DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to amendment filed on 6 May 2026. Claims 1, 8, and 15 have been amended. Claims 1-20 are currently pending 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6 May 2026Fig. has been entered.
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.
Step 1: The claims 1-7 are a method, claims 8-14 are a system and claims 15-20 are a media. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: the claims recites the limitation of determining, response rate matrix Rij for each synthetic company I of a set of synthetic companies and each synthetic rotation j of a set of synthetic rotations, wherein each element of the response rate matrix Rij is an average response rate of synthetic customers of a respective synthetic rotation in the set of synthetic rotations to linear television (TV) spots from a respective synthetic company in the set of synthetic companies; determining, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation by matching a measured average response rate of a real customer to a mean synthetic response rate of a synthetic customer; and generating, , the synthetic data universe, the generating comprising: building a unique-visitor-per-minute UV(t) dataset over a time period for each synthetic customer, the building comprising: determining a baseline rate modeled after the respective actual-customer analog’s measured baseline in the time period; randomly generating, using the baseline rate, a simulated baseline representing a number of unique visitors (UVs) visiting a website on a minute-by-minute basis without any TV spot airing; determining (TV) spots for each synthetic customer over the time period; and for each TV spot of the TV spots, determining lift-visitors as compared to the simulated baseline, representing an increase of UVs to the website following a number of synthetic customers viewing each TV spot, the lift-visitors representing a simulated ground truth verifiable to be true in the synthetic data universe, wherein the synthetic data universe is provided as an input to a performance measurement algorithm to compare a measured output from the performance measurement algorithm with the simulated ground truth for identifying an inaccuracy in the measurement algorithm without human intervention. These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers a performance on generated a synthetic data universe to evaluate performance measurement algorithms using generic mathematical calculations (e.g., matrices, averaging, and random number generation). The claim uses mathematical and statistical concepts to organize and manipulate information. Mathematical correlations (such as response rate matrices and calculated lift visitors) and "mental processes" (simulating a "ground truth" or comparing synthetic models) by a computer. That is, other than reciting “by a computer ,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a computer ” language, the claim encompasses rotating of ads from particular company . The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mathematical concept.
Dependent claims 2-7, 9-14, and 16-20, merely provide additional abstract concepts and narrow the abstract idea of claim 1, 8 and 15. Further, claims 1-20, 22 and 23 are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content and authorize compensation.
Step 2A-Prong 2: The claim recites one additional element: that a processor is used to perform both the determining and generating steps. The processor in both steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (the amount of use of each ad rotation) based on the determined amount of use). 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.
Step 2B: 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. The claim is ineligible
The closest prior arts to the applicants’ claimed invention:
Singh et al. (US Pub., No., 2023/0222178 Al) focused on method and system for synthetic data generation are provided the archive a schema configuration file in a synthetic data set request from a client application, create a set of worker processes to generate the synthetic data set based on the schema configuration file, upload the generate synthetic data to an analytics platform, and enable the client application to utilize the generated synthetic data in prediction models for the analytics platform, and enable the client application to utilize the generated synthetic data in prediction models for the analytics platform (abstract), teaches a method for generating a synthetic data universe, (paragraphs [0016]- [0018], discloses synthetic data generation system ) the method comprising:
Frisardi et al. (US Pub. No.: US 2023/0016021 Al) focused on methods, apparatus, systems and articles of manufacture to determine synthetic total audience ratings are disclosed. Disclosed example apparatus are to access census data including census viewing statements associated with media content presented by census devices, access panel data including panelist viewing statements associated with media content presented by panel devices, the panel data including weights to represent numbers of individuals to be represented by corresponding panelists, assign the census devices to the panel devices based on the weights, divide the weights for respective ones of the panelists into respective sets of split weights, and assign the census viewing statements to at least subsets of the sets of split weights to determine audience ratings for a population (abstract), determining, by a computer operating on an analytics platform in a networked computing environment, response rate matrix (paragraph [0031], discloses deteriming synthetic total audience rating .., audience measurement entities (AMEs) seek to understand the composition and size of audience of media, such as television programming.., paragraph [0034], discloses measure a total audience across media technologies .., ) Rij for each synthetic company I of a set of synthetic companies (Fig. 18, discloses broadcasters DR1, TV2 LIVE, DR2 [synthetic companies] and paragraph [0052], discloses performing viewing activity .., the universe of active user is estimated and panelists are assigned an active weight, measuring their ability.. )
Chen et al (US Pub., No., 2023/0117593 A1) focused on a self-consistent inception architecture includes a process that integrates online data and offline data to determine an estimated lift ("prior lift") in the number of unique visitors (UVs) to a website caused by a television (TV) spot airing on an offline medium. The prior lift is used to adjust a UV profile of the website. A baseline thus produced is fitted through an inception process in which a locally weighted scatterplot smoothing algorithm is applied iteratively until a final baseline converges. The baseline from the inception process is used to determine a calculated lift. If the prior lift and the calculated lift are not consistent (e.g., within a threshold), the process is run iteratively until the prior lift and the calculated lift are consistent. The calculated lifts can be used to determine and visualize performance metric(s) relating to media creatives such as TV spots airing in the physical world (abstract), generating, by the computer, the synthetic data universe, the generating(paragraph [0031], dislcies data aggregated and/or provided by online and offline data source ) comprising: building a unique-visitor-per-minute UV(t) dataset over a time period for each synthetic customer (Fig. 3A, Fig. 7 and paragraph [0020], discloses a plot diagram sowing raw unique vastier (UV) data with period VU spike occurring at a website over time) , the building comprising: determining a baseline rate modeled after the respective actual-customer analog’s measured baseline in the time period(Figs. 6A-E, Fig. 7, and paragraph [0064], discloses using moving average baselining approach .., different baselines determined utilizing different baseline approach ..); determining (TV) spots for each synthetic customer over the time period(Fig. 8, paragraph [0026], dislcies UV lifts computed for each stops based on the different baselining approach and paragraph [0063], dislcies TV spot campaign by determining which network base to airing their TV spots ); and for each TV spot of the TV spots, determining lift-visitors as compared to the simulated baseline, representing an increase of UVs to the website following a number of synthetic customers viewing each TV spot, the lift-visitors representing a simulated ground truth verifiable to be true in the synthetic data universe(Fig. 8, paragraph [0026], paragraphs [0061], discloses UV curve 710 UV traffic to a website has visible traffic spike that corresponding to TV spots airing.., and paragraph [0055], and Table 2, dislcies lift is distributed to the N (e.g., 5) minutes with the airing time. The distributed lift is subtracted from the VU to get an adjusted UV as shown in Table 2 .., paragraph [0059], discloses calculated spot lift is set as prior lift and .., the calculated spot lift is within a threshold of the prior lift ).
Ganapathi et al. (US Pub. No.: US 2019/0140910 Al) focused on a data driven approach to generating synthetic data matrices is presented. By retrieving historical network traffic data, probabilistic models are generated. Optimal distribution families for a set of independent data segments are determined. Applications are tested and performance metrics are determined based on the generated synthetic data matrices(abstract), synthetic data metric generator and an application performance evaluator according to an embodiment of the invention (Fig. 1 paragraph [0008]).
WALTERS et al. (Pub. No.: US 2021/0397972 Al) focused systems and methods for classifying data are disclosed. For example, a system may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving training data comprising a class. The operations may include training a data classification model using the training data to generate a trained data classification model. The operations may include receiving additional data comprising labeled samples of an additional class not contained in the training data. The operations may include creating a synthetic data generator (abstract), generate synthetic data using class-specific model (Fig. 5A) and create synthetic data having statistical characteristics similar to those of the original data, limiting the utility of such data for training and testing purposes (paragraph [0004]).
Hazard et al. (US Pub. No.: US 2021/0312307 Al) focused on Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified ( e.g., resampled), removed, and/or replaced(abstract).
Ebstyne et al (US Pub., No., 2019/0347547 A1) discloses an immersive feedback loop is disclosed for improving artificial intelligence (AI) applications used for virtual reality (VR) environments. Users may iteratively generate synthetic scene training data, train a neural network on the synthetic scene training data, generate synthetic scene evaluation data for an immersive VR experience, indicate additional training data needed to correct neural network errors indicated in the VR experience, and then generate and retrain on the additional training data, until the neural network reaches an acceptable performance level.
None of the above reference either alone or in combination teaches or suggest for each synthetic company I and each synthetic rotation j of a set of synthetic rotations , wherein each element of the response rate matrix Rij is an average response rate of synthetic customers of a respective synthetic rotation in the set of synthetic rotations to linear television (TV) spots from a respective synthetic company in the set of synthetic companies; determining, by the computer, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation by matching a measured average response rate of a real customer to a mean synthetic response rate of a synthetic customer; randomly generating, using the baseline rate, a simulated baseline representing a number of unique visitors (UVs) visiting a website on a minute-by-minute basis without any TV spot airing; and wherein the synthetic data universe is provided as an input to a performance measurement algorithm to compare a measured output from the performance measurement algorithm with the simulated ground truth for identifying an inaccuracy in the measurement algorithm without human intervention.
Response to Arguments
Applicant's arguments filed 35 U.S.C 101 rejection filed on 6 May 2026 with respect to claims 1-20 have been fully considered but they are not persuasive. Applicants’ arguments of the claims have been amended as a good faith effort to expedite the prosecution and without conceding the Examiner’s positions is not persuasive. The claim relies on generating a "synthetic data universe" using statistical matrices (Rij), calculating averages, finding analogs by matching statistical means, and "randomly generating... a simulated baseline." These limitations fails into mathematical formulas and data manipulation techniques that can be performed in the human mind or with a pencil and paper. Furthermore, modeling customer behavior, response rates to television spots, and website visitor traffic constitutes tracking and predicting consumer commercial activity.
Generic Computer Implementation: The claim recites "generating... using [a] computer " "determining, by the computer," and "randomly generating." Under Alice, simply invoking a generic computer to automate mathematical steps or execute simulations faster than a human does not transform an abstract idea into patent-eligible subject matter.
Conventional Activity: Generating synthetic data, running simulations (like Monte Carlo or baseline modeling), and using a simulated ground truth to test an algorithm's accuracy are standard, conventional practices in data science, software testing, and analytics.
The "Without Human Intervention" Limitation: While the claim specifies identifying an inaccuracy "without human intervention," automation of an abstract process by a computer is generally insufficient to confer eligibility unless it improves the underlying technological functioning of the computer itself.
Therefore, the 35 U.S.C 101 rejection to claims 1-20 is maintained.
Conclusion
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/SABA DAGNEW/Primary Examiner, Art Unit 3621
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