DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Final Office Action is responsive to Applicant's amendment filed on 25 August 2025. Applicant’s amendment on 25 August 2025 amended Claims 1, 8, and 15. Currently Claims 1, 3, 4, 7, 8, 10, 11, 14, 15, 17, 18, and 20 are pending and have been examined. Claims 2, 5, 6, 9, 12, 13, 16, and 19 were previously canceled. The Examiner notes that the 101 rejection has been maintained.
Response to Arguments
The Applicant argues on pages 13-14 that “Srivastava in view of Opdycke in further view of Dirac does no teach or suggest training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign as in claim 1”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that as provided in the previous Office action in par. [0027] of Opdycke it is taught that the training a machine learning model to detect duplication of media content, this de-duplicate implies knowing what is a duplicate and what can then be de-duplicated. Furthermore, for further clarification in par. [0063] it is disclosed that understanding the long-term impact on campaigns and utilizing production modeling by gathering and enough data to understand behavior based on exposure data (metrics)). Opdycke teaching the scheduling of marketing campaigns based on measurements which is then used to optimize audience responses. This includes as previously provided the par. [0027] that discloses a machine learning model where the audience will be exposed to stimuli and this exposure is analyzed based on the weight and decay of the influence.
It is viewed that it is inherent that if a de-duplication can occur the analysis and understanding and with the addition of understanding the exposure it is viewed that the machine learning model takes this information and then predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign. Therefore, the rejection is maintained.
The Applicant argues on pages 14-15 that “Srivastava in view of Opdycke does not teach or suggest selecting a reference media campaign based on a comparison of the estimated duplication factors”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that as provided in the par. [0038] of Srivastava it is disclosed that there is a determination of reference media using, par. [0175]-[0177] further provides an example as a comparison which estimates a duplication factor and comparing to other duplication factors [0185] and makes de-duplication impressions to provide a media campaign. Similar to what was mentioned above pertaining to Opdycke, it is viewed that it is inherent that if a de-duplication can occur the analysis and understanding and with the addition of understanding the exposure it is viewed that the machine learning model takes this information and then predict duplication factors for a media campaign based on total exposure metrics. As the de-duplication is determined based on impressions it is then viewed that the duplication in order to provide a media campaign. Therefore, it is viewed that selecting a reference media campaign based on a comparison of the estimated duplication factors and the rejection is therefore maintained.
The Applicant argues on pages 8-9 that “As amended, claim 1 recites determining, by a max entropy engine utilizing a numeric solver, final duplication factors for the query media campaign. This feature cannot practically be performed in the human mind. Thus, amended claim 1 does not fall within the mental processing grouping. For at least this reason, amended claim 1 is not directed to an abstract idea and qualifies as eligible subject matter under 35 U.S.C. § 101. And for largely the same reasons, amended claims 8 and 15 qualify as eligible subject matter under 35 U.S.C. § 101.”
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the argument that the claimed subject matter cannot practically be performed in the human mind is not persuasive for establishing under 35 U.S.C. 101. The Supreme Court in Alice Corp. v. CLS Bank International, made clear that the mere use of a computer to implement an abstract idea does not render the idea non-abstract or provide patent eligibility. The Federal Circuit has repeatedly held that mathematical calculation and algorithmic processing, even when too complex for mental performance and requiring computer implementation, remain abstract ideas. See, e.g., SAP Amercia, Inc. v. InvestPic, LLC (statistical analysis using neural networks held abstract; Electric Power Group, LLC v. Alstom S.A., (collecting data, performing calculations, and displaying results held abstract).
The claims are directed to the abstract idea of using mathematical algorithms to predict and optimize duplication factors – specifically, training machine learning model, predicting values, comparing those values, and determining final values through analysis or mathematical optimization (max entropy with numeric solver). These are fundamentally analysis of data or mathematical calculation and data processing operations. Under step two of Alice, the claims recite only generic computer components (processor, memory) performing conventional functions, with no specific technical implementation details that would constitute an inventive concept. The “max entropy engine utilizing a numeric solver” merely invokes well-known mathematical optimization techniques without providing a specific technological improvement. Accordingly, claims 1, 8, and 15 remain directed to ineligible subject matter under 35 U.S.C. 101, therefore the rejections are maintained.
The Applicant argues on pages 8-9 that “Aspects of Applicant's invention relate and provide improvements to digital advertisement campaign measurement technology. As described in Applicant's Specification, in some examples, for some media campaigns, advertisement exposure for individual media platforms (e.g., TV, online, mobile) is known, but duplication across platform combination is unknown… The resulting deduplicated audience view observed with the estimated deduplication factors for the query media campaign contains a set unique audience metrics at a platform level. With this set of unique audience metrics, a "total audience" of a given query media campaign can be determined… The resulting deduplicated audience view observed with the estimated deduplication factors for the query media campaign contains a set unique audience metrics at a platform level. With this set of unique audience metrics, a "total audience" of a given query media campaign can be determined.”
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the argument that the invention digital advertisement campaign measurement technology is not persuasive for establishing patent eligibility. While the specification describes the application of mathematical techniques to the field of advertising analytics, the claims are directed to the abstract idea of data collection, mathematical analysis, and determination of metrics – activities that are not tied to any improvement in computer technology itself. The Federal Circuit has consistently held that applying mathematical algorithms or data processing techniques to a particular field of use, even if beneficial in that field, does not render claims patent-eligible. See ChargePoint, Inc. v. SemaConnect, Inc. (improvement to abstract idea, rather than improvement to technology, insufficient for eligibility); SAP America, Inv. v. InvestPic, LLC, (applying investment analysis techniques to specific data set remains abstract).
The claimed invention addresses a business analytics challenge – measuring deduplicated audience reach across media platform – through conventional data processing operations: training models on known data, making predictions, selecting references through comparison, and applying mathematical optimization. These operations do not improve the functioning of a computer or other technology; rather, they use generic computing components to perform abstract data manipulation for advertising metrics. The resulting “total audience” determination is simply the output of analysis and/or mathematical calculations applied to advertising data. Under Electric Power Group, LLC v. Alstom, such data collection, analysis, and display operations remain abstract regardless of the specific field of application. The claims remain ineligible under 35 U.S.C. 101, and therefore the rejection is maintained.
The Applicant argues on pages 8-9 that “Amended claim 1 reflects this disclosed improvement. For instance, amended claim 1 relates to these concepts and recites, among other things (i) training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign, (ii) predicting, using the machine learning model, estimated duplication factors for a query media campaign, (iii) selecting a reference media campaign based on a comparison of the estimated duplication factors with reference duplication factors for the reference media campaign, and (iv) determining, by a max entropy engine utilizing a numeric solver, final duplication factors for the query media campaign. Thus, the claimed invention provides an improvement to a technical field, namely that of digital media campaign measurement technology, and provides a particular solution to a problem arising in that technical field.”
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that with respect to the argument the claims provide an improvement to digital media campaign measurement technology is not persuasive because this alleged improvement addresses a business analytics challenge, not a technological problem with computer functionality. The Federal Circuit has distinguished between improvements to technology itself versus improvements to abstract ideas applied in a particular field. See Two-Way Media Ltd. V. Comcast Cable Comm’ns, (applying abstract idea to particular technological environment or field of use does not render claims patent-eligible); Customedia Techs., LLC v. Dish Network Corp (improvements to business practices, even if implemented with technology, remain abstract).
The recited steps – training a machine learning model, predicting values, selecting reference through comparisons, and determining results through mathematical optimization – are conventional data processing operations applied to advertising analytics data. The claims do not recite any specific technical mechanism or improvement to how computer function, how machine learning models operate more efficiently, or how data is processed or stored. Instead, they apply generic mathematical and data processing techniques to solve a business problem: determining deduplicated audience reach across media platforms. As the Federal Circuit held in ChargePoint, improving an abstract idea through application to a specific field does not constitute a technological improvement sufficient for patent eligibility. The claims therefore remain ineligible under 35 U.S.C. 101, the rejection is therefore maintained.
Applicant's arguments filed 25 August 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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, 3, 4, 7, 8, 10, 11, 14, 15, 17, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1, 3, 4, 7, 8, 10, 11, 14, 15, 17, 18, and 20 is/are directed to the abstract idea of audience measurement and mapping of campaigns. 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 than the judicial exception itself. Claim(s) (1, 3, 4, 7, 8, 10, 11, 14, 15, 17, 18, and 20) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1, 3, 4, and 7) is/are directed to a system, claim(s) (8, 10, 11, and 14) is/ are directed to a non-transitory computer readable medium, and claims(s) (15, 17, 18, and 20) is/are directed to a method and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process. Specifically, the independent claims 1, 8, and 15 recite a mental process: as drafted, the claim recites the limitation of comparing at least one of the collected traffic data to a predefined threshold which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor and memory nothing in the claim precludes the training, predicting and determining steps from practically being performed in the human mind. For example, but for a processor and memory language, the claim encompasses the user manually analyzing data, then training, predicting and determining audience measurements related to a campaign. The mere nominal recitation of a generic processor and memory does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images. However with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally that data selecting steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity
The claim recites the additional element(s): that a processor is used to perform the training, predicting and determining steps. The processor in the steps are recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (audience measurement and mapping of campaigns). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): selecting a reference media campaign performs the training, predicting and determining steps. The selecting step is recited at a high level of generality (i.e., as a general means of selecting data for use in the training, predicting and determining steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The processor and memory that performs the training, predicting and determining steps are also recited at a high level of generality, and merely automates the training, predicting and determining steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor and memory).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the October 2019 Guidance: Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1, 3, 4, 7, 8, 10, 11, 14, 15, 17, 18, and 20 recite(s) training, predicting and determining estimated duplication factors, selecting a reference media, training to predict duplication, predicting estimated duplication factors and determining final duplication factors which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for training, predicting, determining and selecting which is the abstract idea steps of valuing an idea (audience measurement and mapping of campaigns) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to calculate the audience measurement and mapping of campaigns). Using a computer to training, predicting, determining, and selecting the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The calculation of the audience measurement and mapping of campaigns would clearly be to a mental activity that a company would go through in order to map audience measurement. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to determine campaign mapping:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
No Claims recite limitations which further limit the claimed analysis of data.
Claims 3, 10, and 17 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Srivastava which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 4, 7, 11, 14, 18, and 20 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the campaign mapping for total audience measurement. This is not a technical or technological problem but is rather in the realm of campaign analysis and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when training, predicting and determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the October 2019 Guidance and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the January 2019 Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through 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.
Claim 1, 3, 4, 10, 11, 15, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (U.S. Patent Publication 2015/0186403 A1) (hereafter Srivastava) in view of Opdycke et al. (U.S. Patent Publication 2008/0306804 A1) (hereafter Opdyke) in further view of DIRAC et al. (U.S. Patent Publication 2015/0379430 A1) (hereafter Dirac) in further view of Sheppard et al. (U.S. Patent Publication 2017/0061470 A1) (hereafter Sheppard).
Referring to Claim 1, A computing system comprising a processor and a memory, the computing system configured to perform a set of acts comprising.
selecting a reference media campaign based on a comparison of the estimated duplication factors with reference duplication factors for the reference media campaign (see; par. [0038] of Srivastava teaches reference media using, par. [0175]-[0177] for example a comparison estimated duplication factor and comparing to other duplication factors [0185] and makes de-duplication impressions to provide a media campaign)).
determining final duplication factors for the query media campaign based on (a) a distance between final duplication factors and the reference duplication factors and (b) consistency between i) the total exposure metrics associated with the respective media platforms for the query media campaign and ii) unique audiences for the different possible combinations of the media platforms derived from the final duplication factors (see; par. [0182] of Srivastava teaches determining as part of a duplication estimator and finding an adjustment factor using examples of duplication factors, par. [0103] using a gross rating point as a way to measure consistency between campaigns, par. [0046] including identifying unique audiences and comparing it to total audiences (i.e. final duplication factors)).
Srivastava does not explicitly disclose the following limitations, however,
Opdycke teaches training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign, (see; par. [0027] of Opdycke teaches training a machine learning model to detect duplication of media content, par. [0063] understanding long term impact on campaigns and utilizing production modeling by gathering and enough data to understand behavior based on exposure data (metrics)), and
wherein the machine learning model is trained using training data comprising a plurality of media campaigns having known total exposure metrics for the respective media platforms and known duplication factors (see; Abstract of Opdycke teaches based on media exposures, par. [0027] of Opdycke teaches training a machine learning model to detect duplication of media content, par. [0043] where the analysis is based on multiple exposure measurements, par. [0027] and [0038]-[0039] using machine learning).
The Examiner notes that Srivastava teaches similar to the instant application teaches method and apparatus to de-duplicate impression information on media campaigns. Specifically, Srivastava discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions in order to understand audience measurement it is therefore viewed as analogous art in the same field of endeavor. Additionally, Opdycke teaches scheduling marketing campaigns in public places in order to enable measurement and optimization of audience response and as it is comparable in certain respects to Srivastava which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Srivastava discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions. However, Srivastava fails to disclose training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign, and wherein the machine learning model is trained using training data comprising a plurality of media campaigns having known total exposure metrics for the respective media platforms and known duplication factors.
Opdycke discloses training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign, and wherein the machine learning model is trained using training data comprising a plurality of media campaigns having known total exposure metrics for the respective media platforms and known duplication factors.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Srivastava training a machine learning model to predict duplication factors for a media campaign based on total exposure metrics associated with respective media platforms for the media campaign, and wherein the machine learning model is trained using training data comprising a plurality of media campaigns having known total exposure metrics for the respective media platforms and known duplication factors as taught by Opdycke since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Srivastava, and Opdycke teach the collecting and analysis of data in order to mapping for total audience measurement and they do not contradict or diminish the other alone or when combined.
Srivastava in view of Opdycke does not explicitly disclose the following limitation, however,
Dirac teaches predicting, using the machine learning model, estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the respective media platforms for the query media campaign, wherein duplication of media exposure across different possible combinations of the media platforms is unknown for the query media campaign (see; par. [0345] of Dirac teaches machine learning that takes into account a duplication factor and is utilized in training of the observations (i.e. exposure) from, par. [0120] variables are not known (i.e. platforms not known)).
The Examiner notes that Srivastava teaches similar to the instant application teaches method and apparatus to de-duplicate impression information on media campaigns. Specifically, Srivastava discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions in order to understand audience measurement it is therefore viewed as analogous art in the same field of endeavor. Additionally, Opdycke teaches scheduling marketing campaigns in public places in order to enable measurement and optimization of audience response and as it is comparable in certain respects to Srivastava which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Dirac teaches efficient duplicate detection for machine learning data sets and as it is comparable in certain respects to Srivastava and Opdycke which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Srivastava and Opdycke discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions. However, Srivastava and Opdycke fails to disclose predicting, using the machine learning model, estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the respective media platforms for the query media campaign, wherein duplication of media exposure across different possible combinations of the media platforms is unknown for the query media campaign.
Dirac discloses predicting, using the machine learning model, estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the respective media platforms for the query media campaign, wherein duplication of media exposure across different possible combinations of the media platforms is unknown for the query media campaign.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Srivastava and Opdycke predicting, using the machine learning model, estimated duplication factors for a query media campaign based on total exposure metrics associated with individual ones of the respective media platforms for the query media campaign, wherein duplication of media exposure across different possible combinations of the media platforms is unknown for the query media campaign as taught by Dirac since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Srivastava, Opdycke, and Dirac teach the collecting and analysis of data in order to mapping for total audience measurement and they do not contradict or diminish the other alone or when combined.
Srivastava in view of Opdycke in further view of Dirac does not explicitly disclose the following limitations, however,
Sheppard teaches a max entropy engine utilizing a numeric solver (see; par. [0011] of Sheppard teaches markets analysis for understanding reach, par. [0020] using max entropy engine).
The Examiner notes that Srivastava teaches similar to the instant application teaches method and apparatus to de-duplicate impression information on media campaigns. Specifically, Srivastava discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions in order to understand audience measurement it is therefore viewed as analogous art in the same field of endeavor. Additionally, Opdycke teaches scheduling marketing campaigns in public places in order to enable measurement and optimization of audience response and as it is comparable in certain respects to Srivastava which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Dirac teaches efficient duplicate detection for machine learning data sets and as it is comparable in certain respects to Srivastava and Opdycke which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Sheppard improving the reach calculation efficiency for the distribution and distribution of maximum entropy distribution and as it is comparable in certain respects to Srivastava, Opdycke, and Dirac which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Srivastava, Opdycke, and Dirac discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions. However, Srivastava and Opdycke fails to disclose a max entropy engine utilizing a numeric solver.
Sheppard discloses a max entropy engine utilizing a numeric solver.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Srivastava, Opdycke, Sheppard a max entropy engine utilizing a numeric solver as taught by Dirac since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Srivastava, Opdycke, Dirac, and Sheppard teach the collecting and analysis of data in order to mapping for total audience measurement and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3 see discussion of claim 1 above, while Srivastavat in view of Opdycke in further view of Dirac in further view of Sheppard teaches the system above, Srivastava further discloses a system having the limitations of:
the machine learning model is trained using respective features of media campaigns of the plurality of media campaigns (see; par. [0080] of Srivastava teaches a machine learning generate based on evolving empirical data (i.e. training) using campaigns) to create campaigns).
Referring to Claim 4 see discussion of claim 3 above, while Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches the system above, Srivastava further discloses a system having the limitations of:
the features include at least one of demographics, a media campaign time step, a media platform reach, or a digital duplicated reach, the digital duplicated reach determined based on a combination of desktop reach and mobile reach (see; Abstract of Srivastava teaches duplicate impressions taking into account, par. [0032] demographics, par. [0042] reach, including par. [0177] analyzing duplicate reach, par. [0059] on desktop and mobile).
Referring to Claim 8, Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches a non-transitory computer readable medium. Claim 8 recites the same or similar limitations as those addressed above in claim 1, Claim 8 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 10, see discussion of claim 8 above, while Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches the non-transitory computer readable medium above Claim 10 recites the same or similar limitations as those addressed above in claim 3, Claim 10 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 11, see discussion of claim 10 above, while Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard in further view of Sheppard teaches the non-transitory computer readable medium above Claim 11 recites the same or similar limitations as those addressed above in claim 4, Claim 11 is therefore rejected for the same or similar limitations as set forth above in claim 4.
Referring to Claim 15, Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches a method. Claim 15 recites the same or similar limitations as those addressed above in claim 1, Claim 15 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 17, see discussion of claim 15 above, while Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches the non-transitory computer readable medium above Claim 17 recites the same or similar limitations as those addressed above in claim 3, Claim 17 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 18, see discussion of claim 17 above, while Srivastava in view of Opdycke in further view of Dirac in further view of Sheppard teaches the non-transitory computer readable medium above Claim 18 recites the same or similar limitations as those addressed above in claim 4, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 4.
Claim 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al. (U.S. Patent Publication 2015/0186403 A1) (hereafter Srivastava) in view of Opdycke et al. (U.S. Patent Publication 2008/0306804 A1) (hereafter Opdyke) in further view of DIRAC et al. (U.S. Patent Publication 2015/0379430 A1) (hereafter Dirac) in further view of Sheppard et al. (U.S. Patent Publication 2017/0061470 A1) (hereafter Sheppard) in further view of Ray et al. (U.S. Patent Publication 2017/0034592 A1) (hereafter Ray).
Referring to Claim 7 see discussion of claim 1 above, while Srivastava in view of Opdyke in further view of Dirac in further view of Sheppard teaches the system above, Srivastava in view of Opdyke in further view of Dirac in further view of Sheppard does not explicitly disclose a system having the limitations of, however,
Ray teaches the distance is a Euclidean distance based on a KD tree (see; par. [0106] of Ray teaches an ad campaign being measured by Euclidean distance).
The Examiner notes that Srivastava teaches similar to the instant application teaches method and apparatus to de-duplicate impression information on media campaigns. Specifically, Srivastava discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions in order to understand audience measurement it is therefore viewed as analogous art in the same field of endeavor. Additionally, Opdycke teaches scheduling marketing campaigns in public places in order to enable measurement and optimization of audience response and as it is comparable in certain respects to Srivastava which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Dirac teaches efficient duplicate detection for machine learning data sets and as it is comparable in certain respects to Srivastava and Opdycke which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Sheppard improving the reach calculation efficiency for the distribution and distribution of maximum entropy distribution and as it is comparable in certain respects to Srivastava, Opdycke, and Dirac which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ray teaches sequential delivery of advertising content across media devices based on deduplication to understand audience measurement using machine learning and as it is comparable in certain respects to Srivastava, Opdycke, Dirac, and Sheppard which method and apparatus to de-duplicate impression information on media campaigns as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Srivastava, Opdycke, Dirac, and Sheppard discloses the de-duplicate media impression information using machine learning which includes determining an overlap based on impressions. However, Srivastava, Opdycke, Dirac, and Sheppard fails to disclose the distance is a Euclidean distance based on a KD tree.
Ray discloses the distance is a Euclidean distance based on a KD tree.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Srivastava, Opdycke, Dirac, and Sheppard the distance is a Euclidean distance based on a KD tree as taught by Ray since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Srivastava, Opdycke, Dirac, Sheppard, and Ray teach the collecting and analysis of data in order to mapping for total audience measurement and they do not contradict or diminish the other alone or when combined.
Referring to Claim 14, see discussion of claim 8 above, while Srivastava in view of Opdycke in further view of Sheppard in further view of Ray teaches the non-transitory computer readable medium above Claim 14 recites the same or similar limitations as those addressed above in claim 7, Claim 14 is therefore rejected for the same or similar limitations as set forth above in claim 7.
Referring to Claim 20, see discussion of claim 15 above, while Srivastava in view of Opdycke in further view of Sheppard in further view of Ray teaches the non-transitory computer readable medium above Claim 20 recites the same or similar limitations as those addressed above in claim 7, Claim 20 is therefore rejected for the same or similar limitations as set f