Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/11/2026 has been entered.
This office action is responsive to RCE filed on 02/11/2026. Claims 1, 7-8, 14, and 20 are amended. Claims 1-20 are pending examination.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 and 8 is/are drawn to method (i.e., a process), claim(s) 14 is/are drawn to a system (i.e., a machine/manufacture). As such, claims 1, 8, and 14 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. Specifically, claim(s) 1, 8, and 14 recite(s) obtaining input data including content presentation data and user interaction data, wherein the content presentation data describes a user interaction with presentation content and is time varying with reference to changes in an amount of the presentation content provided via at least one digital content channel over a plurality of time periods, and wherein the user interaction describes user activity in response to the user interaction with the presentation content; modifying the content presentation data by applying a temporal delay effect to the content presentation data to obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, predicted user interaction data by computing a plurality of decision tree regressors, wherein trained to predict user interactions based on nodes of the plurality of decision tree regressors are trained to infer a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data, which is grouped within the Mathematical Concepts and is similar to the concept of (mathematical relationships and mathematical calculations) and Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and Mental Processes and grouped as following:
obtaining input data including content presentation data and user interaction data, wherein the content presentation data describes a user interaction with presentation content and is time varying with reference to changes in an amount of the presentation content provided via at least one digital content channel over a plurality of time periods, and wherein the user interaction describes user activity in response to the user interaction with the presentation content; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
modifying the content presentation data by applying a temporal delay effect to the content presentation data to obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, predicted user interaction data by computing a plurality of decision tree regressors, wherein trained to predict user interactions based on nodes of the plurality of decision tree regressors are trained to infer a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights based on the prediction; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
presenting content via the at least one digital content channel to a user based on the predicted user interaction data; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as system, processor, memories, machine learning model merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the system, processor, memories, machine learning model perform(s) the steps or functions of obtaining input data including content presentation data and user interaction data, wherein the content presentation data describes a user interaction with presentation content and is time varying with reference to changes in an amount of the presentation content provided via at least one digital content channel over a plurality of time periods, and wherein the user interaction describes user activity in response to the user interaction with the presentation content; modifying the content presentation data by applying a temporal delay effect to the content presentation data to obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, predicted user interaction data by computing a plurality of decision tree regressors, wherein trained to predict user interactions based on nodes of the plurality of decision tree regressors are trained to infer a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, processor, memories, machine learning model to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. As discussed above, taking the claim elements separately, the system, processor, memories, machine learning model perform(s) the steps or functions of obtaining input data including content presentation data and user interaction data, wherein the content presentation data describes a user interaction with presentation content and is time varying with reference to changes in an amount of the presentation content provided via at least one digital content channel over a plurality of time periods, and wherein the user interaction describes user activity in response to the user interaction with the presentation content; modifying the content presentation data by applying a temporal delay effect to the content presentation data to obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, predicted user interaction data by computing a plurality of decision tree regressors, wherein trained to predict user interactions based on nodes of the plurality of decision tree regressors are trained to infer a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2-7, 9-13, and 15-20 further describe the abstract idea of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. Claim(s) 2-7, 9-13, and 15-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. As discussed above, taking the claim elements separately, the system perform(s) the steps or functions of wherein: the content presentation data is divided across different channels; processing the content presentation data using a transformation; and applying mix modeling (MM) to the transformed content presentation data; wherein: the carryover effect is modeled using an adstock transformation; wherein: the plurality of decision tree regressors form a causal forest; wherein: the user interaction variable models user activity in response to the content presentation data; wherein: the node in the decision tree regressors indicates a causal factor of an outcome. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Subject Matter Overcoming the Cited Prior Art
3. As detailed in the Office Action the Examiner has not applied a prior art rejection to Claim(s) 1-20 when viewed in combination with the corresponding independent claims, however the claim(s) has/have been rejected other grounds as detailed in the Office Action.
In reference to independent claims 1, 8, and 14, the Office is unaware of any references that teach, individually or without an unreasonable combination of references, the combination of limitations steps found in the claims especially limitation that says: “obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, wherein the machine learning model is trained to predict user interactions based on a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein the machine learning model is trained by generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights of the machine learning model based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data.”. No reference found that would teach the above limitation(s).
The first most relevant prior art identified by the Examiner is US11057655B2. It teaches obtaining input data including content presentations and where in the content presentation data is time varying, but its missing the feature of obtaining modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, wherein the machine learning model is trained to predict user interactions based on a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein the machine learning model is trained by generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights of the machine learning model based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data. Therefore, it lacks the combination of claimed elements as claimed by the independent claims.
The second most relevant prior art identified by the Examiner is/are 20230085559. It teaches generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, but its missing the feature of the face, but its missing the features of obtaining modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, wherein the machine learning model is trained to predict user interactions based on a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein the machine learning model is trained by generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights of the machine learning model based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data. Therefore, it lacks the combination of claimed elements as claimed by the independent claims.
Other relevant prior art found by the Examiner is 20250225379, 20190080246, US11843568B1 but they also are missing some of the features in the independent claims.
All these references listed above teaches some of the features in the limitations of the claim but when combining it becomes not obvious and the references would teach the claim as a whole.
Examiner note: none of the references or combined references teach the combination of limitations of claim 1, 8, and 14 or no reference found that would teaches the combination of limitations of claim 1, 8, and 14, especially claim limitations: obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, wherein the machine learning model is trained to predict user interactions based on a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein the machine learning model is trained by generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights of the machine learning model based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data, and which is an idea of obtaining content presentation data; generating, using a machine learning model, predicted user interaction data by computing a plurality of decision tree regressors, wherein nodes of the decision tree regressors are trained to infer a causal relationship between a user interaction variable and a treatment variable; and present content to the user based on the predicted user interaction data. The causal relationship is based on maximizing a difference in a relationship between a user interaction variable and a treatment variable of a tree.
When taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available prior art suggest or otherwise render obvious further modification of the evidence at hand. Such modifications would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious. Therefore, the prior art rejection has been withdrawn.
NPL Reference
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Decision Tree Algorithm, Explained” describes “Classification is a two-step process, learning step and prediction step, in machine learning. In the learning step, the model is developed based on given training data. In the prediction step, the model is used to predict the response for given data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. Decision Tree Algorithm Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too.”.
Pertinent Art
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20200390337 teaches similar invention which describes utilizing the model 346 to detect whether the user has a fever and/or whether the user is intoxicated may involve computer 340 performing various operations, depending on the type of model. The following are some examples of various possibilities for the model 346 and the type of calculations that may be accordingly performed by the computer 340, in some embodiments, in order to detect whether the user has a fever and/or whether the user is intoxicated: (a) the model 346 comprises parameters of a decision tree. Optionally, the computer 340 simulates a traversal along a path in the decision tree, determining which branches to take based on the feature values. A value indicative of whether the user has a fever and/or whether the user is intoxicated may be obtained at the leaf node and/or based on calculations involving values on nodes and/or edges along the path; (b) the model 346 comprises parameters of a regression model (e.g., regression coefficients in a linear regression model or a logistic regression model). Optionally, the computer 340 multiplies the feature values (which may be considered a regressor) with the parameters of the regression model in order to obtain the value indicative of whether the user has a fever and/or whether the user is intoxicated; and/or (c) the model 346 comprises parameters of a neural network. For example, the parameters may include values defining at least the following: (i) an interconnection pattern between different layers of neurons, (ii) weights of the interconnections, and (iii) activation functions that convert each neuron's weighted input to its output activation. Optionally, the computer 340 provides the feature values as inputs to the neural network, computes the values of the various activation functions and propagates values between layers, and obtains an output from the network, which is the value indicative of whether the user has a fever and/or whether the user is intoxicated.
Response to Arguments
6. Applicant's arguments filed 02/11/2026 have been fully considered but they are not persuasive.
A. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong One. Examiner respectfully disagrees. As for Step 2A Prong One, of the Abstract idea is directed towards the abstract idea of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data which is grouped within the Mathematical Concepts and is similar to the concept of (mathematical relationships and mathematical calculations) and Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)), (MPEP § 2106.04).
B. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong Two. Examiner respectfully disagrees. As for Step 2A Prong Two, the claim limitations do not include additional elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and the claim is not more than a drafting effort designed to monopolize the judicial exception and the claim limitation simply describe the abstract idea. The limitation directed to gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data does not add technical improvement to the abstract idea. The recitations to “system, processor, memories, machine learning model” perform(s) the steps or functions of obtaining input data including content presentation data and user interaction data, wherein the content presentation data describes a user interaction with presentation content and is time varying with reference to changes in an amount of the presentation content provided via at least one digital content channel over a plurality of time periods, and wherein the user interaction describes user activity in response to the user interaction with the presentation content; modifying the content presentation data by applying a temporal delay effect to the content presentation data to obtain modified content presentation data, wherein the temporal delay effect represents a decay of an effect of the presentation content; generating, predicted user interaction data by computing a plurality of decision tree regressors, wherein trained to predict user interactions based on nodes of the plurality of decision tree regressors are trained to infer a causal relationship between a user interaction variable of the user interaction data and a treatment variable corresponding to the modified content presentation data, and wherein generating the plurality of decision tree regressors, predicting the user interactions using the plurality of decision tree regressors, and adjusting weights based on the prediction; and presenting content via the at least one digital content channel to a user based on the predicted user interaction data. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
C. Applicant argues that the claims are not directed to a judicial exception under Step 2B.
Examiner respectfully disagrees. As for Step 2B, The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the limitation directed to gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data does not add significantly more to the abstract idea. Furthermore, using well-known computer functions to execute an abstract idea does not constitute significantly more. The recitations to “system, processor, memories, machine learning model” are generically recited computer structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of gathering and obtaining content or data with prediction for user interaction or clicks with content divided across different channels with decision tree regressors with notes of the plurality of decision tree regressors which are trained to infer a causal relationship between the user interaction or clicks and treatment variable and displaying or presenting content to the user based on predicted user interaction data. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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