DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/18/2025 has been entered.
Status of Claims
Claims 1-11, 13-15, 22-26 are pending; of which claims 13-15 are withdrawn from consideration. Claims 12, 16-21 are cancelled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/18/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6, 8-9, 22-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Laoutaris et al (WO 2019/020812).
Regarding Claim 1:
Laoutaris teaches a method for evaluating consent management related to online content (page 12 line 22-31, once the algorithm detects online behavioral advertising (OBA) toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with Do Not Track (DNT) and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)), the method comprising:
generating a plurality of synthetic-user profiles including a first group and a second group (page 12 line 2-17, an operator gives inputs to the system of the present invention selects the demographic types ("Personas") for which he wishes to test a number of Audited domains (news portals, kids related web-sites, etc.) to verify whether the domains target said personas or not; the operator selects from predefined Personas that follow different standardized taxonomies of the AdTech sector (e.g., IAB taxonomy); such taxonomies are used in the actual definition of advertising campaigns by brands and their ad delivery partners; in addition, the invention allows the operator to define his own Personas by providing a list of URLs that this Persona visits);
establishing, for the first group, a first consent status with respect to a target website provided by a first content provider (page 12 line 22-31, in the present invention, the cookie is preferably set programmatically when the user selects AdChoices opt-out during the setup phase; the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case); therefore, one group of personas will have DNT or AdChoices Opt-Out, and the other group of personas will not; page 13 line 19-26, during the collection or training phase, the Container starts visiting the web-pages in the definition of the Persona; for example, for a Persona corresponding to an underage kid, the Container will be visiting web-sites of popular children's TV shows, i.e. “target website provided by a first content provider”, computer games, video distribution sites, etc.; during each of these visits the Container renders fully each page and executes the entire code in it, including tracking and advertising code; in this way, advertisers and trackers start "seeing" the Container visiting children-related content and therefore start building a corresponding profile using cookies and other tracking mechanisms that are opaque to our method);
establishing, for the second group, a second consent status with respect to the target website (page 12 line 22-31, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case); therefore, one group of personas will have DNT or AdChoices Opt-Out, and the other group of personas will not);
exposing the synthetic-user profiles to third-party websites such that dynamic content of the third-party websites are received by the first and second groups of the synthetic-user profiles (page 13 line 27-page 14 line 10, after a number of visits, governed by the input parameters, the Container starts visiting also the Audited domains (in tandem or interchangeably); during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; the details of this complex operation are defined in "Extraction of advertising landing pages without clicking on links"; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next);
retrieving data corresponding to dynamic content of the third-party websites (page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next); and
based on the retrieved data, evaluating consent management with respect to the target website (page 12 line 22-31, once the algorithm detects online behavioral advertising (OBA) toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with Do Not Track (DNT) and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)), wherein the evaluating comprises,
identifying, from among the third-party websites, websites having dynamic content associated with the target website (page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next), and
for at least one of the identified websites having dynamic content associated with the target website, determining that the dynamic content of the at least one identified website corresponds to an advertiser (page 15 line 24-28, Detection of the involved AdTech companies: For each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery; this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident) other than the first content provider (page 14 line 13-page 15 line 28, detection of OBA is achieved by means of evaluating various metrics such as Domain Match, Topic Match, and Frequency counts; Topic Match: if a Container pretends to be a child and visits children-related sites (i.e. “identified websites”) then under various types of behavioral targeting the Container may collect children-related advertisements from domains that do not belong to any of the domains visited during training; Topic Match is calculated by listing all the Topics obtained during the visits to different pages during training phase and then looking for recurrence of the same Topics in the landing URLs of collected advertisements (i.e. “associated with the at least one category of goods or services associated with the target website”); Detection of the involved AdTech companies: for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery (i.e. “any content provider, other than the first content provider”); this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident).
Regarding Claim 2:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein the first consent status comprises an opt-in status with respect to one or more trackers (page 12 line 22-31, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt- out initiatives or not (see the Self-Regulation use case); therefore, one group of personas will have DNT or AdChoices Opt-Out, and the other group of personas will not), and the second consent status comprises an opt-out status with respect to the one or more trackers (page 12 line 22-31, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt- out initiatives or not (see the Self-Regulation use case); therefore, one group of personas will have DNT or AdChoices Opt-Out, and the other group of personas will not).
Regarding Claim 4:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein (i) the dynamic content of the third-party websites received by the first group comprises first dynamic content (page 12 line 22-31, the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service, i.e. “dynamic content”), (ii) the dynamic content of the third-party websites received by the second group comprises second dynamic content (page 12 line 22-31, the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service, i.e. “dynamic content”; as this is done for each Persona, each separate collection of advertisements can be considered separate dynamic content), and evaluating consent management further comprises comparing the first dynamic content and the second dynamic content (page 12 line 22-31, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not).
Regarding Claim 5:
Laoutaris teaches the method of claim 4. In addition, Laoutaris teaches wherein a higher rate of retargeted advertisements in the first dynamic content indicates tracking by the target website in violation of user consent (page 14 line 16-19, Domain Match: For each persona this metric indicates the number of times that the domain of a web-page visited during a training phase is re-encountered in the URL of an advertisement collected at an Audited page; Domain Match captures "Retargeted" advertisements as well as other types of behavioral targeting; page 15 line 20-23, Automating the detection process: If more than X Domain Matches and more than Y Topic Matches and Frequency difference between Persona and Clean more than Z% then flag an Audited domain as suspicious for targeting on the Topic upon which the 3 metrics have been evaluated).
Regarding Claim 6:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein the synthetic-user profiles have profile characteristics (page 12 line 2-17, an operator gives inputs to the system of the present invention selects the demographic types ("Personas") for which he wishes to test a number of Audited domains (news portals, kids related web-sites, etc.) to verify whether the domains target said personas or not; the operator selects from predefined Personas that follow different standardized taxonomies of the AdTech sector (e.g., IAB taxonomy)), and wherein the first and second groups have similar or identical profile characteristics except for the consent statuses with respect to the target website (page 12 line 22-31, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt- out initiatives or not (see the Self-Regulation use case); therefore, one group of personas will have DNT or AdChoices Opt-Out, and the other group of personas will not).
Regarding Claim 8:
Laoutaris teaches a tangible, non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system cause the computing system to perform a method comprising the method of claim 1 (as per rejection of claim 1, above; see also abstract, the present invention also relates to a system and a computer program product adapted to implement the steps of the method of the invention).
Regarding Claim 9:
Laoutaris teaches a computing system for evaluating consent management related to online content, the computing system comprising (page 12 line 22-31, once the algorithm detects online behavioral advertising (OBA) toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with Do Not Track (DNT) and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)):
one or more processors (page 5 line 21-26, system comprises a server having a computer processing unit);
one or more memories (page 3 line 16-19, profiles stored in memory);
a component configured to generate a synthetic-user profile (page 12 line 2-17, an operator gives inputs to the system of the present invention selects the demographic types ("Personas") for which he wishes to test a number of Audited domains (news portals, kids related web-sites, etc.) to verify whether the domains target said personas or not; the operator selects from predefined Personas that follow different standardized taxonomies of the AdTech sector (e.g., IAB taxonomy); such taxonomies are used in the actual definition of advertising campaigns by brands and their ad delivery partners; in addition, the invention allows the operator to define his own Personas by providing a list of URLs that this Persona visits) including a consent property indicating an opt-out status with respect to tracking user behavior for one or more target websites (page 12 line 22-31, in the present invention, the cookie is preferably set programmatically when the user selects AdChoices opt-out during the setup phase; the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem (i.e. first group in parallel with second group), a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 19-26, during the collection or training phase, the Container starts visiting the web- pages in the definition of the Persona; for example, for a Persona corresponding to an underage kid, the Container will be visiting web-sites of popular children's TV shows, computer games, video distribution sites, etc.);
a component configured to expose the synthetic-user profile to one or more third-party websites such that dynamic content of the third-party websites is received by the synthetic-user profile, (page 13 line 27-page 14 line 10, after a number of visits, governed by the input parameters, the Container starts visiting also the Audited domains (in tandem or interchangeably); during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; the details of this complex operation are defined in "Extraction of advertising landing pages without clicking on links"; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next);
a component configured to retrieve data corresponding to dynamic content of one or more third-party websites, the dynamic content including one or more online advertisements (page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next); and
a component configured to, based on the retrieved data, evaluate consent management related to one or more target websites (page 12 line 22-31, once the algorithm detects online behavioral advertising (OBA) toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with Do Not Track (DNT) and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)), wherein the evaluating comprises, for each of the one or more target websites,
identifying, from among the one or more third-party websites, websites having dynamic content associated with the target website (page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service; once an advertisement has been detected and its URL extracted, Topics are assigned; these Topics are the means by which we compare different similarity metrics between the collected advertisements and the web-sites visited initially by the Container; the amount of the said overlap is a prime indicator of OBA as described next), and
for each of the identified websites having dynamic content associated with the target website, determining whether the dynamic content of the identified website corresponds to an advertiser (page 15 line 24-28, Detection of the involved AdTech companies: For each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery; this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident) other than the content provider of the target website (page 14 line 13-page 15 line 28, detection of OBA is achieved by means of evaluating various metrics such as Domain Match, Topic Match, and Frequency counts; Topic Match: if a Container pretends to be a child and visits children-related sites (i.e. “identified websites”) then under various types of behavioral targeting the Container may collect children-related advertisements from domains that do not belong to any of the domains visited during training; Topic Match is calculated by listing all the Topics obtained during the visits to different pages during training phase and then looking for recurrence of the same Topics in the landing URLs of collected advertisements (i.e. “associated with the at least one category of goods or services associated with the target website”); Detection of the involved AdTech companies: for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery (i.e. “any content provider, other than the first content provider”); this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident),
wherein each component comprises computer-executable instructions stored in the one or more memories for execution by the computing system (abstract, the present invention also relates to a system and a computer program product adapted to implement the steps of the method of the invention).
Regarding Claim 22:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches the concept wherein the first group comprises synthetic profiles that have been opted out of a first privacy regulation (page 9 line 19-page 10 line 12, one key challenge related to such self-regulatory programs is convincing the regulators, the consumers and their representatives that the undersigning companies indeed implement the strict restrictions prescribed by these programs; examples of such programs: Opt-out function defined by self-regulatory bodies, e.g. DAA, of the Network Advertising Initiative (NAI) and the Internet Advertising Bureau (IAB); page 12 line 22-page 13 line 8, in the present invention, the cookie is preferably set programmatically when the user selects AdChoices opt-out during the setup phase; the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona (i.e. “synthetic profiles that have been opted out of a first privacy regulation”), it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set (i.e. “synthetic profiles that have been opted into the first privacy regulation”), collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)), wherein the second group comprises synthetic profiles that have been opted into the first privacy regulation (page 12 line 22-page 13 line 8, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set (i.e. “synthetic profiles that have been opted into the first privacy regulation”), collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not (see the Self-Regulation use case)), and wherein the dynamic content of each third-party website includes an advertisement for at least one good or service available from the target website (page 14 line 21-page 15 line 2, Topics in the landing URLs of collected advertisements).
Regarding Claim 23:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein a browsing history of at least one synthetic-user profile of the first group includes a single target webpage associated with the target website (page 12 line 2-17, an operator gives inputs to the system of the present invention selects the demographic types ("Personas") for which he wishes to test a number of Audited domains (news portals, kids related web-sites, etc.) to verify whether the domains target said personas or not; the operator selects from predefined Personas that follow different standardized taxonomies of the AdTech sector (e.g., IAB taxonomy); such taxonomies are used in the actual definition of advertising campaigns by brands and their ad delivery partners; in addition, the invention allows the operator to define his own Personas by providing a list of URLs that this Persona visits; a third option that the system offers for defining a persona is to import it from a real world user browser; in this case the system imports the recent history and the cookies found in a real world browser; then visits the web-sites found in the recent history during the subsequent Collection (also referred to as Training) phase; page 13 line 19-26, during the collection or training phase, the Container starts visiting the web-pages in the definition of the Persona; for example, for a Persona corresponding to an underage kid, the Container will be visiting web-sites of popular children's TV shows, computer games, video distribution sites, etc.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3, 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laoutaris, and further in view of Wilson (PGPUB 2013/0212638).
Regarding Claim 3:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein the second consent status comprises a cookie indicating an opt-out status with respect to user tracking (page 12 line 22-31, AdChoices is implemented through an appropriate cookie which, when detected, indicates the explicit wish of the user to be excluded from data collection and targeting).
Laoutaris does not explicitly teach wherein the first consent status comprises a cookie indicating an opt-in status with respect to user tracking.
However, Wilson teaches the concept wherein a first consent status comprises a cookie indicating an opt-in status with respect to user tracking (paragraph 34, cookies stored on non-opted-out users' workstations 110 may allow the advertising system 140 to track online usage and user preferences to transmit targeted advertising).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the opt-in status cookie teachings of Wilson with the evaluating consent management teachings of Laoutaris, with the benefit of improving testing and training by asserting opt-in tracking status, thereby establishing a more well-defined baseline by directing sites to opt-in to tracking, incorporating sites which would not otherwise enable tracking, and providing a better result for comparison to the opt-out group.
Regarding Claim 10:
Laoutaris teaches the computing system of claim 9.
Laoutaris does not explicitly teach the system, further comprising:
a component configured to identify at least one tracker associated with the one or more target website.
However, Wilson teaches the concept of a system, comprising:
a component configured to identify at least one tracker associated with one or more target website (paragraph 127, agent 852 may detect zombie cookies (e.g., PII and non-PII tracking cookies that respawn) and/or cross-device tracking techniques by maintaining historical record of tracking identifiers).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the tracker detection teachings of Wilson with the evaluating consent management teachings of Laoutaris, in order to immediately detect non-compliance with do-not-track preferences by identifying potentially active trackers and being able to distinguish between new trackers and preexisting ones.
Regarding Claim 11:
Laoutaris in view of Wilson teaches the computing system of claim 10. In addition, Wilson teaches the system, further comprising:
a component configured to identify a tracking-consent status for the agent as reported by the at least one tracker (paragraph 34, 37, based on the opt-out options selected by a user, opt-out system 130 may then send instructions to advertising system 140 to cause advertising system 140 to create and send an opt-out cookie to user workstation 110; in response, advertising system 140 may create and send an opt-out cookie to user workstation 110); and
Laoutaris teaches wherein the agent is the synthetic-user profile (page 12 line 2-17, an operator gives inputs to the system of the present invention selects the demographic types ("Personas") for which he wishes to test a number of Audited domains (news portals, kids related web-sites, etc.) to verify whether the domains target said personas or not; the operator selects from predefined Personas that follow different standardized taxonomies of the AdTech sector (e.g., IAB taxonomy); such taxonomies are used in the actual definition of advertising campaigns by brands and their ad delivery partners; in addition, the invention allows the operator to define his own Personas by providing a list of URLs that this Persona visits).
The rationale to combine Laoutaris and Wilson is the same as provided for claim 10 due to the overlapping subject matter between claims 10 and 11.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laoutaris, and further in view of Kosai et al (PGPUB 2017/0243238).
Regarding Claim 7:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein:
the dynamic content of the third-party websites received by the first group comprises first dynamic content (page 12 line 22-31, the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service, i.e. “dynamic content”),
the dynamic content of the third-party websites received by the second group comprises second dynamic content (page 12 line 22-31, the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 27-page 14 line 10, during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service, i.e. “dynamic content”; as this is done for each Persona, each separate collection of advertisements can be considered separate dynamic content),
the first dynamic content and the second dynamic content of the third-party websites are based on individual synthetic-user profiles (page 12 line 22-31, the present invention makes use of the features mentioned above to offer the operator the possibility to perform more complex experiments and, thus, to be able to compare results from the same "Personas" but using different countermeasures facing the OBA; for example, once the algorithm detects OBA toward a certain persona, it can launch, either in parallel or in tandem, a replica of the experiment with DNT and AdChoices Opt-Out set, collect the results to be compared against the original experiment and thus reveal whether the involved companies truly implement these opt-out initiatives or not; page 13 line 19-page 14 line 10, the Container starts visiting the web- pages in the definition of the Persona; during each visit to an Audited Page, the Container identifies all advertisements included in the page as well as the URL of the advertised product or service, i.e. “dynamic content).
Laoutaris does not explicitly teach the third-party websites include internal content hosted by a first web server, and
the first dynamic content and the second dynamic content of the third-party websites are hosted by a second web server different than the first web server.
However, Kosai teaches the concept wherein third-party websites include internal content hosted by a first web server (paragraph 29, the webpage may include requests for both internal content and external content; accordingly, the web server 305 may retrieve 363 certain content for the webpage from a content store 305; in this example, the content is under the control of the publisher 320), and
first dynamic content and second dynamic content of the third-party websites are hosted by a second web server different than the first web server (paragraph 29-30, the web server 305 may retrieve 363 certain content for the webpage from a content store 305; the affiliate 307 in turn retrieves 367 the ad from an ad store 309).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the first and second web server teachings of Kosai with the evaluating consent management teachings of Laoutaris. It is well-known in the art that multiple web-hosting schemes are available; this can allow individuals to host personal content as well as enable fetching of advertisements from third-party ad servers. It would therefore be obvious to incorporate such schemes in order to isolate first-party content from third-party advertisements, and improve the accuracy of do-not-track compliance determination and detection of unauthorized online behavioral advertisements.
Claim(s) 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Laoutaris, and further in view of Hall et al (PGPUB 2022/0141297).
Regarding Claim 24:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein determining that dynamic content of a first identified website corresponds to an advertiser, other than the first content provider, comprises determining, for each of a plurality of [dynamic content providers], whether dynamic content of the first identified website corresponds to the [dynamic content provider] (page 14 line 13-page 15 line 28, Detection of the involved AdTech companies: for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery (i.e. “dynamic content provider”); this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident).
Laoutaris does not explicitly teach wherein [dynamic content providers] are direct competitors of the first content provider.
However, Hall teaches the concept wherein [dynamic content providers] are direct competitors of a first content provider ([0032] if the provider of the sponsored content knows that the first user browsed to a first website associated with a direct competitor of the provider, the provider may invest more heavily on advertising directed to the first user than if the first user browsed to a second website associated with an indirect competitor of the provider; in this regard, the user's online activities (as obtained by, e.g., the analysis server 206a) may include an identification of sponsors (or owners) of websites, an identification of products or services advertised on or associated with the websites, etc.).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the competitor detection teachings of Hall with the evaluating consent management teachings of Laoutaris, in order to improve the system of Laoutaris by incorporating improved provider detection and identification techniques; in the context of Laoutaris, detecting competitors would improve the system by improving the accuracy of domain/topic categorization, as the system would be more certain that competing providers belonged to the same category.
Regarding Claim 25:
Laoutaris teaches the method of claim 1. In addition, Laoutaris teaches wherein determining that dynamic content of a first identified website corresponds to an advertiser, other than the first content provider, comprises determining, for each of a plurality of [dynamic content providers], whether dynamic content of the first identified website corresponds to the [dynamic content providers] (page 14 line 13-page 15 line 28, Detection of the involved AdTech companies: for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery (i.e. “dynamic content provider”); this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident).
Laoutaris does not explicitly teach wherein [dynamic content providers] are indirect competitors of the first content provider.
However, Hall teaches the concept wherein [dynamic content providers] are indirect competitors of a first content provider ([0032] if the provider of the sponsored content knows that the first user browsed to a first website associated with a direct competitor of the provider, the provider may invest more heavily on advertising directed to the first user than if the first user browsed to a second website associated with an indirect competitor of the provider; in this regard, the user's online activities (as obtained by, e.g., the analysis server 206a) may include an identification of sponsors (or owners) of websites, an identification of products or services advertised on or associated with the websites, etc.).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the competitor detection teachings of Hall with the evaluating consent management teachings of Laoutaris, in order to improve the system of Laoutaris by incorporating improved provider detection and identification techniques; in the context of Laoutaris, detecting competitors would improve the system by improving the accuracy of domain/topic categorization, as the system would be more certain that competing providers belonged to the same category.
Regarding Claim 26:
Laoutaris teaches the computing system of claim 9. In addition, Laoutaris teaches wherein determining whether dynamic content of a first identified website corresponds to a content provider other than the content provider of the target website comprises determining for each of a plurality of [dynamic content providers], whether the dynamic content of the first identified website corresponds to the [dynamic content providers] (page 14 line 13-page 15 line 28, Detection of the involved AdTech companies: for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery (i.e. “dynamic content provider”); this is a very important function since it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident).
Laoutaris does not explicitly teach wherein [dynamic content providers] are competitors of the content provider of the target website.
However, Hall teaches the concept wherein [dynamic content providers] are competitors of a content provider of a target website ([0032] if the provider of the sponsored content knows that the first user browsed to a first website associated with a direct competitor of the provider, the provider may invest more heavily on advertising directed to the first user than if the first user browsed to a second website associated with an indirect competitor of the provider; in this regard, the user's online activities (as obtained by, e.g., the analysis server 206a) may include an identification of sponsors (or owners) of websites, an identification of products or services advertised on or associated with the websites, etc.).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the competitor detection teachings of Hall with the evaluating consent management teachings of Laoutaris, in order to improve the system of Laoutaris by incorporating improved provider detection and identification techniques; in the context of Laoutaris, detecting competitors would improve the system by improving the accuracy of domain/topic categorization, as the system would be more certain that competing providers belonged to the same category.
Response to Arguments
Applicant's arguments filed 12/18/2025 have been fully considered but they are not persuasive.
Regarding the rejection of claims under 35 USC 103:
Examiner’s response to applicant’s arguments, page 8 paragraph 2: Examiner disagrees. Examiner notes the breadth of the term “first content provider”; this could refer to a website, individual creator, company, conglomerate, etc. For the website domain, e.g. www.google.com, Google could be viewed as the provider. Laoutaris teaches that “for each collected and analysed ad, the system can also reveal the chain of AdTech companies involved in its delivery”, and that “it permits to know exactly which one of its AdTech partners/contracts is responsible for each incident”. A chain of AdTech companies (plural) described as partners or contracts can be seen as at least one separate advertising provider from the first content provider, e.g. the website domain. Therefore, Laoutaris does teach determining that the dynamic content of the at least one identified website corresponds to an advertiser other than the first content provider.
Applicant further argues that the dependent claims are allowable due to depending on an allowable independent claim. However, as shown above, the independent claims are not allowable.
Regarding the withdrawn claims:
As the generic claims are not allowable (as per the above), the withdrawn claims will not be rejoined at this time.
Conclusion
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/FORREST L CAREY/Examiner, Art Unit 2491
/AMIR MEHRMANESH/Supervisory Patent Examiner, Art Unit 2491