DETAILED ACTION
This Office action is in reply to RCE filed on 04/11/2026.
Claims 1, 8, and 15 are amended; claims 6, 13, and 20 are cancelled; claims 21-23 are newly added.
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 .
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 1-5, 7-12, 14-19, and 21-23, 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.
Step 1: In the instant case, claims 1-5, 7, and 21 are directed to a method, claims 8-12, 14, and 22 are directed to a computer program product, and claims 15-19, and 23 are directed to a computer system, therefore the claims are directed to statutory categories of invention.
Step 2A- Prong 1: Independent claim 1 as now amended comprises steps of: identifying a content campaign having a campaign-level bid, a plurality of keywords, and a sponsor; presenting, a first plot in a graphical user interface element in a graphical user interface; using a training dataset generated from one or more prior content campaigns of the sponsor, the training dataset comprising training keywords with corresponding bids from a prior content campaign, the training comprising, for each of a set of training keywords: comparing an output of the primary computer model with a score of the training keyword with respect to the primary category; automatically determining, based on a primary trained computer model, a primary keyword group having a subset of keywords of the plurality of keywords; the primary trained model is trained using a scoring of the plurality of keywords with respect to a primary category; determining predicted performance metrics for the primary keyword group at a plurality of modified bids that are different from the campaign-level bid; presenting data on a bid landscape interface and using a drop down menu interface element, the predicted performance metrics for the primary keyword group at the plurality of modified bids that are different from the campaign-level bid; displaying, a first tabular graphical presentation of at least one predicted performance metric at different modified bids ; displaying a second plot in a graphical user interface element in a graphical user interface, tracking user interaction with the interface and responsive to a user interaction with a bid landscape interface element in the graphical user interface, displaying, in a second tabular interface element, different override bids for the primary keyword group, the second tabular interface comprising a set of selectable interface elements each corresponding to an override bid of the different override bids; modifying the graphical presentation; receiving a user selection of an override bid value for the subset of keywords that is different from the campaign-level bid; and using the override bid in a process for selection of the content campaign in response to a keyword in the keyword subset matching a keyword auction opportunity.
The independent claims are directed to a method for bidding optimization in an advertising campaign. Accordingly, the claimed steps represent a method of organizing commercial interactions comprising advertising, marketing and sales activities, which falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping.
In addition, it is noted, these claimed steps are steps of collecting/tracking data (transmitting, receiving, storing, gathering), analyzing data, making determinations/correlations, and displaying/presenting data., which as recited, are abstract. All these steps, but for the use of generic computer components that execute them, are generic functions performed by general-purpose computers, which relate to concepts that can be performed in the human mind.
Claims 8 and 15, recite substantially similar subject matter and the same subsequent analysis should be applied thereto.
Step 2A- Prong 2: Additional elements include: a processor; and a non-transitory computer-readable storage medium having instructions; a graphical user interface, automatically determining, based on a primary trained computer model, training a primary computer model for a primary category, applying the primary computer model to the training keyword, and updating one or more parameters of the primary computer model based on the comparison.
These additional elements are recited at a high level of generality and the steps that they execute represent generic functions which can be performed by a general-purpose computer without any novel programming or improvement in the operation of the computer itself. These additional elements are merely invoked as tools to perform an abstract idea (mere instructions to apply the exception) as discussed in MPEP 2106.05(f).
As amended, the claim now recites “training a primary computer model for a primary category, applying the primary computer model to the training keyword, and updating one or more parameters of the primary computer model based on the comparison”, however the steps of training a model using the data, and updating the model based on outcome feedback is merely applying the well known and conventional techniques for training and refining the model.
Claim further recites a “graphical user interface” comprising graphical elements that is/are configured to graphically display metrics, wherein the metrics are the results of this bidding optimization and a first and a second plot. However, the technology of displaying graphs and tables of data is not improved in any way, and the fact that the data consists of bidding metrics does not change this situation. The claimed invention merely uses a general-purpose computer to display available information in graphical form.
Step 2B: Based on the reasoning provided under Step 2A- Prong 2, the claims under Step 2B do not recite “significantly more” than the abstract idea. At this point, either under the “Certain Methods of Organizing Human Activity” grouping scenario where all the claim steps can be seen as being part of the abstract ideas, or under the “Mental Processes” grouping scenario, the analysis is terminated because the same analysis with respect to Step 2A Prong Two applies here in Step 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 dependent claims have been considered.
Claims 2, 9, 16, recite a second trained computer model, also alluding to a pre-determined model, and therefore it is outside the scope of the claims. The second trained model is trained using a scoring of the plurality of keywords with respect to a second category , but this training recitation still does not describe sufficient details of how the training is performed and therefore the ML-mediated solution only recites the idea of a solution or outcome.
Claims 3, 10, 17, recite displaying data.
Claims 4, 11, 18, and 5, 12, 19, further narrows the scoring with a label indicating a relevance of a keyword and association with a scoring.
Claims 7, 14, further narrow the determining predicted campaign metrics.
When considered as a whole, the same analysis with respect to Step 2A Prong Two and step 2B, apply to these additional elements. They cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 21, 22, and 23 further recites additional limitation of “wherein updating one or more parameters of the primary computer model based on the comparison comprises backpropagating through the primary computer model based on the comparing to update one or more parameters of the primary computer model.” Step 2A- Prong 2: The additional limitation does not integrate the abstract idea into practical application, as the additional limitations merely recites applying machine learning. Here, the machine learning techniques such as backpropagation for updating/refining the models are being implemented. Step 2B: Based on the reasoning provided under Step 2A- Prong 2, the claims under Step 2B do not recite “significantly more” than the abstract idea. The same rational applies, here as previous step.
Allowable Subject Matter
Claims 1-5, 7-12, 14-19, and 21-23 are allowed over the prior art. The applicant’s arguments with regard to prior art rejection are persuasive. Furthermore, though each limitation can be addressed individually (see prior art rejection previously presented, the amended claim language substantially recites previous claim 6); the combination and rational to arrive at the claimed invention would not be reasonable without hindsight.
Response to Arguments
Applicant's arguments filed 08/08/2025 have been fully considered.
Typographic error acknowledgement:
The examiner acknowledges typographic errors in the Conclusion section of the pending nonfinal office rejection sent out 04/09/2025. The examiner inadvertently posted final rejection conclusive statements in said pending non-final rejection.
35 U.S.C. 101
Applicant's arguments regarding 35 U.S.C. 101 are not persuasive. The rejection is maintained.
Applicant argues:
“The Office Action acknowledges that claims 6, 13, and 20, which recite training details, "integrate the abstract idea into a practical application" and are eligible under 35 U.S.C. § 101. The amendments to claims 1, 8, and 15 now incorporate substantially similar training steps into the independent claims.”
In response:
The applicant does positively recite the training aspect of the model into the claim language; however, the training steps merely indicate applying the known technique to train the model. Furthermore, claim 21, recite the parameter updating aspect, however, the claim makes it clear that backpropagation is being used to update/refine the model, which is known technique in machine learning. The claims don’t present any technical detail, that teach an improvement to technology, or providing a solution to technical problem, rather the machine learning is merely at apply it level.
Applicant argues:
“The claimed training methodology improves the efficiency of the computer system compared to alternatives. As described in the specification, campaigns typically comprise several thousand keywords, and manually determining optimal bid values and keyword groupings is challenging. The trained computer model enables the system to automatically determine keyword groups based on learned patterns from prior campaigns, eliminating the need for sponsors to manually evaluate and group thousands of individual keywords.”
In response:
As the applicant have made it clear in the remarks, the issue is of automating, a manual process. Automating a process does make the system more efficient, however that is due to the use of tool, rather than improvement into the technology. A simple example would be using excel for accounting; the accounting of millions of entries can be very challenging, and using the excel sheet one can quickly automate the accounting tasks; this will make the task efficient, and save time, however it will not overcome the abstract idea of simply performing accounting using mathematical concepts. Automating a process alone, without providing a technical improvement to the system will not integrate the abstract idea into practical application.
Applicant argues:
“The efficiency improvement is achieved through the specific training approach recited in the claims. By training the computer model using a dataset comprising training keywords with corresponding bids from prior content campaigns, the system learns sponsor-specific bidding patterns and preferences. This enables the "automatically determining" step to efficiently identify keyword groups that align with the sponsor's historical behavior, reducing the computational resources and time required compared to manual keyword-by-keyword evaluation or generic keyword grouping approaches that do not account for sponsor-specific patterns.”
In response:
This is merely training the model, and applying machine learning to a specific environment. The training the model is not being interpreted to be an abstract idea, rather these limitations are being considered as additional limitations. The claims, or the specification doesn’t provide any detail as to how the training the model technology is being improved, rather it clears indicates applying the machine learning on sponsor data.
Applicant argues:
The claimed method reduces the number of user interactions required to configure a campaign with customized keyword bids. Without the trained model, a sponsor would need to individually evaluate each of potentially thousands of keywords to determine appropriate groupings and bid values. The trained model automates this process by predicting which keywords should be grouped together based on the category and the sponsor's historical bidding behavior, presenting the sponsor with automatically-generated keyword groups and predicted performance metrics at different bid values. This represents a specific improvement in how the computer system processes and manages large-scale advertising campaigns.
In response:
The examiner agrees, that without the trained model, a sponsor would need to individually evaluate each of potentially thousands of keywords to determine appropriate groupings and bid values. However, the machine learning is merely at apply it level, and the claims or the specification don’t provide any technical details that improves machine learning itself, or show any technical improvement. Please note, the limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
.S.C. 103
Applicant’s arguments, see Pg. 16-19, filed on 04/11/2026, with respect to claims 1-5, 7-12, 14-19, and 21-23 have been fully considered and are persuasive. The prior art rejection has been withdrawn. For further details, please see allowable subject matter section of the office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
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