DETAILED ACTION
This in in reference to communication received 16 February 2026. Cancellation of claims 2, 5, 13 and 16 and addition of claim 21 is acknowledged. Claims 1, 3 – 4, 6 – 12, 14 – 16 and 17 – 21 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3 – 4, 6 – 12, 14 – 16 and 17 – 21 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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Applicant’s claimed invention added limitations generating, by the computer, based on the detected event, a cease-and-desist notice by applying a second ML model on first registration information associated with a registration of the first trademark term by the first entity, and second registration information associated with the registration of the one or more domain names by the second entity; and transmitting, by the computer, the cease-and-desist notice to one or more electronic devices associated with at least one of the second entity or the plurality of domain name registrars for blocking the registration of the one or more domain names by the second entity. However, applicant’s disclosure, after identifying cybersquatting event associated with a trademark of an entity, notifies the entity that there is a possible event associated with their trademark, and request the entity to identify whether they want to ignore the cybersquatting event, or, they would want to initiate a legal action, subsequent to which legal action is initiated on behalf of the entity.
In the amended claimed invention, applicant’s system and method automatically initiates the legal action on behalf of the entity without informing the entity [See at least, applicant’s disclosure Fig. 7A-7D and associated disclosure].
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.
Independent claim 1, representative of claims 11 and 20, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to
Predicting a first confidence score indicative of at least one cybersquatting event associated with the first trademark of a first entity and render a first alert based on the predicted first confidence score. Data from plurality domain name registrars are monitored to detect an event associated with the registration of the one or more domain names by the second entity; (disregarding whether the domain name registered by the second entity is cybersquatting), a cease-and-desist notice is generated and transmitted to second entity or the plurality of domain name registrars for blocking the registration of the one or more domain names by the second entity.
These limitations describe marketing/sales/advertising activities. Monitoring the world-wide-web (aka web) to determine whether there are potential cybersquatting activities on the web, identify who is associated with the cybersquatting activities (e.g., by accessing a public query and response protocol “WHOIS”), and send them a legal-notice like cease-or-desist to inform them that they should stop their cybersquatting activities. Causing sending of a legal-notice would be the marketing team (or person) providing notice to the party associated with the cybersquatting activities.
Additionally, The independent claims further recite the additional functional element of using a trained machine-learning mode to implement an abstract idea of making a prediction of a confidence-score for a cybersquatting event. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 11 and 20, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 11), and a (transitory) machine-readable medium comprising having executable program instructions embodied therewith to perform the method addressed above (claim 20).
The processor, memory, and (transitory) machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. 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 the processor, memory, and non-transitory machine-readable medium 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. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 3 – 4, 6 – 10, 12, 14 – 16, 17 – 19 and 21, these claims recite limitations that further define the same abstract idea of defining that the trademark term is associated with a first entity, and cybersquatting event corresponds to a different entity registering one or more domain names associated with the first trademark term; alert will be generated if the first-trademark is registered as other domain names by a second-party (e.g., a cyber-squatter); generating and sending of a legal-notice to the second-party by using a ML-model, and using of Natural-Language-Processing to determine one or more name associated with the first-trademark-term (e.g., renaming domain-name “xxx-foods.com” with “xxx.food.biz”, or “xxx.foodstuff.com”) as cybersquatting-event and predict confidence-score indicative of the cybersquatting-event, and using of smart-contract for blocking the registration, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3 – 4, 6 – 12, 14 – 6 and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Keren et al. US Publication 2021/0248624 in view of Lantz et al. US Publication 2019/0130508.
Regarding claim 1 and representative claim 11 and 20, Karen teaches system and method for protecting brand-name or a brand-owner comprising:
a processor set [Karen, 0436]:
computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a system to cause the system to (Keren, a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system) [Keren, 0440]:
receiving, by a computer, a first input associated with a first trademark term, wherein the first trademark term is associated with a first entity (Keren, identifying which domains are owned by the company, and which are taken by other parties; even though the user provided the list of owned domain names, the system may verify again which domain names are owned by the company and discover mistakes in the initial data entry;) [Keren, 0319];
determining, by the computer, a first set of features associated with the first trademark term based on the received first input (Keren, utilize a Machine Learning (ML) and Artificial Intelligence (AI) engine, or a Learned Features generator unit and enforcement unit, in order to generate additional or new features or parameters that can be taken into account for the purpose: generating the Relevancy score and/or generating the Popularity score, and/or generating the Investment score, and/or generating the Damage score, and/or generating the combined (weighted) RPID score.) [Keren, 0454];
predicting, by the computer, a first confidence score indicative of at least one cybersquatting event associated with the first trademark term by applying a first machine learning (ML) model on the determined first set of features (Keren, A Risk and Opportunities Analysis (ROA) module 221 may perform Risks and Opportunities Analysis. The service gets the input of the brand name, relevant brand key words, the website of the brand owner, etc. The service activates the algorithms over the data inserted to it and based on the information it collects from the different system information services. The service may calculate the RPID score(s) and/or the individual scores that together make the RPID score, optionally utilizing an RPID score generator 247) [Keren, 0130], wherein
the at least one cybersquatting event corresponds to a registration of one or more domain names associated with the first trademark term by a second entity different from the first entity (Keren, the system may utilize a module and/or algorithm in order to detect, identify and/or determine cross-brand infringement. In a demonstrative example, the system may collect and analyze data, domain registration data, Internet traffic data, website content, and/or other data, and may detect that: (a) a first website, such as "Samsung-Phonez.co.uk" is abusing a first brand that belongs to a first brand owner; and also, (b) a second website, such as "Nokia-Phonez.co.uk", is abusing a second brand that belongs to a second brand owner) [Keren, 0212];
Keren does not explicitly teach training dataset. However, Keren teaches Negotiation Recommendation Algorithm of the system uses statistical and historical data to analyze the probability and price ranges of a domain name being bought in negotiations.) [Keren, 0175]. However, Lantz teaches online system and method to search for content items provided to users via the online system, where the content items include trademark violations.) [Lantz, 0004]. Lantz teaches The training data store 206 stores content items that are used by the machine learning module for training a prediction model [Lantz, 0039], and, the machine learning module 209 determines weights of various features used by the prediction model based on training data stored in the training data store 206 [Lantz. 0037].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Keren by adopting teachings of Lantz to verify whether a content provider legally has the rights in the trademarks that it was using.
Keren in view of Lantz teaches system and method further comprising:
the first ML model is trained on a training dataset comprising historical trademark term data associated with a set of historical trademark terms and historical event data associated with one or more cybersquatting events associated with each historical trademark term of the set of historical trademark terms (as responded to above) [Lantz, 0037, 0039];
rendering, by the computer, a first alert based on the predicted first confidence score (Keren, The automatic or "one-click" actions according to the analysis may include: Automatic alerts of high-risk violations; Automatic "Cease and Desist" notifications to the domain registrants and to other involved parties; Automatic requests to ISPs and hosting companies to disable violating sites;) [Keren, 0268];
continuously monitoring, by the computer (Keren, The Taken domain names info (they are constantly or continuously or periodically monitored for change of use) may include: current usage characteristics) [Karen, 0278], a third set of databases associated with a plurality of domain name registrars to detect an event associated with the registration of the one or more domain names by the second entity (Keran, The statistics are based both on historical data of the system, and on external data about secondary market of domain names received from outsource data providers such as domain name marketplace websites (for example afternic.com, sedo.com, etc.) if available. The system may support Domain registrar transfer away from the current registrar, to release such domain to a different ( external) registrar.) [Keren, 0175, 0368];
generating, by the computer, based on the detected event, a cease-and-desist notice by applying a second ML model on first registration information associated with a registration of the first trademark term by the first entity, and second registration information associated with the registration of the one or more domain names by the second entity (Keren, an automated cease-and-desist engine 250 may handle ceased-and-desist notifications and follow ups. Based on previously found Risk Websites, i.e. websites, webpages or domain names that potentially infringe or abuse a brand, the user which represents the brand is able to react to these infringements, by automatically or semi-automatically sending Cease and Desist notifications to the registrant of each such Risk Website or other parties and/or contacts listed as connected to that website (such as the hosting provider, domain registrar, etc.).) [Keren, 0161, 0201]; and
transmitting, by the computer, the cease-and-desist notice to one or more electronic devices associated with at least one of the second entity or the plurality of domain name registrars for blocking the registration of the one or more domain names by the second entity (Keren, an automated cease-and-desist engine 250 may handle ceased-and-desist notifications and follow ups. Based on previously found Risk Websites, i.e. websites, webpages or domain names that potentially infringe or abuse a brand, the user which represents the brand is able to react to these infringements, by automatically or semi-automatically sending Cease and Desist notifications to the registrant of each such Risk Website or other parties and/or contacts listed as connected to that website (such as the hosting provider, domain registrar, etc.) [Keren, 0161, 0201].
Regarding claim 3 and representative claim 14, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising:
retrieving, by the computer, the first registration information from a first set of databases (Keren, identifying which domains are owned by the company, and which are taken by other parties; even though the user provided the list of owned domain names, the system may verify again which domain names are owned by the company and discover mistakes in the initial data entry) [Keren, 0319];
retrieving, by the computer, the second registration information from the third set of databases (Keren, identifying the common pattern among the multiple web-sites is performed based on at least one of: identifying common domain ownership for said multiple web-sites; identifying common domain registrar for said multiple web-sites;) [Keren, 0015]; and
rendering, by the computer, a second alert based on a comparison of the retrieved first registration information with the retrieved second registration information, wherein the second alert is indicative of the registration of the one or more domain names by the second entity (Keren, The Taken domain names info (they are constantly or continuously or periodically monitored for change of use) may include: current usage characteristics. an automated cease-and-desist engine 250 may handle ceased-and-desist notifications and follow ups. Based on previously found Risk Websites, i.e. websites, webpages or domain names that potentially infringe or abuse a brand, the user which represents the brand is able to react to these infringements, by automatically or semi-automatically sending Cease and Desist notifications to the registrant of each such Risk Website or other parties and/or contacts listed as connected to that website (such as the hosting provider, domain registrar, etc.) [Keren, 0161, 0201, 0278].
Regarding claim 4 and representative claim 15, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising:
receiving, by the computer, a second input based on the rendered second alert (Keren, The Taken domain names info (they are constantly or continuously or periodically monitored for change of use) may include: current usage characteristics. an automated cease-and-desist engine 250 may handle ceased-and-desist notifications and follow ups.) [Keren, 0278]; and
transmitting, by the computer, the cease-and-desist notice to the one or more electronic devices based on the received second input to one or more electronic devices (Keren, Based on previously found Risk Websites, i.e. websites, webpages or domain names that potentially infringe or abuse a brand, the user which represents the brand is able to react to these infringements, by automatically or semi-automatically sending Cease and Desist notifications to the registrant of each such Risk Website or other parties and/or contacts listed as connected to that website (such as the hosting provider, domain registrar, etc.) [Keren, 0161, 0201].
Regarding claim 6, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising:
generating, by the computer, a second set of databases with a set of cybersquatting events based on at least one of the first trademark term, the first set of features, the first registration information, the second registration information, or the first confidence score (Keren, The score section includes a risk score calculated based on each of the RPID algorithms, and the particular scores of each of the RPID components. If the website or webpage was recognized as being part of a group of websites or webpages with similar characteristics (Risk Patterns), then an icon indicating that it is a part of that group may be added in the line of that risk website or webpage.) [Keren, 0233], and
predicting, by the computer, a second confidence score associated with at least one cybersquatting event associated with a second trademark term based on the generated second set of databases (Keren, A Risk and Opportunities Analysis (ROA) module 221 may perform Risks and Opportunities Analysis. The service gets the input of the brand name, relevant brand key words, the website of the brand owner, etc. The service activates the algorithms over the data inserted to it and based on the information it collects from the different system information services. The service may calculate the RPID score(s) and/or the individual scores that together make the RPID score, optionally utilizing an RPID score generator 247.) [Keren, 0130].
Regarding claim 7 and representative claim 18, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method, wherein the first set of features is selected from the group consisting of a length of the first trademark term, or classification information associated with the first trademark term (Keren, A "whois" module 201 collects all the relevant information about a domain name. For example, on whose name it is registered, registration date, expiration date, DNS servers, etc. This component connects to numerous servers that provide this information in order to provide the information in real-time.). The information that is being assessed, analyzed and compared by the Negotiation Recommendation Algorithm include but is not limited to the following data: the domain string characteristics (length, generic level of the string, use of popular keywords in the string, etc.) [Keren, 0119, 0176],
Regarding claim 8, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising:
determining, by the computer, the one or more domain names associated with the first trademark term by applying natural language processing on the first trademark term (Keren, the NLP engine may be automatically fed with textual inputs from the website ( or other online venue) that is being inspected, from the "body" of the HTML page, from tags or meta-data in the HTML code, …. The text is analyzed by the NLP engine, to determine the level of relevancy of the text to the protected brand and/or to the brand-owner (whose brand is being protected by the system.) [Keren, 0456].
Regarding claim 9 and representative claim 12, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising: generating, by the computer, a training dataset comprising historical trademark term data associated with a set of historical trademark terms and historical event data associated with one or more cybersquatting events associated with each historical trademark term of the set of historical trademark terms (Keren, Negotiation Recommendation Algorithm of the system uses statistical and historical data to analyze the probability and price ranges of a domain name being bought in negotiations. …. The statistics are based both on historical data of the system, and on external data about secondary market of domain names received from outsource data providers such as domain name marketplace websites (for example afternic.com, sedo.com, etc.) [Keren, 0175]; and
training, by the computer, the first ML model based on the generated training dataset (Lantz, The training data store 206 stores content items that are used by the machine learning module for training a prediction model [Lantz, 0039], and, the machine learning module 209 determines weights of various features used by the prediction model based on training data stored in the training data store 206 [Lantz. 0037].
Regarding claim 10 and representative claim 19, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method further comprising:
calculating, by the computer, a first interval associated with the registration of the one or more domain names based on a first timestamp associated with the registration of the first trademark term and a second timestamp associated with the at least one cybersquatting event; and training, by the computer, the first ML model based on the calculated first interval, wherein a second confidence score associated with at least one cybersquatting event associated with a second trademark term is predicted based on the calculated first interval (Keren, The Taken domain names info (they are constantly or continuously or periodically monitored for change of use) may include: current usage characteristics) [Karen, 0278].
Regarding claim 17, as combined and under the same rationale as above, Keren in view of Lantz teaches system and method, wherein the processor set is further configured to:
determine a first set of features associated with the first trademark term based on the received first registration information (Keren, utilize a Machine Learning (ML) and Artificial Intelligence (AI) engine, or a Learned Features generator unit and enforcement unit, in order to generate additional or new features or parameters that can be taken into account for the purpose: generating the Relevancy score and/or generating the Popularity score, and/or generating the Investment score, and/or generating the Damage score, and/or generating the combined (weighted) RPID score.) [Keren, 0454; also see 0288]; and
train the first ML model based on the determined first set of features, the received first registration information, and the retrieved second registration information (Lantz, The training data store 206 stores content items that are used by the machine learning module for training a prediction model [Lantz, 0039], and, the machine learning module 209 determines weights of various features used by the prediction model based on training data stored in the training data store 206 [Lantz. 0037].
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Keren et al. US Publication 2021/0248624 in view of Lantz et al. US Publication 2019/0130508 and CFI-Team published article “Smart Contracts” hereinafter referred to as CFI-Team.
Regarding claim 21, Keren in view of Lantz does not teach executing a smart contract for perform task of blocking the registration. However, Lantz teaches the content distribution module 122 blocks content items that include fraudulent use of trademarks and withholds such content from users [Lantz, 0029]. However, CFI-Team teaches Smart contracts refer to computer protocols that digitally facilitate the verification, control, or execution of an agreement. CFI-Team further teaches. Smart contracts automate tasks by using computer protocols, saving hours of various business processes. Using smart contracts results in the elimination of errors that occur due to manual filling of numerous forms.
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Keren in view of Lantz by adopting teachings of CFI-Team to minimize error by eliminating task performed by intermediaries [CFI-Team, page 3, 5].
as combined and under the same rationale as above, Keren in view of Lantz and CFI-Team teaches system and method further comprising:
executing a smart contract for blocking the registration of the one or more domain names by the second entity (as responded to above) [CFI-Team, page 3, 5].
Response to Arguments
Applicant's argument that pending claimed amended invention are acknowledged and considered. However, applicant is arguing added limitations which have been responded to in this office action above. Therefore, applicant’s arguments are moot under new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
March 20, 2026