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 .
This action is in response to the Application filed on 10/24/2023. Claims 1-20 are pending in the case. All claims are examined and rejected accordingly.
Information Disclosure Statement
As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 10/24/2023 and 07/16/2025 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
4. 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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, 13 and 18,
Step 1 Analysis:
According to the first part of the analysis, in the instant case, claims 1-20 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A Prong One Analysis: The limitations:
obtain interaction data of a media channel, the interaction data comprising timeseries emphasis data for the media channel (This step recites data gathering and observation of marketing activity and historical information which fall within the mental processes/certain methods of organizing human activity.)
execute a neural network using the interaction data as input to generate transformed timeseries emphasis data for the media channel, the neural network configured to estimate a shape function (This step recites mathematical manipulation of data and mathematical concept which fall within the mental processes and mathematical concepts grouping of abstract ideas.); and
execute a Bayesian regression model using the transformed timeseries emphasis data for the media channel as input to generate one or more performance variables for the media channel (This step recites mathematical manipulation of data and mathematical concept which fall within the mental processes and mathematical concepts grouping of abstract ideas.);
The above limitations in the context of this claim merely used mathematical concepts of estimation of shape function, execution of Bayesian regression model, transformation of time series data and generation of performance variables which constitute mathematical calculations and relationships. The claim also recites obtaining and analyzing medical media interaction data to predict performance variables which falls under evaluation and analysis of information. Thus, the claims are patent eligible because they do not recite a judicial exception.
Step 2A Prong Two Analysis: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
The claim recites additional element:
The claim does not recite a particular machine learning architecture.
A system, comprising: at least one processing circuit comprising at least one memory and one or more processors, the one or more processors configured to: (claim 1)
One or more non-transitory computer-readable media, the one or more non-transitory computer readable media comprising instructions which, when executed by one or more processors, cause the one or more processors to… (claim 18)
Addental elements such as executing time series data, executing g neural network, estimating shape function does not recite a technological process other than mathematical computation and the additional limitations fail to integrate the abstract idea into a practical application. This judicial exception is not integrated into a practical application. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B Analysis: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
The claim recites additional element:
Addental elements such as processors, memory neural network and Bayesian regression models were well understood routine and conventional and the claim merely uses generic computing components to implement mathematical calculations and perform predictions. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Additional elements A system, comprising: at least one processing circuit comprising at least one memory and one or more processors, the one or more processors configured to:… (claim 1), One or more non-transitory computer-readable media, the one or more non-transitory computer readable media comprising instructions which, when executed by one or more processors, cause the one or more processors to… (claim 18), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The dependent claims respectively recite a judicial exception in limitations of: “wherein the one or more processors are further configured to: select the neural network for execution from a plurality of respective neural networks stored in the at least one memory responsive to determining the neural network corresponds to the media channel, each of the plurality of respective neural network corresponding to a different media channel.” (claims 2/14/19); “wherein the one or more processors are configured to: for each of the plurality of respective neural networks corresponding to respective media channels, execute the respective neural network using interaction data for the respective media channel for a time period to output respective transformed timeseries emphasis data; propagate the respective transformed timeseries emphasis data of each of the respective neural networks into a regression layer to generate a predicted result; and train the neural network based on an actual performance of the media channel during the time period compared to the predicted result.” (claims 3/15/20), “wherein the one or more processors are configured to train each of the plurality of respective neural network based on the actual performance of the media channel during the time period compared to the predicted result.” (claims 4/16), “wherein the one or more processors are configured to train the neural network by: obtaining an aggregate result based on an actual result for each of the respective media channels for the time period; and training the neural network using a loss function according to a difference between the aggregate result and the predicted result.”(claim 5/17 ), “wherein the one or more processors are configured to: propagate the transformed timeseries emphasis data into a regression layer to generate a predicted result, wherein the one or more processors are configured to train the neural network based on an actual performance of the media channel compared to the predicted result.” (claim 6), “wherein the interaction data is first interaction data, the one or more performance variables are first one or more performance variables, and the one or more processors are further configured to: obtain second interaction data associated with the media channel for a time period, the media channel operating based on the transformed timeseries emphasis data for a duration of the time period; execute the neural network using the second interaction data as input to generate second transformed timeseries emphasis data for the media channel; execute the Bayesian regression model using the second transformed timeseries emphasis data for the media channel as an input to generate second one or more performance variables for the media channel; and generate a record comprising an identification of the media channel and the second one or more performance variables.”(claim 7) , “wherein the one or more processors are further configured to: receive the second interaction data from a remote computing device associated with the media channel. (claims 8), “wherein the one or more processors are configured to: propagate one or more characteristics into the regression layer in addition to the transformed timeseries emphasis data to generate the predicted result.”(claim 9), “wherein the one or more processors are further configured to: simulate operation of the media channel using the shape function of the neural network to generate the interaction data.”(claim 10), “wherein the one or more processors are configured to: execute the neural network using the interaction data and one or more key performance indicators (KPIs) as input to generate the transformed timeseries emphasis data.”(claim 11), “wherein the one or more processors are configured to: execute the neural network using the interaction data and one or more seasonality factors as input to generate the transformed timeseries emphasis data.” (claim 12).
These additional limitations (in claims 2-12, 14-17 and 19-20) also constitute concepts performed in the human mind which fall within the “Mathematical concepts” groupings of abstract ideas. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible
Examiner Comments
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ma (Pat. No. US 12579552 B1, Pub. Date US 12579552 B1) in view of LEVESQUE (US 20200387849 A1, 2020-12-10)
Regarding independent Claim 1,
Ma teaches a system, comprising:
at least one processing circuit comprising at least one memory and one or more processors, the one or more processors (see Ma: Fig.1, Col.4, Ln. 51-65, “Analytical engine 120 can be executed by a server, … implemented using a single-processor system including one processor, or a multi-processor system … software instructions contained within a computer-readable medium, such as within memory.’), configured to:
obtain interaction data of a media channel the interaction data comprising timeseries emphasis data for the media channel (see Ma: Fig.3, Col.7, Ln. 64-67 and Col.8, Ln. 1-3, “At step 302, the computer monitors impression data inputted from a plurality of media channels into a machine learning predictive model. The impression data include a plurality of attributes of content presented on a respective media channel. In an embodiment, the inputted impression data include a plurality of attributes of content presented on the respective media channel for a respective campaign”, see also Fig.1, Col.5, Ln.41-48, “Impressions database 150 stores data representing marketing efforts (e.g., marketing expenditures, also herein called “spend”) that have been put into a specific marketing channel and campaign. In an embodiment, impressions data stores impression data collected from various media channels. In an embodiment, impressions data include various attributes such as timestamp, geolocation, channel, campaign and spend information associated with impressions”),
execute a neural network using the interaction data as input to generate transformed timeseries emphasis data for the media channel (see Ma: Fig.3, Col.8, Ln. 13-24, “step 304, the machine learning predictive model is trained by determining an impact of historical impression data on historical conversion data for each media channel of the plurality of media channels. In an embodiment, the machine learning predictive model is trained on a plurality of attributes of the historical impression data to determine model coefficients for a plurality of marketing channel-campaign combinations of the historical conversion data. In an embodiment, the model coefficients represent respective conversion values for the plurality of the marketing channel-campaign combinations of the historical conversion data.”), the neural network configured to estimate a shape function (see Ma: Fig.3, Col.9, Ln. 25-30, “the optimization model applies an objective function to maximize the total number of predicted conversions due to marketing efforts. In an embodiment, the objective function employs a multiplicative response function that models a convex shape of marketing response curve”)
Ma does not teach the method wherein:
execute a Bayesian regression model using the transformed timeseries emphasis data for the media channel as input to generate one or more performance variables for the media channel.
However, LEVESQUE teaches the method wherein:
execute a Bayesian regression model using the transformed timeseries emphasis data for the media channel as input to generate one or more performance variables for the media channel (see LEVESQUE: Fig.3A, [0104], “At 302 (“Automated Variable Selection 302”), the MMM pipeline 140 may automatically select variables for modeling based on supervised ML network learning. Such supervised ML network learning may use a Bayesian belief network to identify variables that correlate with a performance metric such as sales. Examples of the variables include, without limitation, a brand health (whether the brand was tried by consumers or unaided awareness of the brand by consumers), TV (national, regional, etc.), trade scheme, total cinema, out of home, digital-video, content-print, sponsorships, consumer promotions (sampling, point of sale promotions), digital (social media, standard display, rich media, mobile), radio, and/or other variables that may have a direct, indirect, or latent impact on sales or other performance metric., see also Fig. 4C, [0115] describing “the market mix modeling pipeline 140D may include the market mix modeling pipeline 140C, with added functionality. Such added functionality may include identification of interaction relationships 436 (identifying which interactions together lead to a sale, for example), ML training 437 based on the interaction relationships 436, assessment of interaction impacts 438, and an integrated effectiveness 439 that integrates media effectiveness and interaction relationships to predict factors that lead to a sale and/or other KPI.”)
Because both Ma and LEVESQUE are in the same/similar field of endeavor of machine learning based market mix modeling and media performance prediction , accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Ma to include the system that Bayesian regression model using the transformed timeseries emphasis data for the media channel as input to generate one or more performance variables for the media channel as taught by LEVESQUE. After modification of Ma, the machine learning framework of Ma can incorporate model validation and ensemble technique as taught by LEVESQUE. One would have been motivated to make such a combination in order to improve reliability, prediction accuracy, robustness and model validation.
Regarding Claim 2,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 1. Ma further teaches the one or more processors are further configured to:
select the neural network for execution from a plurality of respective neural networks stored in the at least one memory responsive to determining the neural network corresponds to the media channel, each of the plurality of respective neural network corresponding to a different media channel (see LEVESQUE: Fig.4A-4D, [0111], “illustrate examples of configurable market mix modeling pipelines 140A-140D. The examples illustrated in FIGS. 4A-D are for illustrative purposes only. Based on the configurable nature of a given market mix modeling pipeline 140, the number and arrangement of the particular elements that form the given market mix modeling pipeline may be different than as illustrated. For example, an end user may configure, via the control interface 120, a market mix modeling pipeline according to market mix modeling pipelines 140A-140D or other configuration. It should be further noted that each of the market mix modeling pipelines 140A-140D are illustrated as having functional blocks, which may correspond to or be executed by elements illustrated in FIG. 1, 2 or 3.”)
it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Ma to include the system that determine the neural network corresponds to the media channel, as taught by LEVESQUE. After modification of Ma, the machine learning framework of Ma can incorporate model validation and ensemble technique as taught by LEVESQUE. One would have been motivated to make such a combination in order to improve reliability, prediction accuracy, robustness and model validation.
Regarding Claim 3,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 2. LEVESQUE further teaches the one or more processors are configured to:
for each of the plurality of respective neural networks corresponding to respective media channels, execute the respective neural network using interaction data for the respective media channel for a time period to output respective transformed timeseries emphasis data (see LEVESQUE: Fig.4D, [0115], “a modular and configurable market mix modeling pipeline 140D. As illustrated, the market mix modeling pipeline 140D may include the market mix modeling pipeline 140C, with added functionality. Such added functionality may include identification of interaction relationships 436 (identifying which interactions together lead to a sale, for example), ML training 437 based on the interaction relationships 436, assessment of interaction impacts 438, and an integrated effectiveness 439 that integrates media effectiveness and interaction relationships to predict factors that lead to a sale and/or other KPI.”)
propagate the respective transformed timeseries emphasis data of each of the respective neural networks into a regression layer to generate a predicted result (see LEVESQUE: Fig.7, [0131], “processor 114 may generate and provide, based on the unified MMM, an MMM output 107. The MMO output 107 may include a result of modeling, including predicted effects of certain activities on sales. For example, the unified MMM may output a hypothetical set of marketing activities such as spend amount, marketing time period, duration, and/or other activities and correlate such activities with predicted performance such as sales. In this way, the unified MMM may provide a set of activities and their predicted effect on sales.”)
train the neural network based on an actual performance of the media channel during the time period compared to the predicted result (see LEVESQUE: Fig.2, [0130], “At 712, the processor 114 may ensemble some or all of the plurality of MMMs to determine a unified MMM that models impacts of the one or more activities on the performance metric.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Ma to include the system that train the neural network based on an actual performance of the media channel during the time period compared to the predicted result as taught by LEVESQUE. After modification of Ma, the machine learning framework of Ma can incorporate model validation and ensemble technique as taught by LEVESQUE. One would have been motivated to make such a combination in order to improve reliability, prediction accuracy, robustness and model validation.
Regarding Claim 4,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 3. Ma further teaches the one or more processors are configured to:
train each of the plurality of respective neural network based on the actual performance of the media channel during the time period compared to the predicted result (see LEVESQUE: Fig.7, [0128], “At 708, the processor 114 may generate a custom MMM pipeline 140 based on the one or more portions. For example, the custom MMM pipeline 140 may include the portions illustrated in FIGS. 4A-4D, although other customized set of functions may be included as well or instead. In this manner, the end user may design a set of functions (the custom MMM pipeline 140) for ML on marketing data to predict and optimize activities to maximize some performance metric such as sales.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of Ma to include the system that train each of the plurality of respective neural network based on the actual performance of the media channel as taught by LEVESQUE. After modification of Ma, the machine learning framework of Ma can incorporate model validation and ensemble technique as taught by LEVESQUE. One would have been motivated to make such a combination in order to improve reliability, prediction accuracy, robustness and model validation.
Regarding Claim 5,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 3. Ma further teaches the one or more processors are configured to train the neural network by:
obtaining an aggregate result based on an actual result for each of the respective media channels for the time period (see LEVESQUE: Fig.2, [0071], “The sum of all weights w.sub.i equals 1. As such, a given weight w.sub.i may indicate the probability that the corresponding model is the best among the set of candidate models. The model ensembler 207 may compare the Akaike weight of the best model (the model having the highest corresponding Akaike weight) and the Akaike weight of competing models to determine to what extent the best model is better than other models by calculating evidence ratios, according to Equation 5”); and
training the neural network using a loss function according to a difference between the aggregate result and the predicted result (see LEVESQUE: Fig.2, [0099], “the optimization, simulation and forecasting 216 may provide periodic forecast from the latest models generated by the MMM pipeline 140. In this sense, the optimization, simulation and forecasting 216 may provide updated forecasting and suggestions based on current data available to the system so that marketers and others may revise their marketing strategies accordingly”)
Regarding Claim 6,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 1. Ma further teaches the method wherein:
propagate the transformed timeseries emphasis data into a regression layer to generate a predicted result (see Ma: Fig.2, Col.7, Ln. 25-30, “the machine learning predictive model includes a linear regression model that applies a regression equation to calculate total number of predicted conversions due to marketing impressions.”), wherein the one or more processors are configured to train the neural network based on an actual performance of the media channel compared to the predicted result (see Ma: Fig.2, Col.6, Ln. 16-23, “impressions/conversions model 134 applies linear regression techniques in predicting total number of conversions. In an embodiment, the impressions/conversions model 134 model was trained on historical impressions and conversions data”)
See Motivation to combine Ma and LEVESQUE on claim 1 above.
Regarding Claim 7,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 6. Ma further teaches the method wherein:
the interaction data is first interaction data, the one or more performance variables are first one or more performance variables (see Ma: Fig.3, Col.7, Ln. 64-67 and Col.8, Ln. 1-3, “At step 302, the computer monitors impression data inputted from a plurality of media channels into a machine learning predictive model. The impression data include a plurality of attributes of content presented on a respective media channel. In an embodiment, the inputted impression data include a plurality of attributes of content presented on the respective media channel for a respective campaign”), and the one or more processors are further configured to:
obtain second interaction data associated with the media channel for a time period, the media channel operating based on the transformed timeseries emphasis data for a duration of the time period (see Ma: Fig.1, Col.5, Ln. 55-63, “conversions data include various attributes such as timestamp, geolocation, product, line of business, whether the product is purchased by a new or existing customer, and associated revenue (e.g., premium for an insurance policy). In an embodiment, stored conversions data are indexed to mixed media marketing parameters such as channel and campaign.”);
the neural network using the second interaction data as input to generate second transformed timeseries emphasis data for the media channel (see Ma: Fig.1, Col.8, Ln. 41-48, “the computer executes the machine learning predictive model to determine how the conversion data corresponds to the plurality of attributes for the respective media channel. The machine learning predictive model generates an attribution of the plurality of attributes of the impression data monitored at step 304 for each media channel of the plurality of media channels.”)
generate a record comprising an identification of the media channel and the second one or more performance variables (see Ma: Fig.1, Col.5, Ln. 55-63,
execute the Bayesian regression model using the second transformed timeseries emphasis data for the media channel as an input to generate second one or more performance variables for the media channel (see LEVESQUE: Fig.3A, [0104], “At 302 (“Automated Variable Selection 302”), the MMM pipeline 140 may automatically select variables for modeling based on supervised ML network learning. Such supervised ML network learning may use a Bayesian belief network to identify variables that correlate with a performance metric such as sales.;
Regarding Claim 8,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 7. Ma further teaches the one or more processors are further configured to:
receive the second interaction data from a remote computing device associated with the media channel (see Ma: Fig.1, Col.4, Ln. 30-39, “In various embodiments, mixed media marketing factors include MMM impressions and MMM conversions, and model inputs also may include budget data and external data. In an embodiment, MMM system 100 may be associated with a sponsoring enterprise, such as an insurance company or another financial services company. MMM system 100 may give marketers a better understanding of budget allocation strategy in order to boost total sales of products, such as insurance policies.”)
Regarding Claim 9,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 6. Ma further teaches the method wherein:
propagate one or more characteristics into the regression layer in addition to the transformed timeseries emphasis data to generate the predicted result (see Ma: Fig.1, Col.8, Ln. 25-33, “the machine learning predictive model includes a linear regression model that applies a regression equation to calculate total number of predicted conversions due to marketing impressions. In an illustrative embodiment, the machine learning predictive model effects feature selection by calculating z-scores for coefficients of a non-linear model. In an illustrative embodiment, model tuning employs a greedy grid search procedure to tune model hyper-parameters.”)
Regarding Claim 10,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 1. Ma further teaches the method wherein:
simulate operation of the media channel using the shape function of the neural network to generate the interaction data (see Ma: Fig.1, Col.9, Ln. 42-45, “the optimization model may apply different scenarios such as optimizing only on total budget and optimizing on all constraints.”)
Regarding Claim 11,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 1. Ma further teaches the method wherein:
execute the neural network using the interaction data and one or more key performance indicators (KPIs) as input to generate the transformed timeseries emphasis data (see Ma: Fig.2, Col.7, Ln. 29-36, “The optimization model 250 finds the optimal impressions 280 to maximize quarterly conversions. In an embodiment, given various budget constraints, the optimization model may apply different scenarios in running the optimization. In an embodiment, optimization scenarios include Optimizing only on Total Budget; and Optimizing on all Constraints. In an embodiment, the model uses the metric cost per impression 284 to report on optimal spend 290.” … see Table 2 and Table 3 also for conversion metrics and KPIs used during training and evaluation)
Regarding Claim 12,
As shown above, Ma and LEVESQUE and teaches all the limitations of claim 1. Ma further teaches the method wherein:
execute the neural network using the interaction data and one or more seasonality factors as input to generate the transformed timeseries emphasis data (see Ma: Fig.1, Col.4, Ln. 5-10, “the data indicated that life insurance policies are still the main category of insurance products sold by a financial services enterprise. These weekly data exhibited a clear seasonal pattern of sales. These data indicated model development should extract seasonality separately for each line of business.”)
Regarding independent claim 13,
Claim 13 is directed to a method claim and the claim have similar/same claim limitation as Claim 1 and is rejected under same rationale.
Regarding Claim 14,
Claim 14 is directed to a method claim and has similar/same claim limitation as Claim 2 and is rejected under same rationale.
Regarding Claim 15,
Claim 15 is directed to a method claim and has similar/same claim limitation as Claim 3 and is rejected under same rationale.
Regarding Claim 16,
Claim 16 is directed to a method claim and has similar/same claim limitation as Claim 4 and is rejected under same rationale.
Regarding Claim 17,
Claim 17 is directed to a method claim and has similar/same claim limitation as Claim 5 and is rejected under same rationale.
Regarding independent Claim 18,
Claim 18 are directed to non-transitory computer-readable media claim and has similar/same claim limitation as Claim1 and is rejected under same rationale.
Regarding Claim 19,
Claim 19 is directed to non-transitory computer-readable media claim and have similar/same claim limitation as Claim 2 and is rejected under same rationale.
Regarding Claim 20,
Claim 20 is directed to non-transitory computer-readable media claim and have similar/same claim limitation as Claim3 and is rejected under same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20250094879 A1
Zhao, Xinghua
Title: Generation and Utilization of Channel Allocation Models for Resource Allocation Recommendations.
Description: Computing devices can perform data processing and run machine learning models. Users can engage in various online and offline activities which can result in exposure of information to the user. Subsequent activities by a user can be influenced by prior activity and information exposure.
US 11790379 B2
Saini; Shiv Kumar
Title: Bayesian Estimation Of The Effect Of Aggregate Advertising On Web Metrics
Description: The following description relates to a method and device with natural language processing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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