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 30 September 2025. Claims 1, 8, and 15 have been amended. Claims 1-20 are currently pending and have been examined.
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, a set of synthetic companies, a set of synthetic rotations, and a respective response rate matrix for each of the set of synthetic companies and the set of synthetic rotations; and determining, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation and generating, by the computer, a synthetic data universe, the generating comprising building a unique-visitor-per-minute UV(t) dataset over a time period for each synthetic customer, using UV(t) timeseries of the respective actual-customer analog and a list of linear television (TV) spots and associated data run by the respective actual-customer analog in a particular date range. The determining limitation as drafted, is a process that, under its broadest reasonable interpretation, covers a performance of a response behavior of a population of viewers of a particular TV network or ration to media creative from particular company, which fails within certain method of organizing human activity, but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the
claim element precludes the step from practically being performed in the mind. For example, but
for the “by a processor” 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 mental process.
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
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (US Pub., No., 2022/0237653 A1) in view of Ebstyne et al (US Pub., No., 2019/0347547 A1)
With respect to claim 1, Chen teaches a method for generating synthetic data (Fig. 3, 301 discloses create response profile (synthetic data]) the method comprising:
determining, by a computer operating on an analytics platform in a networked computing environment(paragraph [0037], discloses a media performance analytics service provider ), a set of synthetic companies, a set of synthetic rotations, and a respective response rate matrix for each of the set of synthetic companies and the set of synthetic rotations (abstract, discloses determine a response profile withing an attribution time window, Fig. 2, discloses spot airing data provider 210a…210n [synthetic companies], paragraph [0038], discloses the media performance analytics service provider may purchase spots from TV networks…, sport airing data providers 210a…210n and paragraph [0040], discloses media creative performance analyzer 280 is configured for determining media creative attribution and corresponding performance metric(s) ); and
determining, by the computer, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation(paragraphs [0006], discloses audience response to TV programs , paragraph [0012], discloses determining a response profile on minute-by-minute basis within an attribution time window.., the response profile refers to a portion of a unique visitor (UV) cure assocted with the website and paragraph [0049], discloses a response profile can be determined by correlation th number of UVs tracked at the website with the airtime of media creative ..) by matching a measured average response rate of a real customer to a mean synthetic response rate of the synthetic customer (paragraph [0009], discloses evaluating the performance of a TV commercial is to define the efficient of the TV commercial as Response per amount spent ..).
Chen teaches the above elements including the generating comprising building a unique-visitor-per minute UV(t) dataset over a time period for each synthetic customer, using UV(t) timeseries of the respective actual-customer analog and a list of linear television (TV) spots and associated data run by the respective actual-customer analog in a particular date range (Figs. 3-7, paragraphs [0047]-[0049], discloses creating a response performance on a minute by-minute bases withing an attribution time window (e.g., five minutes)). Chen failed to teach improving a computer-based measurement algorithm by generating synthetic data may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth ; may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth; and , wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm.
However, Ebstyne teaches improving a computer-based measurement algorithm by generating synthetic data (Fig. 10, 1002, discloses generate synthetic scene training data, 1006, discloses generate synthetic scene evaluation data, paragraph [0004], discloses using an immersive feedback loop for improving artificial intelligence (AI)…, synthetic scene generator for may be configured to generate synthetic scene data comprising a first set of synthetic sensor data and a first ground truth data and paragraph [0020], discloses generating training data and permit evaluation of the training, to identify which objects may require futher training efforts, can facilitate an immersive feedback loop for improving AI application), generating, by the computer, a synthetic data universe comprising a simulated ground truth value (paragraph [0004], discloses a model evaluator identifies objects in the second set of synthetic sensor data by using the neural network, and compares the identified object), and wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm(abstract, paragraph [0004], dislcies generate synthetic scene evolution data for an immersive VR experience, indicate additional training data needed to correct neural network errors and paragraph [0005], discloses based on comparison, errors may visibly be identified within the VR environment by the user and feedback on the error submitted directly through the VR .., the feedback may be used to indicate training data corresponding to the indicted errors ). Therefore, it would have been obvious to the one ordinary skill in the art before the ordinary in the art before the effective filing date of the claimed invention for the generated performance metrics relating to the individual media creative that aired within the attribution time window data reside on a cloud-based server operating in cloud by one or more service machine operated of Chen with a feature of using artificial intelligence to be trained with the collected data , generate and retain to on additional training data of Ebstyne in order to correct errors and improve the artificial intelligence application (see paragraph [0004]).
With respect to claim 2, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the method wherein the synthetic data universe comprises, for each synthetic customer:
a list of synthetic unique visitors per minute, with a number of baseline visitors and visitors due to known lift(paragraphs [0013] & [0055], discloses obtain a lift per minute , the system then aggregates the lift per minute within the attribution time window) ; and
a list of synthetic spots, with associated airing time, impressions, spend, response rate, creative, immediate lift, and lift (Fig. 7 and paragraph [0058], discloses media creatives (Adl and Ad2) occurred within time window 750, during which UV spike 710 occurred. Referring to FIGS. 8A and 8B, media creative Adl (spot 1) was aired at a first time (13:47) and media creative Ad2 (spot 2) was aired at a second time (13:50) shortly after the first time. To compute
the lift attributable per creative for a specific minute ( e.g., at a third time, 13:51), the system is operable to determine that, of the 15 UV s associated with that time, 7 .2 are counted)
With respect to claim 3, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the method wherein determining the response rate matrix comprises determining a target match fraction, an in-market fraction, and a quality factor based on a response model that models a response behavior of a population of viewers of a particular TV network or rotation to media creatives from a particular company (Fig. 9 , paragraph [0070], discloses determine a weighting function utilizing the audience size times anticipated response rate.., and paragraph [0082], discloses determining response profile (within an attribution time window) and runs .., the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocation, performance metrics etc.).
With respect to claim 4, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the method wherein the synthetic data universe comprises a set of UV(t) tables for each synthetic customer over the particular date range, and a set of ad spots per company over the particular date range, each with a ground truth number of lift visitors(Fig. 8A-B, and paragraphs [0081]- [0082], discloses together show how lift is attributable to two sport that aired .., determines that response profile (within an attribution time window) and runs ) .
With respect to claim 5, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the method further comprising:
applying a measurement algorithm to the synthetic data universe or a portion thereof so as to produce a performance measurement of a media creative(paragraph [0006], discloses Nielson’s audience measurement systems can provide some quantified measures of audience response to TV programs, the Nielson television rating , paragraph [0010], discloses measure the effectiveness of a TV spot ( or, the efficiency of an ad spend) in the physical world by the number of unique visitors (UVs) to its website in the online world and paragraph [0042], discloses an important metric in measuring the performance of a media creative (e.g., attribution to the number of UVs at the website)); and
comparing the performance measurement with a simulated ground truth value from the synthetic data universe so as to generate a result(paragraph [0081], discloses the final computed lift values that will be allocated to each of the spots based on the overall lift for each minute (minutely lift) and paragraph [0082], discloses the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocations, performance metrics etc.) ).
With respect to claim 6, Chen in view of Ebstyne teaches elements of claim 5, furthermore, Chen teaches the method further comprising: tuning the measurement algorithm based on the result so as to improve the measurement algorithm(paragraph [0040], discloses a lift can be an important metric in measuring the performance of a media creative (e.g., its attribution to the number of UVs at the website), it can be important to accurately attribute the lift to the correct TV spot)
With respect to claim 7, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the method further comprising:
generating a media plan for each synthetic customer using a media plan of the respective actual-customer analog such that the media plan for each synthetic customer contains spots with realistic spend patterns and distribution of airing times that mimic the media plan of the respective actual-customer analog(abstract, discloses generate performance metric relating to the individual media creative that aired within the attribution time window and paragraph [0040], discloses the media creative performance metric can be utilized for presentation on e.g., on client deices 230a.., 230n through a user interface (UI) generated by visualizer 270.., generating various visualizations particularly useful for decision making purpose).
With respect to claim 8, Chen teaches a system for generating synthetic data (Fig. 3, 301 discloses create response profile [synthetic data]), the system comprising:
a processor(paragraph [0019], discloses a system having a processor);
a non-transitory computer-readable medium(paragraph [0019], discloses non-transitory computer-readable storage medium); and
instructions stored on the non-transitory computer-readable medium and translatable by the processor (paragraph [0019], discloses a non-transitory computer-readable storage medium which stores computer instructions that are executable by a processor to perform the SCI process..) for:
determining, by a computer operating on an analytics platform in a networked computing environment(paragraph [0037], discloses a media performance analytics service provider ), a set of synthetic companies, a set of synthetic rotations, and a respective response rate matrix for each of the set of synthetic companies and the set of synthetic rotations (abstract, discloses determine a response profile withing an attribution time window, Fig. 2, discloses spot airing data provider 210a…210n [synthetic companies], paragraph [0038], discloses the media performance analytics service provider may purchase spots from TV networks…, sport airing data providers 210a…210n and paragraph [0040], discloses media creative performance analyzer 280 is configured for determining media creative attribution and corresponding performance metric(s) ); and
determining, by the computer, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation(paragraphs [0006], discloses audience response to TV programs , paragraph [0012], discloses determining a response profile on minute-by-minute basis within an attribution time window.., the response profile refers to a portion of a unique visitor (UV) cure assocted with the website and paragraph [0049], discloses a response profile can be determined by correlation th number of UVs tracked at the website with the airtime of media creative ..) by matching a measured average response rate of a real customer to a mean synthetic response rate of the synthetic customer (paragraph [0009], discloses evaluating the performance of a TV commercial is to define the efficient of the TV commercial as Response per amount spent ..).
Chen teaches the above elements including the generating comprising building a unique-visitor-per minute UV(t) dataset over a time period for each synthetic customer, using UV(t) timeseries of the respective actual-customer analog and a list of linear television (TV) spots and associated data run by the respective actual-customer analog in a particular date range (Figs. 3-7, paragraphs [0047]-[0049], discloses creating a response performance on a minute by-minute bases withing an attribution time window (e.g., five minutes)). Chen failed to teach improving a computer-based measurement algorithm by generating synthetic data may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth ; may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth; and , wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm.
However, Ebstyne teaches improving a computer-based measurement algorithm by generating synthetic data (Fig. 10, 1002, discloses generate synthetic scene training data, 1006, discloses generate synthetic scene evaluation data, paragraph [0004], discloses using an immersive feedback loop for improving artificial intelligence (AI)…, synthetic scene generator for may be configured to generate synthetic scene data comprising a first set of synthetic sensor data and a first ground truth data and paragraph [0020], discloses generating training data and permit evaluation of the training, to identify which objects may require futher training efforts, can facilitate an immersive feedback loop for improving AI application), generating, by the computer, a synthetic data universe comprising a simulated ground truth value (paragraph [0004], discloses a model evaluator identifies objects in the second set of synthetic sensor data by using the neural network, and compares the identified object), and wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm(abstract, paragraph [0004], dislcies generate synthetic scene evolution data for an immersive VR experience, indicate additional training data needed to correct neural network errors and paragraph [0005], discloses based on comparison, errors may visibly be identified within the VR environment by the user and feedback on the error submitted directly through the VR .., the feedback may be used to indicate training data corresponding to the indicted errors ). Therefore, it would have been obvious to the one ordinary skill in the art before the ordinary in the art before the effective filing date of the claimed invention for the generated performance metrics relating to the individual media creative that aired within the attribution time window data reside on a cloud-based server operating in cloud by one or more service machine operated of Chen with a feature of using artificial intelligence to be trained with the collected data , generate and retain to on additional training data of Ebstyne in order to correct errors and improve the artificial intelligence application (see paragraph [0004]).
With respect to claim 9, Chen in view of Ebstyne teaches elements of claim 8, furthermore, Chen teaches the system wherein the synthetic data universe comprises, for each synthetic customer:
a list of synthetic unique visitors per minute, with a number of baseline visitors and visitors due to known lift(paragraphs [0013] & [0055], discloses obtain a lift per minute , the system then aggregates the lift per minute within the attribution time window) ; and
a list of synthetic spots, with associated airing time, impressions, spend, response rate, creative, immediate lift, and lift (Fig. 7 and paragraph [0058], discloses media creatives (Adl and Ad2) occurred within time window 750, during which UV spike 710 occurred. Referring to FIGS. 8A and 8B, media creative Adl (spot 1) was aired at a first time (13:47) and media creative Ad2 (spot 2) was aired at a second time (13:50) shortly after the first time. To compute
the lift attributable per creative for a specific minute ( e.g., at a third time, 13:51), the system is operable to determine that, of the 15 UV s associated with that time, 7 .2 are counted)
With respect to claim 10, Chen teaches elements of claim 8, furthermore, Chen teaches the system wherein determining the response rate matrix comprises determining a target match fraction, an in-market fraction, and a quality factor based on a response model that models a response behavior of a population of viewers of a particular TV network or rotation to media creatives from a particular company (Fig. 9 , paragraph [0070], discloses determine a weighting function utilizing the audience size times anticipated response rate.., and paragraph [0082], discloses determining response profile (within an attribution time window) and runs .., the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocation, performance metrics etc.).
With respect to claim 11, Chen in view of Ebstyne teaches elements of claim 8, furthermore, Chen teaches the system wherein the synthetic data universe comprises a set of UV(t) tables for each synthetic customer over the particular date range, and a set of ad spots per company over the particular date range, each with a ground truth number of lift visitors(Fig. 8A-B, and paragraphs [0081]- [0082], discloses together show how lift is attributable to two sport that aired .., determines that response profile (within an attribution time window) and runs ) .
With respect to claim 12, Chen in view of Ebstyne teaches elements of claim 8, furthermore, Chen teaches the system further comprising:
applying a measurement algorithm to the synthetic data universe or a portion thereof so as to produce a performance measurement of a media creative(paragraph [0006], discloses Nielson’s audience measurement systems can provide some quantified measures of audience response to TV programs, the Nielson television rating , paragraph [0010], discloses measure the effectiveness of a TV spot ( or, the efficiency of an ad spend) in the physical world by the number of unique visitors (UVs) to its website in the online world and paragraph [0042], discloses an important metric in measuring the performance of a media creative (e.g., attribution to the number of UVs at the website)); and
comparing the performance measurement with a simulated ground truth value from the synthetic data universe so as to generate a result(paragraph [0081], discloses the final computed lift values that will be allocated to each of the spots based on the overall lift for each minute (minutely lift) and paragraph [0082], discloses the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocations, performance metrics etc.) ).
With respect to claim 13, Chen in view of Ebstyne teaches elements of claim 12, furthermore, Chen teaches the system further comprising: tuning the measurement algorithm based on the result so as to improve the measurement algorithm(paragraph [0040], discloses a lift can be an important metric in measuring the performance of a media creative (e.g., its attribution to the number of UVs at the website), it can be important to accurately attribute the lift to the correct TV spot).
With respect to claim 14, Chen in view of Ebstyne teaches elements of claim 1, furthermore, Chen teaches the system further comprising:
generating a media plan for each synthetic customer using a media plan of the respective actual-customer analog such that the media plan for each synthetic customer contains spots with realistic spend patterns and distribution of airing times that mimic the media plan of the respective actual-customer analog(abstract, discloses generate performance metric relating to the individual media creative that aired within the attribution time window and paragraph [0040], discloses the media creative performance metric can be utilized for presentation on e.g., on client deices 230a.., 230n through a user interface (UI) generated by visualizer 270.., generating various visualizations particularly useful for decision making purpose).
With respect to claim 15, Chen teaches a computer program product for generating synthetic data(Fig. 3, 301 discloses create response profile [synthetic data]), the computer program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor(paragraph [0019], discloses a system having a processor and paragraph [0019], discloses a non-transitory computer-readable storage medium which stores computer instructions that are executable by a processor to perform the SCI process..) for:
determining, by a computer operating on an analytics platform in a networked computing environment(paragraph [0037], discloses a media performance analytics service provider ), a set of synthetic companies, a set of synthetic rotations, and a respective response rate matrix for each of the set of synthetic companies and the set of synthetic rotations (abstract, discloses determine a response profile withing an attribution time window, Fig. 2, discloses spot airing data provider 210a…210n [synthetic companies], paragraph [0038], discloses the media performance analytics service provider may purchase spots from TV networks…, sport airing data providers 210a…210n and paragraph [0040], discloses media creative performance analyzer 280 is configured for determining media creative attribution and corresponding performance metric(s) ); and
determining, by the computer, a respective actual-customer analog for each synthetic customer and a respective actual-rotation analog to each synthetic rotation(paragraphs [0006], discloses audience response to TV programs , paragraph [0012], discloses determining a response profile on minute-by-minute basis within an attribution time window.., the response profile refers to a portion of a unique visitor (UV) cure assocted with the website and paragraph [0049], discloses a response profile can be determined by correlation th number of UVs tracked at the website with the airtime of media creative ..) by matching a measured average response rate of a real customer to a mean synthetic response rate of the synthetic customer (paragraph [0009], discloses evaluating the performance of a TV commercial is to define the efficient of the TV commercial as Response per amount spent ..).
Chen teaches the above elements including the generating comprising building a unique-visitor-per minute UV(t) dataset over a time period for each synthetic customer, using UV(t) timeseries of the respective actual-customer analog and a list of linear television (TV) spots and associated data run by the respective actual-customer analog in a particular date range (Figs. 3-7, paragraphs [0047]-[0049], discloses creating a response performance on a minute by-minute bases withing an attribution time window (e.g., five minutes)). Chen failed to teach improving a computer-based measurement algorithm by generating synthetic data may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth ; may require futher training useful for quantitatively evaluating the measurement algorithm against a verifiable ground truth; and , wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm.
However, Ebstyne teaches improving a computer-based measurement algorithm by generating synthetic data (Fig. 10, 1002, discloses generate synthetic scene training data, 1006, discloses generate synthetic scene evaluation data, paragraph [0004], discloses using an immersive feedback loop for improving artificial intelligence (AI)…, synthetic scene generator for may be configured to generate synthetic scene data comprising a first set of synthetic sensor data and a first ground truth data and paragraph [0020], discloses generating training data and permit evaluation of the training, to identify which objects may require futher training efforts, can facilitate an immersive feedback loop for improving AI application), generating, by the computer, a synthetic data universe comprising a simulated ground truth value (paragraph [0004], discloses a model evaluator identifies objects in the second set of synthetic sensor data by using the neural network, and compares the identified object), and wherein the synthetic data universe is provided as an input to the measurement algorithm to compare a measured output from the algorithm with the simulated ground truth value to identify an inaccuracy in the measured algorithm(abstract, paragraph [0004], dislcies generate synthetic scene evolution data for an immersive VR experience, indicate additional training data needed to correct neural network errors and paragraph [0005], discloses based on comparison, errors may visibly be identified within the VR environment by the user and feedback on the error submitted directly through the VR .., the feedback may be used to indicate training data corresponding to the indicted errors ). Therefore, it would have been obvious to the one ordinary skill in the art before the ordinary in the art before the effective filing date of the claimed invention for the generated performance metrics relating to the individual media creative that aired within the attribution time window data reside on a cloud-based server operating in cloud by one or more service machine operated of Chen with a feature of using artificial intelligence to be trained with the collected data , generate and retain to on additional training data of Ebstyne in order to correct errors and improve the artificial intelligence application (see paragraph [0004]).
With respect to claim 16, Chen in view of Ebstyne teaches elements of claim 15, furthermore, Chen teaches the computer program product wherein the synthetic data universe comprises, for each synthetic customer:
a list of synthetic unique visitors per minute, with a number of baseline visitors and visitors due to known lift(paragraphs [0013] & [0055], discloses obtain a lift per minute , the system then aggregates the lift per minute within the attribution time window) ; and
a list of synthetic spots, with associated airing time, impressions, spend, response rate, creative, immediate lift, and lift (Fig. 7 and paragraph [0058], discloses media creatives (Adl and Ad2) occurred within time window 750, during which UV spike 710 occurred. Referring to FIGS. 8A and 8B, media creative Adl (spot 1) was aired at a first time (13:47) and media creative Ad2 (spot 2) was aired at a second time (13:50) shortly after the first time. To compute
the lift attributable per creative for a specific minute ( e.g., at a third time, 13:51), the system is operable to determine that, of the 15 UV s associated with that time, 7 .2 are counted)
With respect to claim 17, Chen in view of Ebstyne teaches elements of claim 15, furthermore, Chen teaches the computer program product wherein determining the response rate matrix comprises determining a target match fraction, an in-market fraction, and a quality factor based on a response model that models a response behavior of a population of viewers of a particular TV network or rotation to media creatives from a particular company (Fig. 9 , paragraph [0070], discloses determine a weighting function utilizing the audience size times anticipated response rate.., and paragraph [0082], discloses determining response profile (within an attribution time window) and runs .., the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocation, performance metrics etc.).
With respect to claim 18, Chen in view of Ebstyne teaches elements of claim 15, furthermore, Chen teaches the computer program product wherein the synthetic data universe comprises a set of UV(t) tables for each synthetic customer over the particular date range, and a set of ad spots per company over the particular date range, each with a ground truth number of lift visitors(Fig. 8A-B, and paragraphs [0081]- [0082], discloses together show how lift is attributable to two sport that aired .., determines that response profile (within an attribution time window) and runs ) .
With respect to claim 19 Chen in view of Ebstyne teaches elements of claim 15, furthermore, Chen teaches the computer program product further comprising:
applying a measurement algorithm to the synthetic data universe or a portion thereof so as to produce a performance measurement of a media creative(paragraph [0006], discloses Nielson’s audience measurement systems can provide some quantified measures of audience response to TV programs, the Nielson television rating , paragraph [0010], discloses measure the effectiveness of a TV spot ( or, the efficiency of an ad spend) in the physical world by the number of unique visitors (UVs) to its website in the online world and paragraph [0042], discloses an important metric in measuring the performance of a media creative (e.g., attribution to the number of UVs at the website)); and
comparing the performance measurement with a simulated ground truth value from the synthetic data universe so as to generate a result(paragraph [0081], discloses the final computed lift values that will be allocated to each of the spots based on the overall lift for each minute (minutely lift) and paragraph [0082], discloses the system can generate a report and/or send a notification to a user of the result (e.g., attribution allocations, performance metrics etc.) ).
With respect to claim 20, Chen in view of Ebstyne teaches elements of claim 15, furthermore, Chen teaches the computer program product further comprising: tuning the measurement algorithm based on the result so as to improve the measurement algorithm(paragraph [0040], discloses a lift can be an important metric in measuring the performance of a media creative (e.g., its attribution to the number of UVs at the website), it can be important to accurately attribute the lift to the correct TV spot).
Prior arts:
Chen et al (US Pub., No., 2022/0237653 A1) discloses a media creative attribution method includes determining a response profile within an attribution time window, the response profile being a portion of a unique visitor (UV) curve associated with a website. In some cases, a shadow baseline analysis is run on every media creative that aired within an extended time window to determine whether to adjust the response profile. A total lift within the attribution time window is determined utilizing a baseline of the UV curve. A weight for each media creative that aired within the attribution time window is determined. Utilizing the weight, the total lift is allocated to individual media creatives that aired within the attribution time window.
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.
Response to Arguments
Applicant's arguments filed 35 U.S.C 101 rejection filed on 30 September 2025 with respect to claims 1-20 have been fully considered but they are not persuasive. The claimed element focused on determining a set of synthetic companies," "matching a measured average response rate," and "generating a synthetic data universe". These are mathematical, statistical, or data-manipulation techniques, often considered "methods of organizing human activity" or "mental processes". The core function is analyzing TV spot data to find inaccuracies in a measurement algorithm, which is an abstract, non-physical concept.
Furthermore, matching a measure average response rate to a mean synthetic response rate are merely using a computer as a tool for manual analysis that claims will fall. The claims uses standard components (computer, analytics platform, networked computing environment) for implanting an abstract idea rather than solving the technological problem. A 2025 Federal Circuit decision (Recentive Analytics, Inc.) held that applying conventional machine learning/statistical models to analyze TV advertisement data is considered an abstract idea and, therefore, not patent-eligible. Therefore, the 35 U.S.C 101 rejection to claims 1-20 is maintained.
Applicant’s arguments of 35 U.S.C 102 rejection filed on 30 September 2025 with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA DAGNEW whose telephone number is (571)270-3271. The examiner can normally be reached 9-6:45.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at (571) 270 -3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SABA DAGNEW/Primary Examiner, Art Unit 3621