Interview Summary mailed on March 24, 2025, the Examiner explained that the invention is not directed towards the improvement of technology or addressing an issue that arose in technology. Further still, starting at ¶ 63 of the applicant's specification, the Examiner explained that the claimed invention is relying on well-known and established search engine technology, e.g., Google, to perform the search and that the invention is simply directed towards what the applicant believes, in their opinion, are the best search terms to comprise the query. The Examiner explained that the contents of the query does not affect the underlying technology of how search engines fundamentally function. Further, the Examiner explained that the "template", in light of the specification, is nothing more than a form that is being filled out, in this case, a search query form that has been filled out (written) on what a user believes, in their mind and in their opinion, are the best search terms to use in, for example, a Google search.
Finally, the claimed invention is not improving upon GUI/UI technology, resolving an issue that arose in GUI/UI technology, or deeply rooted in GUI/UI technology, but reciting GUI/UI technology at a high level of generality and applying it to the abstract idea to perform the extra solution activity of displaying information. As a result, the claimed invention does not rise to the same level of CoreWireless.
In summary, the Examiner asserts that the applicant is simply utilizing machine learning because of the benefits that it provides, i.e. faster, more efficient, and the like, rather than improving upon the technology of machine learning. The Examiner asserts that the processes performed by the machine learning models are activities that a human can perform in their mind and/or with the aid of pen and paper. Moreover, as stated above, the invention is reliant on a human providing the terms that they believe, in their mind, are the best search terms that best describes their intellectual property and simply utilizing machine learning to perform activities that a human can, indeed, perform in their mind, i.e. collect and compare information, and, as stated above, simply using machine learning for the benefits discussed above. Finally, as stated above, the invention is also not improving upon searching technology since, again, it is relying on well-established technology to perform the search.
Rejection under 35 USC 102
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Saraee et al. (US Patent 12,482,227 B1); Dixon et al. (WO 2006/119479 A2); Shraim et al. (US PGPub 2018/0012184 A1) – which are directed towards systems that perform online searching services to identify fraudulent activity or determining a website’s reputation associated with intellectual property, e.g., trademarks and brand names
Goel (US PGPub 2002/0103786 A1) – which is directed towards searching for specific content on the Internet
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 12/5/2025
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