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 office action is in response to the application filed on 2/7/2025.
Claims 1-8 are 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.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-6 are directed to a method. Claim 7 is directed to a method. Claim 8 is directed to a system. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
Claim 1 includes all of the limitations of claim 7 and additional limitations. Claims 1 and 8 recite virtually identical limitations. Claim 1 will be used as representative. Each claims additional elements will be addressed individually. The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claims 1, 7, and 8:
retrieving data corresponding to a campaign item;
inputting the data corresponding to the campaign item to a first algorithm, wherein the first algorithm was trained to predict a type of change to a campaign item based on first labelled campaign item data, wherein each data point in the first labelled campaign item data comprises data corresponding to a campaign item and a label comprising a categorical value indicating a type of change made to the campaign item;
receiving, from the first algorithm, a first prediction, wherein the first prediction indicates a type of change for the campaign item;
inputting the data corresponding to the campaign item and the first prediction to a second algorithm, wherein the second algorithm was trained to predict a magnitude of the type of change to the campaign item based on second labelled campaign item data, wherein each data point in the second labelled campaign item data comprises data corresponding to a campaign item and a label comprising a numerical value indicating an amplitude of a change to the campaign item;
receiving, from the second algorithm, a second prediction, wherein the second prediction indicates a magnitude corresponding to the first prediction;
outputting, to a user, the first prediction and the second prediction for display as a recommended action for the campaign item;
receiving, via the user, user input indicating whether the user has accepted the recommended action, rejected the recommended action, or modified the recommended action;
modifying the user display based on the user input; and
applying, to the campaign item, changes from the user input.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
2. The method of claim 1, further comprising generating an additional training data point based on the user input; and
further training the first algorithm and the second algorithm using the additional training data point.
3. The method of claim 1, wherein retrieving the data corresponding to the campaign item comprises retrieving a number of clicks, a total cost, a number of impressions, and a number of conversions.
5. The method of claim 1, wherein the categorical value of the label indicates that: no changes were made to the campaign item, a new similar campaign item was created, the campaign item was paused, a budget of the campaign item was increased, or the budget was decreased.
6. The method of claim 1, wherein the numerical value of the label indicates an amount that the campaign item budget was increased or decreased.
The claims provide a manner of inputting campaign data into an algorithm to predict a type of change for the campaign. The campaign data and the first prediction are input into a second algorithm to predict the magnitude of the type of change. Thus, when considered individually and as an ordered combination, the claims embody certain methods of organizing human activity. The predictions are then provided to a user as recommendations. Finally, a user can choose whether to implement the recommendations. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors).
Additionally, but for the inclusion of generic computing devices, the claim limitations could be performed mentally or using pen and paper. A human analog would be able to receive the data, analyze the data using an algorithm, make a prediction from the algorithm, input the data and the prediction into another algorithm, and then decide whether to implement the predications as recommendations to a campaign. As a result, the claims also fall under mental process category of abstract ideas.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: first machine learning algorithm/ machine learning algorithm (claim 1, 2, 7, 8); second machine learning algorithm (claim 1, 2, 8); user interface (claim 1, 7, 8); wherein the first machine learning algorithm and the second machine learning algorithm comprise neural networks (claim 4); at least one processor and memory storing a plurality of executable instructions (claim 8);
The first machine learning algorithm/ machine learning algorithm, second machine learning algorithm, user interface, at least one processor and memory storing a plurality of executable instructions, and wherein the first machine learning algorithm and the second machine learning algorithm comprise neural networks are recited at a high level of generality and merely “apply it” (the abstract idea) using generic computing components (See MPEP 2106.05(f)).
There are no meaning details regarding the actual machine learning algorithms and amount to mere high level “apply it” implementation. Nothing in the claims improves machine learning, technology, or a technical field.
Similarly, the at least one processor and memory is mere generic computer implementation and does not go beyond the “apply it” level. Nothing in the claims improves upon computers, technology, or a technical field.
The user interface merely displays content and allows for user input. As such, it is a generic interface that does not go beyond the “apply it” level of implementation. Nothing in the claims improves upon interfaces, technology, or a technical field.
The limitation reciting wherein the first machine learning algorithm and the second machine learning algorithm comprise neural networks is recited at a high level of generality and does not go beyond the “apply it” level of implementation. The claims provide no meaningful limitations with regard to improving neural networks, technology, or a technical field and merely recites their general use.
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea.
As a result, the claims are not patent eligible.
With regard to prior art:
The examiner was unable to find a reasonable combination of references to teach each and every limitation in the context of the claimed invention. There exist numerous references that teach providing various campaign information into machine learning models that produce recommendations such as increasing the budget of an ad campaign. However, the examiner was specifically unable to find the following limitations:
“inputting the data corresponding to the campaign item to a first machine learning algorithm (MLA), wherein the first MLA was trained to predict a type of change to a campaign item based on first labelled campaign item data, wherein each data point in the first labelled campaign item data comprises data corresponding to a campaign item and a label comprising a categorical value indicating a type of change made to the campaign item;”
“inputting the data corresponding to the campaign item and the first prediction to a second MLA, wherein the second MLA was trained to predict a magnitude of the type of change to the campaign item based on second labelled campaign item data, wherein each data point in the second
labelled campaign item data comprises data corresponding to a campaign item and a label comprising a numerical value indicating an amplitude of a change to the campaign item;”
Murphy (US 2021/0065228) is considered the closest prior art. Murphy teaches Analyzing various campaign parameters and using machine learning to provide recommendations to modify the campaign. In various implementations, the campaign parameters may further define an incentive budget associated with the campaign, an incentive cap defining the maximum amount for any given incentive, an expiration date associated with the campaign, a sale volume cap associated with the campaign, a campaign target parameter (i.e., a sales target, a volume goal, and/or other campaign target), and/or other aspects of the campaign. For example, campaign modification component may be configured to identify a suggested modification to at least one campaign parameter (e.g., the incentive cap) using an unsupervised goal-based machine learning algorithm applied to at least a campaign target parameter and one or more of sale information, incentive request information, and/or other information associated with a campaign available to system. Murphy further includes an interface in which the user can be shown the recommended changes and choose to apply them to the campaign.
Kim et al (US 2017/0323326) teaches using a machine learning model to identify current marketing campaign parameters that are the most effective configurable parameters to adjust.
Brown et al (US 2015/0310481) teaches using machine learning to adjust business input data (e.g., marketing budget, business offering prices, etc.), the marketing parameters 130 (i.e., the marketing allocations), and/or the list of keywords in order to improve marketing campaigns. The machine learning algorithm monitors the CPC, the CTR, and the average number of impressions to determine the budget weights needed for each keyword to maximize the number of clicks at the requested marketing budget.
Ma et al (US 2019/0130436) teaches a digital campaign modification system that can generate suggestions utilizing a machine learning model. It using machine learning to determine a set of triggers for automatic campaign adjustments. Additionally, it includes an interface for making modification to the campaign and reviewing recommendations.
Stacking to Improve Model Performance: A Comprehensive Guide on Ensemble Learning in Python by Brijesh Soni – 5/1/2023 – generally teaches the use of ensemble learning. It specifically discusses the technique of stacking which involves taking outputs from one machine learning model and using them as inputs in another machine learning model.
Aviyam et al (US 2021/0200943) teaches using a machine learning model to make recommendations for changes to a campaign or to create a new campaign for increasing the number of customers, visitors, subscribers, users, etc. The machine learning model may optimize the campaign to acquire a new group of customers by optimizing for the individuals who are not part of a cohort of members or subscribers of the offerings. The machine learning model may recommend changes to the campaign, include modifying content (such as advertisements) or the complete campaign (such as an adset) and targeting criteria.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/ Primary Examiner, Art Unit 3621
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