Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 4/17/2025, Applicant responded on 9/17/2025. Amended claims 1, 10, 58. Added claims 59-63.
Claims 1, 4, 9, 10, 58-63 are pending in this application and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/17/2025 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 10, 58 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to Claims 1, 10, 58 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “...As amended, claim 1 recites sending, via a network interface, a cohort data request to a media publisher that collects user-level media impression data when users of the media publisher access digital media via client devices on a platform of the media publisher during authenticated sessions established with the media publisher. This feature cannot practically be performed in the human mind. Thus, amended claim 1 does not fall within the mental processing grouping.…the claim as a whole integrates the recited judicial exception into a practical application of the exception… Applicant's invention relate and provide improvements to digital media measurement technology. As described in Applicant's Specification, systems and methods for determining an audience reach for an audience of Internet-accessible media are disclosed. The disclosed systems and methods estimate user-level media accesses using cohort-level impression data. Users of an audience measurement entity are divided into cohorts multiple times. A computing system then requests cohort-level impression data from a media publisher: The media publishers, also referred to herein as publishers, may collect user-level media impression data (e.g., media exposure data, frequency of media exposure, etc.) when users of the publisher (e.g., a database proprietor) access media during authenticated sessions established with the publisher. For example, a user may log-in to a website of a publisher to initiate an authenticated session. During the authenticated session, the publisher is able to record media impressions associated with the user. Due to a desire to protect privacies of its users, a publisher may not provide user-level media impression data to an AME. However, the publisher may be more willing to provide impression data aggregated into groups of users (e.g., as cohort-level data). In examples disclosed herein, an AME can define cohorts of users and request cohort-level impression data from one or more publishers. In response to the request, the publisher can aggregate the user-level media impression data into cohort-level impression data and provide the cohort- level impression data to the AME. In examples disclosed herein, the AME can divide users into cohorts multiple times and request the multiple sets of cohort-level impression data from the one or more publishers. See par. 36. Further, the computing system then uses the cohort-level impression data to estimate user-level media access reach. See par. 37… the disclosed systems and methods allow for measuring user-level reach in a manner that protects user privacy. Based on the above-referenced aspects of the specification, one of ordinary skill in the art would recognize the claimed invention as providing an improvement…Amended claim 1 reflects this disclosed improvement. For instance, amended claim 1 relates to these concepts and recites, among other things (i) sending, via a network interface, a cohort data request to a media publisher (ii) and based on the cohort data request, receiving from the media publisher, cohort-level impression data including aggregated numbers of impressions for the digital media for respective cohorts. Thus, the claimed invention provides an improvement to a technical field, namely that of digital media measurement technology, and provides a particular solution to a problem arising in that technical field. Because the claimed invention provides such an improvement, the additional elements in combination integrate the alleged exception into a practical application… Given the above-referenced disclosure of the technical solution and technical problem in Applicant's specification, and the detailed recitations in amended claims 1, 10, and 58 of sending a cohort data request to a media publisher specifying user identifier to cohort assignments, receiving cohort level impression data from the media publisher, and determining an average cohort-level reach for a user, Applicant submits that it is not more likely than not that amended claims 1, 8, and 15 are ineligible under 35 U.S.C. § 101.….”. The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, however, by Applicant’s own admission, the claims are indeed directed to, …media measurement.…determining an audience reach for an audience… estimate user-level media accesses using cohort-level impression data… audience measurement entity are divided into cohorts multiple times…requests cohort-level impression data from a media publisher: The media publishers, also referred to herein as publishers, may collect user-level media impression data (e.g., media exposure data, frequency of media exposure, etc.) when users of the publisher (e.g., a database proprietor) access media… to protect privacies of its users…provide impression data aggregated into groups of users (e.g., as cohort-level data)…. an AME can define cohorts of users and request cohort-level impression data from one or more publishers. In response to the request, the publisher can aggregate the user-level media impression data into cohort-level impression data and provide the cohort- level impression data to the AME… AME can divide users into cohorts multiple times and request the multiple sets of cohort-level impression data from the one or more publishers… allow for measuring user-level reach in a manner that protects user privacy…, which is a problem directed to, organizing human activity, mathematical concepts, and a mental process, as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components performing extra solution activities, gathering data and outputting data, and generally linked to a technical environment, i.e. computer, digital media. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more in Step 2B. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Response to Arguments - 35 USC § Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s remarks are moot in light of new grounds of rejections necessitated by Applicant’s amendments.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 61-62 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 61 recites “the AME”, it is unclear to what this element refers. Further, this element lacks antecedent basis. Appropriate correction is required.
Claims 62 depend on claim 61 and do not cure the aforementioned deficiencies of claim 61, and thus, claims 62 is rejected for the reasons set forth above regarding claim 61 as a result.
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, 4, 9, 10, 58-63 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (similarly 10, 58) recites, ” … perform a set of operations. the set of operations comprising:
sending, via a …, a cohort data request to a media publisher that collects user-level media impression data when subscribers of the media publisher access … media via … of the media publisher during … sessions established with the media publisher, wherein the cohort data request specifies, for each of multiple iterations, user identifier to cohort assignments for a plurality of cohorts, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the user identifier to cohort assignments vary across iterations of the multiple iterations;
based on the cohort data request, receiving from the media publisher, cohort- level impression data for each of the multiple iterations, wherein the cohort-level impression data includes aggregated numbers of impressions for the … media for respective cohorts of the plurality of cohorts;
determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user;
determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and
generating a report including the reach probability for the first user.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …sending, via a …, a cohort data request to a media publisher that collects user-level media impression data when subscribers of the media publisher access … media via … of the media publisher during … sessions established with the media publisher, wherein the cohort data request specifies, for each of multiple iterations, user identifier to cohort assignments for a plurality of cohorts, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the user identifier to cohort assignments vary across iterations of the multiple iterations; based on the cohort data request, receiving from the media publisher, cohort- level impression data for each of the multiple iterations, wherein the cohort-level impression data includes aggregated numbers of impressions for the … media for respective cohorts of the plurality of cohorts; determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user; determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and generating a report including the reach probability for the first user.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …sending, via a …, a cohort data request to a media publisher that collects user-level media impression data when subscribers of the media publisher access … media via … of the media publisher during … sessions established with the media publisher, wherein the cohort data request specifies, for each of multiple iterations, user identifier to cohort assignments for a plurality of cohorts, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the user identifier to cohort assignments vary across iterations of the multiple iterations; based on the cohort data request, receiving from the media publisher, cohort- level impression data for each of the multiple iterations, wherein the cohort-level impression data includes aggregated numbers of impressions for the … media for respective cohorts of the plurality of cohorts; determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user; determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and generating a report including the reach probability for the first user…; therefore, the claims are directed to a mental process.
Further, …sending, via a …, a cohort data request to a media publisher that collects user-level media impression data when subscribers of the media publisher access … media via … of the media publisher during … sessions established with the media publisher, wherein the cohort data request specifies, for each of multiple iterations, user identifier to cohort assignments for a plurality of cohorts, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the user identifier to cohort assignments vary across iterations of the multiple iterations; based on the cohort data request, receiving from the media publisher, cohort- level impression data for each of the multiple iterations, wherein the cohort-level impression data includes aggregated numbers of impressions for the … media for respective cohorts of the plurality of cohorts; determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user; determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and generating a report including the reach probability for the first user…, under the broadest reasonable interpretation, are humans monitoring media exposure reach and impression to other humans, therefore it is, managing personal behavior or relationships or interactions between people. Thus, the claims are directed to certain methods of organizing human activity.
Additionally, …determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user; determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and generating a report including the reach probability for the first user…, are directed to mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, mathematical concepts and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 10, 58: A computing system configured to, client devices, A non-transitory computer readable storage medium comprising instructions that, when executed, cause a computing system to, network interface, digital media via client devices on a platform of the media publisher during authenticated sessions established with the media publisher
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “sending, via a …, a cohort data request …”, “…receiving from the…”, “generating…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “sending, via a …, a cohort data request to…”, “…receiving from …”, data output –“generating……”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0042] Example user devices 106 (e.g., the client devices 106) can be stationary or portable computers, handheld computing devices, smart phones, Internet appliances, and/or any other type of device that may be capable of accessing media over a network (e.g., the Internet, the network 108, etc.). In the illustrated example of FIG. 1, the user devices 106 include a smartphone (e.g., an Apple iPhone@ smartphone, a Motorola® Moto X® smartphone, an Android® smartphone, etc.) and a laptop computer. However, any other type(s) of device(s) may additionally or alternatively be used such as, for example, a tablet (e.g., an Apple iPad@ tablet device an Android® tablet device, etc.), a desktop computer, a camera, an Internet compatible television, a smart TV, etc. Examples disclosed herein may be used to collect impression information for any type of media including content and/or advertisements. Media may include advertising and/or content delivered via websites, streaming video, streaming audio, Internet protocol television (IPTV), movies, television, radio and/or any other vehicle for delivering media. In some examples, media includes user-generated media that is, for example, uploaded to media upload sites, such as a YouTube® website, and subsequently downloaded and/or streamed by one or more other client devices for playback. Media may also include advertisements. Advertisements are typically distributed with content (e.g., programming, on-demand video and/or audio). Traditionally, content is provided at little or no cost to the audience because it is subsidized by advertisers that pay to have their advertisements distributed with the content. The user devices 106 of FIG. 1 are used to access (e.g., request, receive, render and/or present) online media provided, for example, by a web server. For example, users 104 can execute a web browser on the user devices 106 to request streaming media (e.g., via an HTTP request) from a media hosting server. The web server can be any web browser used to provide media (e.g., a YouTube@ website) that is accessed, through the example network 108, by the example users 104 on example user device(s) 106.
[0112] FIG. 12 is a block diagram of an example processor platform 1200 structured to execute and/or instantiate the machine readable instructions and/or the operations of FIGS. 7-11 to implement the audience metrics generator circuitry 122 of FIG. 2. The processor platform1200 can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), or any other type of computing device.
[01131 The processor platform 1200 of the illustrated example includes processor circuitry 1212. The processor circuitry 1212 of the illustrated example is hardware. For example, the processor circuitry 1212 can be implemented by one or more integrated circuits, logic circuits, FPGAs, microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry 1212 may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry 1212implements the audience metrics generator circuitry 122, the network interface circuitry 202, the reporter circuitry 206, the cohort management circuitry 208, the statistics generator circuitry 210, the metrics calculator circuitry 212, and the graph generator circuitry 214.
[0131] Although FIGS. 13 and 14 illustrate two example implementations of the processor circuitry 1212 of FIG. 12, many other approaches are contemplated. For example, as mentioned above, modern FPGA circuitry may include an on-board CPU, such as one or more of the example CPU 1420 of FIG. 14. Therefore, the processor circuitry 1212 of FIG. 12 may additionally be implemented by combining the example microprocessor 1300 of FIG. 13 and the example FPGA circuitry 1400 of FIG. 14. In some such hybrid examples, a first portion of the machine readable instructions represented by the flowcharts of FIGS. 7-11 may be executed by one or more of the cores 1302 of FIG. 13, a second portion of the machine readable instructions represented by the flowcharts of FIGS. 7-11 may be executed by the FPGA circuitry 1400 of FIG. 14, and/or a third portion of the machine readable instructions represented by the flowcharts of FIGS. 7-11 may be executed by an ASIC. It should be understood that some or all of the circuitry of FIG. 2 may, thus, be instantiated at the same or different times. Some or all of the circuitry may be instantiated, for example, in one or more threads executing concurrently and/or in series. Moreover, in some examples, some or all of the circuitry of FIG. 2 may be implemented within one or more virtual machines and/or containers executing on the microprocessor.
[0133] A block diagram illustrating an example software distribution platform 1505 to distribute software such as the example machine readable instructions 1232 of FIG. 12 to hardware devices owned and/or operated by third parties is illustrated in FIG. 15. The example software distribution platform1505may be implemented by any computer server, data facility, cloud service, etc., capable of storing and transmitting software to other computing devices. The third parties may be customers of the entity owning and/or operating the software distribution platform 1505. For example, the entity that owns and/or operates the software distribution platform1505 may be a developer, a seller, and/or a licensor of software such as the example machine readable instructions 1232 of FIG. 12. The third parties may be consumers, users, retailers, OEMs, etc., who purchase and/or license the software for use and/or re-sale and/or sub-licensing. In the illustrated example, the software distribution platform1505 includes one or more servers and oneor more storage devices. The storage devices store the machine readable instructions 1232, which may correspond to the example machine readable instructions 700, 800, 900, 1000, 1010 of FIGS. 7-11, as described above. The one or more servers of the example software distribution platform 1505 are in communication with an example network 1510, which may correspond to any one or more of the Internet and/or any of the example networks 108 described above. In some examples, the one or more servers are responsive to requests to transmit the software to a requesting party as part of a commercial transaction. Payment for the delivery, sale, and/or license of the software may be handled by the one or more servers of the software distribution platform and/or by a third party payment entity. The servers enable purchasers and/or licensors to download the machine readable instructions 1232 from the software distribution platform 1505. For example, the software, which may correspond to the example machine readable instructions 700, 800, 900, 1000, 1010 of FIGS. 7-11, may be downloaded to the example processor platform 1200, which is to execute the machine readable instructions 1232 to implement the audience metrics generator circuitry 122. In some examples, one or more servers of the software distribution platform 1505 periodically offer, transmit, and/or force updates to the software (e.g., the example machine readable instructions 1232 of FIG. 12) to ensure improvements, patches, updates, etc., are distributed and applied to the software at the end user devices.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 4, 9, 10, 58-63 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 9, 10, 58-63 is/are rejected under 35 U.S.C. 103 as being unpatentable by US20140100945A1 to Kitts et al., (hereinafter referred to as “Kitts”) in view of US Patent Publication to US11861538B1 to Ye et al. (hereinafter referred to as “Ye”) in view of US Patent Publication to US20090055267A1 to Roker et al. (hereinafter referred to as “Roker”)
As per Claim 1, Kitts teaches: (Currently amended) A computing system configured to perform a set of operations, the set of operations comprising: ([0048])
sending, via a network interface, a cohort data request to a media publisher that collects user-level media impression data when subscribers of the media publisher access digital media via client devices on a platform of the media publisher during … sessions established with the media publisher, wherein the cohort data request specifies, for each of multiple iterations, user identifier …, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the … vary across iterations of the multiple iterations; (in at least [0049]-[0054][0048] FIG. 1 is a block diagram of a system architecture 100 in which embodiments of the present invention described herein may operate. The system architecture 100 enables an advertisement platform 115 (e.g., the Lucid Commerce®-Fathom Platform®) to collect data relevant to an advertising campaign, to set up experiments, to track an advertising campaign in real time, and to otherwise control an advertisement campaign. The system architecture 100 includes an advertisement platform 115 connected to platform consumers 105, agency data 110, audience data 120, and advertiser data 125. [0048] The advertisement platform 115 receives as input the agency data 110, audience data 120 and advertiser data 125. The agency data 110 may include media plan data (e.g., data indicating advertisements to run, target conversions (e.g., number of sales), target audiences, a plan budget, and so forth), verification data 144 (e.g., data confirming that advertisements were run), and trafficking data 146 (e.g., data indicating what advertisements are shipped to which TV stations). Preferably, all the agency data 110 about what media is being purchased, run, and trafficked to stations is collected and provided to the advertisement platform 115 to ensure that there is an accurate representation of the television media. This may include setting up data feeds for the media plan data 142, verification data 144, and trafficking data 146. [0050] The advertiser data 125 includes data on sales of products and/or services that are being advertised. Advertiser data 125 may include, for example, call center data 152, electronic commerce (ecommerce) data 154 and order management data 156. The advertisement platform 115 may set up a data feed to one or more call centers to receive accurate data about phone orders placed by the call centers for the advertised products or services. Additionally, recurring data feeds may be set up with the vendor or internal system of the advertiser that records orders that come in from the advertiser's website (ecommerce data 154). Recurring data feeds with the order vendor or internal system that physically handles the logistics of billing and/or fulfillment may also be established (order management data 156). This may be used for subsequent purchases such as subscriptions and for returns, bad debt, etc. to accurately account for revenue. This data may also originate from one or more retail Point of Sale systems. [0051] The advertising platform 115 may generate a record for every caller, web-converter, and ultimate purchaser of the advertised product or server that gets reported via the advertiser data 125. The advertising platform 115 may append to each record the data attributes for the purchasers in terms of demographics, psychographics, behavior, and so forth. Such demographic and other information may be provided by data bureaus such as Experian®, Acxiom®, Claritas®, etc. In one embodiment, advertiser data 125 includes consumer information enrichment data 158 that encompasses such demographics, behavior and psychographics information. [0052] Audience data 120 may include viewer panel data 162, guide service data 164 and/or viewer information enrichment data 166. The guide service data 164 may include the programming of what is going to run on television for the weeks ahead. The viewer information enrichment data 166 may be similar to the consumer info enrichment data 158, but may be associated with viewers of television programming as opposed to consumers of goods and services. A feed of such viewer data 166 may include demographic, psychographic, and/or behavioral data. This feed may be obtained using the purchases of products on television, set top box viewer records, or existing panels. [0053] All of the feeds of the various types of data may be received and stored into a feed repository 172 by the advertisement platform 115. All of the underlying data may be put into production and all of the data feeds may be loaded into an intermediate format for cleansing, adding identifier's, etc. Personally Identifiable Information (PII) may also be extracted from the data feeds and routed to a separate pipeline for secure storage. [0057] At block 210, processing logic selects a group granularity (e.g., for treatment groups). The group may be a geographic cell or area. Different group granularities may include: designated market areas (DMAs), cable operator zones (e.g., an area serviced by a cable operator), 5-digit zip codes, 9-digit zip codes, street address, cities, states, counties, towns, etc. In one embodiment, the group granularities may be selected based on one or more conditions. For example, media selected should not overlap with each other at the selected granularity. In another example, the group granularity should be low enough to support the number of treatment groups that the user wants to field for testing. [0079] FIG. 12 illustrates a user interface 1200 showing various factors used by a treatment area selector, according to one embodiment. Any of the illustrated factors may be adjusted by a user via the user interface. [0187] The algorithm for selecting controls may be iterative, similar to treatment group selection. However, in one embodiment, multiple control groups are selected for each treatment group. The set of control groups may be assembled to collectively match the treatment group. In one embodiment, a best matching control group is selected. Say that this control group matches well, but has too few African Americans. When selecting the next control group that is being matched to the treatment group, the error function is the match between the total controls (all control groups selected for that treatment group), including the new candidate control group and the originally selected control group. As a result, if one candidate control group causes the African American quota to move closer to the treatment group, then this control group will be favored. As a result, the iterative procedure may self-correct by successively selecting areas which together have demographics and sales which match the treatment group.)
based on the cohort data request, receiving from the media publisher, cohort- level impression data for each of the multiple iterations, wherein the cohort-level impression data includes aggregated numbers of impressions for the digital media for respective cohorts of the plurality of cohorts; (in at least [0049] The advertisement platform 115 receives as input the agency data 110, audience data 120 and advertiser data 125. The agency data 110 may include media plan data (e.g., data indicating advertisements to run, target conversions (e.g., number of sales), target audiences, a plan budget, and so forth), verification data 144 (e.g., data confirming that advertisements were run), and trafficking data 146 (e.g., data indicating what advertisements are shipped to which TV stations). Preferably, all the agency data 110 about what media is being purchased, run, and trafficked to stations is collected and provided to the advertisement platform 115 to ensure that there is an accurate representation of the television media. This may include setting up data feeds for the media plan data 142, verification data 144, and trafficking data 146. [0053] All of the feeds of the various types of data may be received and stored into a feed repository 172 by the advertisement platform 115. All of the underlying data may be put into production and all of the data feeds may be loaded into an intermediate format for cleansing, adding identifier's, etc. Personally Identifiable Information (PII) may also be extracted from the data feeds and routed to a separate pipeline for secure storage. The advertisement platform 115 may ingest all of the data from the data feeds. The data may be aggregated and final validation of the results may be automatically completed by the advertisement platform 115. After this, the data may be loaded into one or more data stores 176 (e.g., databases) for use with any upstream media systems. These include the ability to support media planning through purchase suggestions, revenue predictions, pricing suggestions, performance results, etc. [0054] The platform consumers 105 may include an agency 130 (e.g., an advertising agency) and an advertiser 132 (e.g., a manufacturer of a product or service who wishes to advertise that product or service). Results of the real time tracking, advertisement optimization suggestions, advertising models (e.g., landscapes), etc. may be provided to the platform consumers 105 to enable them to fully understand and optimize their advertisement campaigns in real time or pseudo-real time (e.g., while the campaigns are ongoing). [0056] FIG. 2, the method 200 starts by selecting media types that will be used for testing at block 205. Media types may include, but are not limited to: television, radio, billboards, magazines, newspapers, pay per click advertisements, banner advertisements, etc. Embodiments will be discussed herein with reference to television advertising [0079] FIG. 12 illustrates a user interface 1200 showing various factors used by a treatment area selector, according to one embodiment. Any of the illustrated factors may be adjusted by a user via the user interface. [0108] factors may be incorporated into a goodness value (e.g., a control score). Areas with the highest goodness values (e.g., the top 10 areas) for each treatment group may be selected to be an aggregate control group. An area may be selected as a control group for multiple treatment groups if the area is appropriate for the each of those treatment groups. [0117] analytics engine 274 decomposes each individual set-top box viewer into a multiple element demographic variable-value vector I, which in one embodiment is a 400 element vector. Analytics engine 274 then compares the viewer demographics to the demographics of purchasers of the advertiser's product P. This method has the advantage that it will work across all possible TV programs)
determining an average cohort-level reach for a first user of the plurality of users using aggregated numbers of impressions for cohorts including the first user; (in at least [0082] When selecting treatment groups using average areas, processing logic may create a goodness function which measures averageness of sales, geographic dispersion, and averageness of population. Areas may be selected on the basis of being as “average as possible” for a business. When extrapolating to the national level, biases between the local area and national are minimal, and it is possible to scale-up by multiplying by the ratio of TV households in national to the area selected. [0083] Multiple factors may be used when selecting average areas. The first factor may be sales per capita. If a candidate area has sales per capita (e.g., SalesPerCapita(L)) that are higher than the national average, then it is possible that the area in question might have advertising elasticities which are also different. In order to introduce fewer assumptions or differences into the design, processing logic may use areas which have sales per capita close to the national average. [0091] selecting treatment groups may be the census disparity from the United States (US) average (e.g., CensusDisparityFromUSAverage). The census disparity from the US average may be the mean absolute difference between the US population census demographic average and the demographic vector of a particular region. [0127] the control groups might average 10 conversions per week and grew by 1 conversion per week. The treatment group might average only 1 conversion per week and grew by 0.1 conversions. Using the standard DD function, the treatment group would be assumed to undergo a 1 conversion upward movement (the same as control group))
determining a reach probability for the first user based on a comparison of the average cohort-level reach for the first user and a census-level reach; and (in at least [0011] By measuring the change in sales or conversions that occur in the treatment groups when compared to the control groups, the effect of the experimental advertising campaign on sales within the treatment groups may be calculated. These effects may then be extrapolated to the larger region (e.g., to the state, to the country, etc.). This allows an advertiser to track, in real time, the effects of an advertising campaign for a larger geographic region (e.g., a state, a country), using smaller regions (e.g., the treatment groups and control groups). [0078] Selecting multiple geographic areas that have the same factors and applying the same experimental treatment (e.g., same advertising weight) to these areas may increase the probability that changes in sales are due the experimental treatment (e.g., the local ad campaign). [0091] A sixth factor for selecting treatment groups may be the census disparity from the United States (US) average (e.g., CensusDisparityFromUSAverage). The census disparity from the US average may be the mean absolute difference between the US population census demographic average and the demographic vector of a particular region. A lower value for the census disparity may be better since this may indicate that the area is not greatly different from the US average. [0127] the control groups might average 10 conversions per week and grew by 1 conversion per week. The treatment group might average only 1 conversion per week and grew by 0.1 conversions. Using the standard DD function, the treatment group would be assumed to undergo a 1 conversion upward movement (the same as control group) [0131] There may be some difference in the behavior and/or means of different groups, and these inherent differences are all being “normalized out” so that the group is only being measured against its own performance during a pre-experimental period. The difference in lifts method may also make predictions for any experimental area or group of interest. The estimate of percent change may be converted into conversions by multiplying with the pre-experiment period average to predict actual conversions in the area or group of interest. The difference in lifts method may look for an increase in conversions, as compared to the typical conversion-generating performance of each group. The difference in lifts method may use historical data to calculate typical or baseline performance, and then may look for changes compared to that historical baseline. Control group increases (or decreases) are first calculated, and then the increase or decrease for the treatment groups are calculated. The excess treatment group percentage change compared to control group may be due to the intervention and is then equal to the lift due to experimental ad campaign. The number of national conversions that, when multiplied by the lift, reach the actual number observed are the national conversions not due to the ad campaign. The excess that when added reach observed national conversions are due to the ad campaign.)
generating a report including the reach probability for the first user. (in at least [0093] FIG. 13A illustrates treatment areas and control areas for a hypothetical local campaign, according to one embodiment. These treatment areas and control areas may be selected according to the techniques discussed herein. FIG. 13B illustrates control area selection fitness function 1350, according to one embodiment. FIGS. 14A-14B illustrate treatment area criteria and values for a hypothetical local campaign. [0114] degree of targetedness, is the percentage of viewers who are like the converting customer. The degree of targetedness is equal to “probability of buyer”. Accordingly, the higher a targetedness rating for a viewer, the greater the probability that the viewer will convert. We provide two targetedness metrics: (a) Direct Targeting and (b) Demographic Targeting. [0115] Direct Targeting looks at what known buyers (converters) of the product are watching, and then creates a probability for each media instance. The method calculates the probability of buyer given the TV programming mix that the individual who is being scored is watching. [0151] At block 290, the processing logic determines whether the national ad campaign has ended. If the ad campaign is ended, the method ends. If the ad campaign is ongoing, then the method may return to block 240, and additional operations may be performed to continue to track the ad campaign in real time. Various analyses, measurements, models, and reports may be generated using the above formulas, data, metrics, lifts, and other information described above. These analyses and reports may be useful in changing or optimizing a media campaign.)
Although implied, Kitts does not expressly disclose the following limitations, which however, are taught by Ye,
… cohort data request specifies, for each of multiple iterations, user identifier to cohort assignments for a plurality of cohorts, wherein each cohort comprises a plurality of user identifiers for respective users of a plurality of users, and wherein the user identifier to cohort assignments vary across iterations of the multiple iterations; (in at least [col15 ln25-45] A plurality of user selection strategies corresponding to Obj1 may be obtained, e.g., at an automated strategy optimizer of the analytics service in the depicted embodiment from the strategy generators (element 607). A strategy generator may provide one or more strategies. A given strategy may assign respective selection probabilities to individual users or groups of users from a user population in various embodiments. If the strategy is implemented, users selected according to the probabilities assigned by the strategy may receive at least some content associated with the offering set for which Obj1 was created. In various embodiments, an initial subset of the received strategies may be selected as candidate strategies for iterative optimization. Respective non-negative weights may be assigned to individual candidate strategies, and a sub-sample of the user population may be chosen for conducting at least the initial optimization iterations in various embodiments. In some embodiments, the sub-sample may be selected using random selection. [col15 ln55-67] During a given iteration started in operations corresponding to element 610, aggregated selection probabilities for users of the sub-sample may be computed based on per-strategy selection probabilities and per-strategy weights (element 613). Presentation of content to the sub-sample users may be initiated based on aggregated selection probabilities in various embodiments; some users may be put in a hold-out group (i.e., a group to which content associated with the offering set of Obj1 is not provided) (element 616). [col8 ln25-45] The analytics service 102 may also have access to end user population metadata 122 in the depicted embodiment, comprising user names/identifiers, demographic information which the users have agreed to provide via opt-in interfaces, records of user interactions and transactions with respect to offerings, and so on. Such user metadata may be used to generate context vectors representing individual users, which are then utilized in the computations of strategy optimization iterations.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kitts with the aforementioned teachings of Ye, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Kitts with the motivation of, ...(a) substantially reducing the overall amount of computation, storage and networking resources required to disseminate content associated with organization offerings to achieve specified organizational goals, (b) improving the user experience of customers of the organizations, e.g., by avoiding presentation of content which is likely to be of less interest to the customers, and/or (c) providing a taxonomy for specifying objectives associated with content presentation using easy-to-use interfaces, and generating machine readable versions of such objectives in a standard format which can be consumed by a variety of strategy generators....., as recited in Ye.
Although implied, Kitts in view of Ye does not expressly disclose the following limitations, which however, are taught by Roker,
…media publisher that collects user-level media impression data when subscribers of the media publisher access digital media via client devices on a platform of the media publisher during authenticated sessions established with the media publisher… (in at least [0096] one or more IABAs dedicated to interoperate with adcasters serving subscribers located in particular areas of the world. For example, in North America 818 a U.S. West Coast IABA 820 may interoperate with an adcaster (e.g., the adcaster 844) connecting an ISP 828 serving subscribers 834. One or more IABAs 838 may enhance RTs associated with subscribers in Europe 844. An IABA 850 may enhance RTs associated with subscribers in India 854, and so on. [0108] The adcaster 1000 may also include subscriber identification (ID) logic 1020 coupled to the communication server 1010. The subscriber ID logic 1020 interfaces with an ISP authentication mechanism, including but not limited to an authentication, authorization, and accounting (AAA) service. The AAA service may be implemented with a remote authentication dial-in user service (RADIUS) server, a dynamic host configuration protocol (DHCP) server, a Kerberos server, or a terminal access controller access-control system (TACACS) server, among other authentication devices and protocols. The subscriber ID logic 1020 may itself comprise an authentication mechanism including one or more of the aforementioned authentication mechanisms. [0109] The subscriber ID logic 1020 identifies the subscriber 1016 and the user session associated with the subscriber data stream 1004. Identification logic may identify multiple user sessions associated with a single subscriber account. Distinguishing a particular user may be important because an ad placement opportunity may be valued according to the degree to which RTs are pinpointed to the interests of a particular user. [0110] The adcaster 1000 may further include a subscriber relevancy AI module 1030 coupled to the communication server 1010. Having identified the subscriber 1016, the adcaster 1000 uses the subscriber relevancy AI module 1030 to create RTs associated with the subscriber 1016. The RTs may be created from relevancy information harvested from the subscriber data stream 1004, from an ISP subscriber database 1032, from internet connection provisioning information associated with the subscriber 1016, and/or from any other relevancy information discernible by the subscriber relevancy AI module 1030. The RTs are stored in a subscriber RT database 1026. [0113] FIG. 11 is a set of RT records 1100 associated with the subscriber RT database 1026 according to various example embodiments. Considering FIG. 11 in view of FIG. 10, the adcaster 1000 engages subscriber ID logic 1020 to assign a user identification (UID) 1102 based upon the afore-mentioned example authentication process and upon information in a browser data stream. In a simple example embodiment the UID 1102 identifies an IACD to which a particular internet protocol (IP) address is assigned. More complex example embodiments may assign UIDs based upon browsing history. The latter complex example embodiments may discriminate between multiple users sharing a single IACD and/or between users who roam between multiple IACDs. [0124] Other data in the browsing stream that may be used to identify a particular user may include an authentication ID generated by an authentication process executed by the ISP and/or by an adcaster, a user agent identifier (e.g., “MSN” or “mozilla”), and/or a language identifier (e.g., “get/yahoo.com” or “get/apple.com”). [0146] content provider redirects the ad request from the subscriber to the IABA 1300, the IABA 1300 may respond to the ad request by supplying appropriate ad content directly from the ad servers 1340 or the ad feed server 1344, by pointing the ad requester to appropriate ad content hosted elsewhere, or by providing targeting RTs to assist the requesting entity in finding an appropriately-targeted ad. The IABA 1300 may pull directly-supplied ad content from one or more ad servers 1340 hosted by the IABA 1300 or may feed ad content from an external ad-feed server 1344. [0248] Adcasting customizes content delivery to improve the browsing experience for the end user. This may includes localization of advertising content by supporting the specification of localized advertising campaigns and the subsequent delivery of localized advertising material. A secondary example use of data acquired in the course of adcasting example embodiments is the mining of this data to reveal trends and develop models to further refine and improve the browsing experience. [0268] A cookie in this context is a small file containing a string of characters that uniquely identifies the End Users' browser. Cookies are used to improve the quality of the ad serving partners' service by limiting ad display frequency, storing user preferences and tracking user trends.)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kitts in view of Ye with the aforementioned teachings of Roker, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Kitts in view of Ye with the motivation of, ...Cookies are used to improve the quality of the ad serving partners' service by limiting ad display frequency...to customize content delivery in order to improve the end user's browsing experience. An example would be localization of advertising content. A secondary use of data acquired in the course of adcasting is the mining of this data to reveal trends and develop models to further refine and improve the browsing experience…Adcasting customizes content delivery to improve the browsing experience for the end user. This may includes localization of advertising content by supporting the specification of localized advertising campaigns and the subsequent delivery of localized advertising material. A secondary example use of data acquired in the course of adcasting example embodiments is the mining of this data to reveal trends and develop models to further refine and improve the browsing experience...performing real-time calculations to optimize the delivery of content on the fly…, as recited in Roker.
As per Claim 4, Kitts teaches: (Previously presented) The computing system of claim 1,
wherein the determining the reach probability for the first user comprises determining the reach probability for the first user is zero if at least one of a first cohort-level reach for a first cohort iteration or second cohort-level reach for a second cohort iteration is zero. (in at least [0060] One other factor may be a rarity of events. For example, if a conversion or sale is generated on average every 10 airings of an advertisement, then a station could easily have 0 sales or conversions just due to chance. Another factor is the variability of events. For example, if sales fluctuate between 0 and 800 per day, with a mean of 80, then sales of 100 for the day (which is 1.2× lift) may be due to chance. Products or services with higher standard deviations for spot sales may require a greater difference in means to ensure changes are statistically significant. [0115] Direct Targeting looks at what known buyers (converters) of the product are watching, and then creates a probability for each media instance. The method calculates the probability of buyer given the TV programming mix that the individual who is being scored is watching…where i is an individual set-top box viewer who is being scored, m is one of the media instances which viewer i has watched, B(m) is the number of buyers viewing media program m, and V(m) is the universe of all viewers of media m. For example, if there were 10 buyers out of 100 on Program A, and 1 out of 100 on Program B, and an individual viewed only Program A and B, then their buyer probability is 5.5%.)
As per Claim 9, Kitts teaches: (Previously presented) The computing system of claim 1, wherein the set of operations further comprises:
determining an average cohort-level frequency for the first user using the cohort-level impression data; (in at least [0067] how to calculate how many impressions to apply to treatment areas in order to produce a lift that will be statistically detectable. In TV, advertisement media is often measured in Gross Rating Points (GRPs) per week, which is a measure of number of impressions that each US household would typically see from the ad campaign multiplied by 100; or alternatively Impressions per Thousand Households per Week (Imp/MHH/Wk) which is much the same but is impressions viewed per household multiplied by 1000. [0131] There may be some difference in the behavior and/or means of different groups, and these inherent differences are all being “normalized out” so that the group is only being measured against its own performance during a pre-experimental period. The difference in lifts method may also make predictions for any experimental area or group of interest. The estimate of percent change may be converted into conversions by multiplying with the pre-experiment period average to predict actual conversions in the area or group of interest.)
determining an estimated frequency for the first user based on the average cohort-level frequency for the first user and a census-level frequency; and (in at least [0067] Assume that the advertiser has been running media in the past with a national GRP (GRPN) of 176 and that the advertiser may plan on four weeks of test or experimental media (e.g., W=4). The local area used as a treatment group may have 1.2 million TV households (e.g., TVHHL) and there may be 112 million TV households (e.g., TVHHN) nationally. The national impressions of the media (IN) may be calculated as follows: IN=GRPN*TVHHN/100. If an advertiser is aware of the sales per impression, then the advertiser may answer a questionnaire and indicate their cpiN value. [0129] in treatment group d in time-period t appropriately normalized to a quantity per unit of time (e.g. a day), where dj is the jth local treatment group selected so as to be matched to national, where Di(d) is the ith local control group matched to d, where t2 is a time-period during the experimental ad campaign, and where t1 is prior to the experimental ad campaign. Each of the time-periods represents a certain amount of time during which measurement is taken. For example, t1 might span two months prior to the experiment start, and t2 might be the first week of the experimental ad campaign. Quantities have may be normalized to equivalent units so that different lengths of time do not increase the quantities. TVHHd are the number of TV Households in treatment group d. TVHHN=112,000,000 are the number of TV Households nationally.)
including the estimated frequency for the first user in the report. (in at least [0143] Media is then selected according to the Greedy Algorithm below, where tratiotarget and tcpmtarget are constraints which are set using the above algorithm, which is dependent upon current performance. Our objective is to select a set of TV media into which to insert an ad, such that advertiser value per dollar is maximized. Let Mi be a contiguous segment of time in the TV MPEG video stream that a station is offering for sale, CPM(Mi) be the cost per thousand impressions of the timeslot, r(Mi) be the degree of targetedness and I(Mi) be the impressions for the timeslot. The objective is to select a set of Media which maximizes:. [0151] If the ad campaign is ongoing, then the method may return to block 240, and additional operations may be performed to continue to track the ad campaign in real time. Various analyses, measurements, models, and reports may be generated using the above formulas, data, metrics, lifts, and other information described above. These analyses and reports may be useful in changing or optimizing a media campaign.)
As per Claim 59, Kitts teaches: (New) The computing system of claim 1,
wherein the media publisher tracks the impressions for the digital media using monitoring techniques that use …. (in at least [0040] existing cable, TV and/or satellite infrastructure may be used to identify and select treatment groups (also referred to as tracking cells, treatment areas or experimental regions) and control groups (also referred to as control areas, control cells or control regions). A group may be a combination of households that are capable of being served advertisements (e.g., broadcast regions, cable zones, geographic areas, demographic and/or other commonalities). Treatment groups are groups that will be used to run experiments, and control groups are groups that will be used as controls for comparison to the treatment groups. In one embodiment, the treatment groups and/or control groups “mirror” a larger region to which an existing ad campaign is being applied (e.g., a national region) in demographics, elasticity and/or other metrics. The treatment groups may be treated with a national advertising campaign as well as additional TV advertising (referred to as a local advertisement campaign or experimental advertisement campaign). The local or experimental ad campaign may be similar to what is occurring nationally from the national advertising campaign but at higher concentrations (e.g., more TV ads are displayed). This causes sales effects in the treatment groups to be greater in magnitude than the surrounding control groups, which may be exposed to just the national advertisement campaign.)
Although implied, Kitts in view of Ye does not expressly disclose the following limitations, which however, are taught by Roker,
…monitoring techniques that use first-party cookies…(in at least [0268] NETWORK OPERATOR's ad serving partners may send the End User one or more “cookies.” A cookie in this context is a small file containing a string of characters that uniquely identifies the End Users' browser. Cookies are used to improve the quality of the ad serving partners' service by limiting ad display frequency, storing user preferences and tracking user trends.)
The reason and rationale to combine Kitts, Ye, Roker is the same as recited above.
As per Claim 60, Kitts teaches: (New) The computing system of claim 59,
wherein the media publisher stores data indicative of the impressions for the digital media in an impression database. (in at least [0053] All of the feeds of the various types of data may be received and stored into a feed repository 172 by the advertisement platform 115. All of the underlying data may be put into production and all of the data feeds may be loaded into an intermediate format for cleansing, adding identifier's, etc. Personally Identifiable Information (PII) may also be extracted from the data feeds and routed to a separate pipeline for secure storage. The advertisement platform 115 may ingest all of the data from the data feeds. The data may be aggregated and final validation of the results may be automatically completed by the advertisement platform 115. After this, the data may be loaded into one or more data stores 176 (e.g., databases) for use with any upstream media systems. These include the ability to support media planning through purchase suggestions, revenue predictions, pricing suggestions, performance results, etc. Additionally, an analytics engine 174 of the advertisement platform 115 may use the data to set up experiments, perform real time tracking of an advertisement campaign, optimize an advertisement campaign in real time, determine a landscape for an advertisement campaign, and so forth. In one embodiment, the analytics engine 174 performs one or more of the methods described herein. [0060] a conversion or sale is generated on average every 10 airings of an advertisement, then a station could easily have 0 sales or conversions just due to chance. Another factor is the variability of events. For example, if sales fluctuate between 0 and 800 per day, with a mean of 80, then sales of 100 for the day (which is 1.2× lift) may be due to chance. Products or services with higher standard deviations for spot sales may require a greater difference in means to ensure changes are statistically significant. Another factor is noise media. Noise media may be national media (e.g., a national advertising campaign) that continues to run during the experiment. For example, an experimental advertising campaign in a local area might generate around 2 conversions. However, if the national advertising campaign is running, it might generate an average of 100 conversions per day and typically vary between 80 and 120 conversions. With that amount of nationally-generated conversions and variation, 2 additional conversions may not be measurably different from noise. Another factor may be conversions or sales which result from other media channels (e.g., direct mail advertisements, web advertisements, etc.). [0115] Direct Targeting looks at what known buyers (converters) of the product are watching, and then creates a probability for each media instance. The method calculates the probability of buyer given the TV programming mix that the individual who is being scored is watching. In other words, the analytics engine reviews all programs viewed…viewer i has watched, B(m) is the number of buyers viewing media program m, and V(m) is the universe of all viewers of media m. For example, if there were 10 buyers out of 100 on Program A, and 1 out of 100 on Program B, and an individual viewed only Program A and B, then their buyer probability is 5.5%)
As per Claim 61, Kitts teaches: (New) The computing system of claim 60,
wherein the plurality of users are users that are known by the AME to be subscribers of the media publisher. (in at least [0012] The multi-dimensional model may be generated by establishing control groups and treatment groups that vary from the control groups either in degree of targetedness or advertising weight. Differences in sales metrics for each of the different treatment groups and control groups may be used along with the known degrees of targetedness and advertising weights associated with those treatment groups and control groups to develop the multi-dimensional model. The multi-dimensional model may then be used to perform real time tracking of an advertising campaign using control groups and/or treatment groups that have different degrees of targetedness and/or advertising weights from one another and/or from a larger region to which the advertising campaign is being applied. [0052] Audience data 120 may include viewer panel data 162, guide service data 164 and/or viewer information enrichment data 166. The guide service data 164 may include the programming of what is going to run on television for the weeks ahead. The viewer information enrichment data 166 may be similar to the consumer info enrichment data 158, but may be associated with viewers of television programming as opposed to consumers of goods and services. A feed of such viewer data 166 may include demographic, psychographic, and/or behavioral data. This feed may be obtained using the purchases of products on television, set top box viewer records, or existing panels. [0115] Direct Targeting looks at what known buyers (converters) of the product are watching, and then creates a probability for each media instance. The method calculates the probability of buyer given the TV programming mix that the individual who is being scored is watching.)
As per Claim 62, Kitts teaches: (New) The computing system of claim 61,
wherein the media publisher does not provide the user- level media impression data to the computing system. (in at least [0049] The agency data 110 may include media plan data (e.g., data indicating advertisements to run, target conversions (e.g., number of sales), target audiences, a plan budget, and so forth), verification data 144 (e.g., data confirming that advertisements were run), and trafficking data 146 (e.g., data indicating what advertisements are shipped to which TV stations). Preferably, all the agency data 110 about what media is being purchased, run, and trafficked to stations is collected and provided to the advertisement platform 115 to ensure that there is an accurate representation of the television media. This may include setting up data feeds for the media plan data 142, verification data 144, and trafficking data 146. [0054] The platform consumers 105 may include an agency 130 (e.g., an advertising agency) and an advertiser 132 (e.g., a manufacturer of a product or service who wishes to advertise that product or service). Results of the real time tracking, advertisement optimization suggestions, advertising models (e.g., landscapes), etc. may be provided to the platform consumers 105 to enable them to fully understand and optimize their advertisement campaigns in real time or pseudo-real time (e.g., while the campaigns are ongoing).)
As per Claim 63, Kitts teaches: (New) The computing system of claim 1, wherein:
the plurality of user identifiers are stored in a database of the computing system that includes demographic information for the respective users; and (in at least [0052] Audience data 120 may include viewer panel data 162, guide service data 164 and/or viewer information enrichment data 166. The guide service data 164 may include the programming of what is going to run on television for the weeks ahead. The viewer information enrichment data 166 may be similar to the consumer info enrichment data 158, but may be associated with viewers of television programming as opposed to consumers of goods and services. A feed of such viewer data 166 may include demographic, psychographic, and/or behavioral data. This feed may be obtained using the purchases of products on television, set top box viewer records, or existing panels.)
the generating the report is based on demographic information for the first user from the database. (in at least [0051] The advertising platform 115 may append to each record the data attributes for the purchasers in terms of demographics, psychographics, behavior, and so forth. Such demographic and other information may be provided by data bureaus such as Experian®, Acxiom®, Claritas®, etc. In one embodiment, advertiser data 125 includes consumer information enrichment data 158 that encompasses such demographics, behavior and psychographics information. [0089] demographic targeting may be used to calculate tratio. The demographic targeting method may decompose each individual viewer into a multi-element demographic variable-value vector I (e.g., a vector which includes elements such as age, income, ethnicity, etc.). In one embodiment, the vector may have any number of elements (e.g., 400, 200, etc.). The user's demographics may be compared to the demographics of purchasers of the advertiser's product P. The demographic targeting method may work across all possible TV programs, regardless of the scarcity of buyers in the population [0093] FIG. 13A illustrates treatment areas and control areas for a hypothetical local campaign, according to one embodiment. These treatment areas and control areas may be selected according to the techniques discussed herein. FIG. 13B illustrates control area selection fitness function 1350, according to one embodiment. FIGS. 14A-14B illustrate treatment area criteria and values for a hypothetical local campaign. [0114] degree of targetedness, is the percentage of viewers who are like the converting customer. The degree of targetedness is equal to “probability of buyer”. Accordingly, the higher a targetedness rating for a viewer, the greater the probability that the viewer will convert. We provide two targetedness metrics: (a) Direct Targeting and (b) Demographic Targeting. [0115] Direct Targeting looks at what known buyers (converters) of the product are watching, and then creates a probability for each media instance. The method calculates the probability of buyer given the TV programming mix that the individual who is being scored is watching. [0151] At block 290, the processing logic determines whether the national ad campaign has ended. If the ad campaign is ended, the method ends. If the ad campaign is ongoing, then the method may return to block 240, and additional operations may be performed to continue to track the ad campaign in real time. Various analyses, measurements, models, and reports may be generated using the above formulas, data, metrics, lifts, and other information described above. These analyses and reports may be useful in changing or optimizing a media campaign.)
As per Claim 10 for a non-transitory computer readable storage medium (see at least Kitts [0251]), respectively, substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
As per Claim 58 for a method (see at least Kitts [0055]), respectively, substantially recite the subject matter of Claim 1 and are rejected based on the same reasoning and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Examiner, Art Unit 3623