DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 8 and 14, 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 Identifying a set of agent actions (e.g., clicks by agents) in a server log when the agents interact an advertisement using combination of identifiers associated with the agent and two or more parts of a domain name associated with the agent identifier (e.g., combination of domain name and top-level domain (e.g., .com. .co, .edu, etc.)); agent clicks are processed to generate a value indicating quality of clicks. Elapse time between consecutive click of an agent str measured to determine velocity (e.g. speed) between consecutive clicks, and generate an output identifying one or more clicks corresponding to low quality clicks with the advertisement, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. advertising accounting). Therefore, the claims recite a judicial exception.
Represented claims 8 and 14, 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 8), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 14).
The processor, memory, and non-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 2 – 7, 9 – 13 and 15 - 20, these claims recite limitations that further define the same abstract idea of
Defining that associated advertisers will not be for the low quality clicks; defining labels that will be assigned to the assessed values of the quality of clicks, defining conversion will be considered as an action by the agent, defining that low quality clicks are clicks generated by one or more of a bot, a spyware, an adware virus, a clickware virus, or an improperly operating software program, or the agent associated with the click is identified in a blacklist of network addresses., 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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 6 and 8 – 19 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Immorlica et al. US Publication 2007/0073579 in view of Interactive Advertising Bureau published article “Interactive Audience Measurement and Advertising Campaign Reporting and Audit Guidelines” hereinafter referred to as IAB and Blue Martini Software published article “Blue Martini Business Intelligence At Work: Charting the Terrains of MEC Website Data” hereinafter referred to as BMS.
Regarding claim 1 and represented claims 8 and 14, Immorlica teaches system and method for learning advertisement click through rates (CTRs) in a fraud resistant manner. Click-based algorithms are leveraged to provide protection against fraudulent user clicks of online advertisements [Immorlica, 0011] comprising:
one or more processors [Immorlica, 0079]; and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more processors [Immorlica, 0082], cause the one or more processors to perform one or more operations including:
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log (Immorlica, obtaining event data relating to an online advertisement 504. The event data can include, but is not limited to, historical data associated with an online advertisement such as, for example, impression data, clicked impression data, and/or acquisition data related to an impression and the like.) [Immorlica, 0073], wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website (Immorlica, system and method are provided for learning advertisement click through rates (CTRs) in a fraud resistant manner. Click-based algorithms are leveraged to provide protection against fraudulent user clicks of online advertisements. This enables mitigation of short term losses due to the fraudulent clicks and also mitigates long term advantages caused by the fraud.) [Immorlica, 0011], and wherein each agent action of the set of agent actions is associated with a combined identifier that includes:
two or more parts of a domain name associated with the agent identifier (Immorlica, many restaurants provide an online reservation service wherein customers can make their reservations via the Internet using the restaurants' websites (e.g., . Unfortunately, this system makes restaurant owners (e.g., www.McDonalds.com, wherein McDonalds + .com) are two parts of the domain name)somewhat vulnerable to automated script attacks that make fraudulent reservations.) [Immorlica, 0007];
Immorlica does not explicitly teach set of agent actions to generate set of values that represent indications of quality of clicks on the advertisement displayed on the publisher website (e.g., a concept of determining fraudulent clicks). However, interactive action bureau (IAB) teaches appropriate filtration of robotic activity is critical to accurate measurements of ad impressions [IAB, page 6]. IAB further teaches filtration can be performed using “Specific Identification Approach” and “Activity-based Filtration” [IAB, page 7].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Immorlica by adopting teachings of IAB and filter fraudulent activities (e.g. robotic activities) to accurately measure ad impressions.
Immorlica in view of IAB teaches system and method further comprising:
processing agent actions to identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log (Immorlica, obtaining event data relating to an online advertisement 504. The event data can include, but is not limited to, historical data associated with an online advertisement such as, for example, impression data, clicked impression data, and/or acquisition data related to an impression and the like.) [Immorlica, 0073], wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website (Immorlica, system and method are provided for learning advertisement click through rates (CTRs) in a fraud resistant manner. Click-based algorithms are leveraged to provide protection against fraudulent user clicks of online advertisements. This enables mitigation of short term losses due to the fraudulent clicks and also mitigates long term advantages caused by the fraud.) [Immorlica, 0011], and wherein each agent action of the set of agent actions is associated with a combined identifier that includes:
an agent identifier of an agent that performed the agent action (IAB, filtration can be performed using “Specific Identification Approach” and “Activity-based Filtration” ) [IAB, page 7]; and
two or more parts of a domain name associated with the agent identifier (Immorlica, many restaurants provide an online reservation service wherein customers can make their reservations via the Internet using the restaurants' websites (e.g., . Unfortunately, this system makes restaurant owners (e.g., www.McDonalds.com, wherein McDonalds + .com) are two parts of the domain name)somewhat vulnerable to automated script attacks that make fraudulent reservations.) [Immorlica, 0007];
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website (IAB, appropriate filtration of robotic activity is critical to accurate measurements of ad impressions [IAB, page 6]. IAB further teaches filtration can be performed using “Specific Identification Approach” and “Activity-based Filtration” [IAB, page 7];
Immorlica in view of IAB does not teach determining a velocity of clicks associated with combined identifier. However, BMS teaches The “bot” filtration algorithm included in the Blue Martini Business Intelligence tools ensures that only true human traffic will be analyzed. Bots are automated programs, sometimes called robots and spiders, launched by search engines, performance monitoring services, and other automated programs. In the case of MEC, bots generated 23 percent of the Web site sessions. In fact, over 50 unique bots were identified (each with multiple sessions). Without filtering the bots, key performance indicators will be skewed. For example, the average session duration at MEC was 5:42 minutes (e.g., velocity – 5.2 minutes), but, after filtering out the bot traffic, Blue Martini found that the average human session was 7:12 minutes (e.g., velocity – 7.12 minutes), 26 percent longer! [BMS, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Immorlica in view of IAB by adopting teachings of BMS to minimize data skewing for generating reliable report.
Immorlica in view of IAB and BMS teaches system and method further comprising:
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website [IAB, page 7], and wherein processing the set of agent actions includes:
measuring an elapsed time between at least two consecutive clicks associated with the combined identifier; and determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website (BMS, the average session duration at MEC was 5:42 minutes (e.g., velocity – 5.2 minutes), but, after filtering out the bot traffic, Blue Martini found that the average human session was 7:12 minutes (e.g., velocity – 7.12 minutes), 26 percent longer!) [BMS, page 3]; and
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website (BMS, In late 2002, MEC turned to Blue Martini Analytic Services (BMAS) for a detailed analysis of its Blue Martini-powered Web site using the reporting, visualization, and analytic capabilities of the Blue Martini Business Intelligence suite. This suite provides a set of comprehensive analysis tools and powerful visualization capabilities that help business users answer critical tactical and strategic questions about their site, their products and services, and their company) [BMS, page 5].
Regarding claim 2 and representative claims 9 and 15, as combined and under the same rationale as above, Immorlica in view of IAB and BMS teaches system and method further comprising causing an advertiser responsible for the advertisement to not be billed for the low quality clicks (Immorlica, Most service providers currently approach the problem of click fraud by attempting to automatically recognize fraudulent clicks and discount them.) [IAB, 0037].
Regarding claim 3 and representative claims 10 and 16, as combined and under the same rationale as above, Immorlica in view of IAB and BMS teaches system and method, wherein the set of values represent an indication of high quality clicks, the indication of low quality clicks, and an indication of invalid clicks (IAB, appropriate filtration of robotic activity is critical to accurate measurements of ad impressions [IAB, page 6]. IAB further teaches filtration can be performed using “Specific Identification Approach” and “Activity-based Filtration” [IAB, page 7].
Regarding claim 4 and representative claims 11 and 17, as combined and under the same rationale as above, Immorlica in view of IAB and BMS teaches system and method, wherein the set of values for the clicks are used to assess a value of the advertisement (Immorlica, Most service providers currently approach the problem of click fraud by attempting to automatically recognize fraudulent clicks and discount them.) [IAB, 0037].
Regarding claim 5 and representative claims 12 and 18, as combined and under the same rationale as above, Immorlica in view of IAB and BMS teaches system and method, wherein at least one of the agent actions results in a desired agent action, wherein the desired agent action comprises a conversion.
Regarding claim 6 and representative claims 13 and 19, as combined and under the same rationale as above, Immorlica in view of IAB and BMS teaches system and method, wherein the one or more low quality clicks are initiated by one or more of a bot, a spyware, an adware virus, a clickware virus, or an improperly operating software program (BMS, The “bot” filtration algorithm included in the Blue Martini Business Intelligence tools ensures that only true human traffic will be analyzed. Bots are automated programs, sometimes called robots and spiders, launched by search engines, performance monitoring services, and other automated programs. In the case of MEC, bots generated 23 percent of the Web site sessions. In fact, over 50 unique bots were identified (each with multiple sessions). Without filtering the bots, key performance indicators will be skewed. For example, the average session duration at MEC was 5:42 minutes (e.g., velocity – 5.2 minutes), but, after filtering out the bot traffic, Blue Martini found that the average human session was 7:12 minutes (e.g., velocity – 7.12 minutes), 26 percent longer!) [BMS, page 3].
Claims 7 and 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Immorlica et al. US Publication 2007/0073579 in view of Interactive Advertising Bureau published article “Interactive Audience Measurement and Advertising Campaign Reporting and Audit Guidelines” hereinafter referred to as IAB, Blue Martini Software published article “Blue Martini Business Intelligence At Work: Charting the Terrains of MEC Website Data” hereinafter referred to as BMS and Fishteyn et al. US Publication 2004/0190448.
Regarding claim 7 and representative claim 20, Immorlica in view of IAB and BMS does not teach referencing blacklist of network addresses. However, Fishteyn teaches system and method of determining a quality ranking of user traffic directed from at least one traffic producer Web site to a traffic consumer Web site [Fishteyn, 0026]. Fishteyn further teaches the percentages of each user traffic data parameter or subset within a parameter are listed. The Black Listed IP field pertains to IP addresses that are black listed or otherwise tagged as special. This may 222, be from an internal list of traffic quality intermediary an external list or a combination of the two [Fishteyn, 0079].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Immorlica in view of IAB and BMS by adopting teachings of Fishteyn to adequately assess where to advertise, the type of advertising to place and how much to pay for the advertising.
as combined and under the same rationale as above, Immorlica in view of IAB, BMS and Fishteyn teaches system and method, wherein the one or more agent actions corresponding to the low quality clicks are identified further based on a determination that the agent is identifiable from a blacklist of network addresses (Fishteyn, the percentages of each user traffic data parameter or subset within a parameter are listed. The Black Listed IP field pertains to IP addresses that are black listed or otherwise tagged as special. This may 222, be from an internal list of traffic quality intermediary an external list or a combination of the two) [Fishteyn, 0079].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 – 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 20 of U.S. Patent No. 11,792,101; claims 1 – 20 of U.S. Patent No. 11,818,026; claims 1 – 20 of U.S. Patent No. 12,284,103; claims 1 – 28 of U.S. Patent No. 11,627,064; and claims 1 – 25 of U.S. Patent No. 12,615,200; Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is fully disclosed in the patent and is covered by the patent since the patent and the application are claiming common subject matter, as follows:
Application 19/031,465
US Patent 11,627,064
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
identifying, by a computer system comprising one or more hardware processors, agent actions in a server log obtained from at least one of a publisher, an advertiser, or a third party, wherein the agent actions correspond to actions performed by a plurality of agents interacting with content presented on a network site, wherein the agent actions are associated with a set of network parameters, and wherein each agent action of the agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
accessing, by the computer system, a set of traffic rules from a data store, wherein each traffic rule of the set of traffic rules correlates with a likelihood that at least one network parameter of each of the agent actions indicates a desired agent action that generates value for the at least one of the publisher, the advertiser, or the third party;
applying, by the computer system, the set of traffic rules to the agent actions to generate non-binary scores for the agent actions, wherein applying the set of traffic rules includes, for each combined identifier:
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes: measuring an elapsed time between at least two consecutive clicks associated with the combined identifier;
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website;
measuring a velocity metric between at least two consecutive agent actions associated with the combined identifier; and
determining an elapsed time during which the agent associated with the combined identifier interacted with the content of the network site;
normalizing, by the computer system, the non-binary scores into a standard range, wherein a first portion of the standard range corresponds to agent actions having a first relative value, a second portion of the standard range corresponds to agent actions having a second relative value, and a third portion of the standard range corresponds to agent actions having a third relative value;
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
generating, based on the normalized non-binary scores and by the computer system, an output identifying that a set of agent actions correspond to low quality interactions with the content presented on the network site; and
causing, based on the output and by the computer system, the content to be removed from one or more web pages of the network site that are associated with the set of agent actions.
Application 19/031,465
US Patent 11,792,101
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes: measuring an elapsed time between at least two consecutive clicks associated with the combined identifier;
analyzing session data, wherein the session data is descriptive of at least one agent session, wherein each agent session comprises one or more clicks by an agent when the agent interacts via the Internet with the advertisement displayed on the publisher website, wherein the agent is identified in the session data by at least two bytes of its IP address in combination with an agent identifier, and wherein the at least one agent session spans one or more visits by the agent to the publisher website;
applying a rule set to each of the at least one agent session, the rule set including at least three rules, wherein applying the rule set comprises:
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website;
measuring by the one or more data processors a velocity with which the agent clicks on the advertisement after the advertisement is displayed,
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
determining by the one or more data processors that a first rule is satisfied when the velocity is slower than a predetermined velocity value,
determining by the one or more data processors an elapsed time between clicks during the at least one agent session,
determining by the one or more data processors that a second rule is satisfied when the elapsed time is greater than a predetermined elapsed time value,
determining by the one or more data processors whether the IP address of the agent is included on an IP address blacklist, and
determining by the one or more data processors that a third rule is satisfied when the IP address is not included on the IP address blacklist;
for each of the at least one agent session, assigning a first non-binary value to any click that satisfies at least each of the first rule, the second rule, and the third rule;
for each of the at least one agent session, assigning a second non-binary value to any click that does not satisfy either the first rule or the second rule, the second non-binary value representing a lower quality than the first non-binary value;
for each of the at least one agent session, assigning a third non-binary value to any click that does not satisfy the third rule, the third non-binary value representing a lower quality than the second non-binary value; and
causing an advertiser responsible for the advertisement to not be billed for clicks having the second non-binary value and clicks having the third non-binary value.
Application 19/031,465
US Patent 11,818,026
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log generated by a content provider system in a network environment, wherein the set of agent actions are associated with a content of a network site of the content provider system, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes: measuring an elapsed time between at least two consecutive clicks associated with the combined identifier;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent an indication of quality of agent actions with the content presented on the network site, and wherein processing the set of agent actions includes:
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website;
measuring a velocity metric between at least two consecutive agent actions associated with the combined identifier; and
determining an elapsed time during which the agent associated with the combined identifier interacted with the content of the network site; and
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
generating, based on the set of values and by the computer system, an output identifying that one or more agent actions correspond to low quality interactions with the content presented on the network site.
Application 19/031,465
US Patent 12,284,103
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes: measuring an elapsed time between at least two consecutive clicks associated with the combined identifier;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes:
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website;
measuring an elapsed time between at least two consecutive clicks associated with the combined identifier; and
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website; and
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
Application 19/031,465
US Patent 12,615,200
identifying, by a computer system comprising one or more hardware processors, a set of agent actions in a server log, wherein the set of agent actions comprises one or more clicks by agents when the agents interact via the Internet with an advertisement displayed on a publisher website, and wherein each agent action of the set of agent actions is associated with a combined identifier that includes: (i) an agent identifier of an agent that performed the agent action; and (ii) two or more parts of a domain name associated with the agent identifier;
in response to user interactions on advertisement links, retrieving respective click characteristics for each of a plurality of clicks, including for individual clicks, on the advertisement links, a plurality of: a respective user identifier, a respective IP address, a respective time stamp of the respective click, and a respective advertisement identifier corresponding to the respective click;
processing, by the computer system, the set of agent actions to generate a set of values, wherein the set of values represent indications of quality of clicks on the advertisement displayed on the publisher website, and wherein processing the set of agent actions includes: measuring an elapsed time between at least two consecutive clicks associated with the combined identifier;
for each of a subset of the clicks:
calculating a respective plurality of quality metrics according to the respective set of click characteristics, and computing a respective quality score as a weighted average of the respective plurality of quality metrics;
determining a velocity with which a click associated with the combined identifier occurs on the advertisement displayed on the publisher website;
generating, based on the set of values and by the computer system, an output identifying that one or more of the clicks correspond to low quality clicks with the advertisement displayed on the publisher website.
Claim 2
causing an advertiser responsible for the advertisement to not be billed for the low quality clicks.
transmitting the respective quality score to a billing platform, thereby causing the billing platform (1) to compare the respective quality score to one or more threshold quality values, (2) to not charge a corresponding advertiser for the respective click when the respective quality score does not meet a first threshold quality value, and (3) to charge the corresponding advertiser for the respective click when the respective quality score meets a second threshold quality value.
Furthermore, there is no apparent reason why applicant was prevented from presenting claims corresponding to those of the instant application during prosecution of the application which matured into a patent. See In re Schneller, 397 F.2d 350, 158 USPQ 210 (CCPA 1968). See also MPEP § 804.
Conclusion
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
April 30, 2026
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