DETAILED ACTION
The present application (Application No. 18/129,447), filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in reply to communications by Applicants responding to first office action on the merits, received 08 August, 2025.
Status of Claims
Claims 1, 8, 15, are amended. Therefore, claims 1-20, are currently pending and addressed below.
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 1-20, 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.
Step 1: In the instant case, claims 1-7, are directed to a method, claims 8-14, are directed to a system, and claims 15-20 are directed to a product, 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; 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.
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).
The claims recite “a primary trained computer model”, however this model is only passively recited in past tense, alluding to a pre-determined model, and therefore it is outside the scope of the claims. Training steps are not claimed and training features are outside of the scope of the claims. Accordingly, the additional elements when the claim elements are viewed individually and as a whole do not integrate the abstract idea into a practical application.
As amended, the claims now recite 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 6, 13, 20, recite additional elements representative of additional training details which when viewed as a whole integrate the abstract idea into a practical application. Claims 6, 13, 20, are eligible.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11, 14-18, are rejected under 35 U.S.C. 103 as being unpatentable over James et al. (US 2018/0218075) (hereinafter “James8075”), in view of Bardin et al. (US 2010/0262484) (hereinafter “Bardin2484”), and further in view of Collins et al. (US 2007/0027760) (hereinafter “Collins7760”).
Regarding claims 1, 8, 15, James8075 discloses:
(identifying a content campaign having a campaign-level bid, a plurality of keywords, and a sponsor). (campaign-level bid). Campaign level parameters.
A content provider 125 may establish an electronic content campaign with one or more parameters, such as keywords or other selection criteria. A content campaign can include a hierarchical data structure that includes content groups, content item data objects, and content selection criteria. The campaign level parameters can include, for example, keywords, a campaign name, a budget for the content campaign, etc. (see at least James8075, FIG. 1, ¶25). Content groups under the same content campaign can share the same campaign level parameters, but may have tailored specifications for particular content group level parameters, such as keywords and/or bids for keywords and/or budget (see at least James8075, ¶26-27).
These campaign level parameters of James8075 represent default campaign parameters.
It is further noted, that since bids are (or can be) tailored (modified), (modifying), then a teaching of the campaign-level bids that are being modified, is implicit. Further it is noted, that since James8075 teaches bidding associated with keywords, then any outputs or recommendations in reference to keywords, are also implicitly in reference to bids for these keywords.
(automatically determining, based on a primary trained computer model, a primary keyword group having a subset of keywords of the plurality of keywords, wherein the primary trained model is trained using a scoring of the plurality of keywords with respect to a primary category).
James8075 discloses: Machine learning technique (a primary trained computer model) to identify concepts, and keywords based on the concepts. Keywords can be included or excluded using a threshold; for example, if a keyword's relevance score with respect to a concept is greater than or equal to a threshold, then the keyword can be included. (see at least James8075, ¶16).
The data processing system 120 can receive, as input, a set of machine learned topics based on clustering keywords with similar network activity or network interactions, and can further use the selected topics to generate an output with a ranked set of keyword recommendations (see at least James8075, ¶37). The data processing system 120 can identify the one or more keywords based on the topics selected by the content provider device 125 (see at least James8075, ¶39).
(a primary keyword group having a subset of keywords of the plurality of keywords). The data processing system 120 can filter out or remove one or more keywords to generate a subset of keywords for which to generate aggregate relevancy scores (see at least James8075, ¶49). For example, the data processing system 120 can identify a first set of five keywords that are each associated with a first topic (primary keyword group), and a second set of five keywords that are associated with a second topic (primary keyword group). The data processing system 120 can determine that one or more of the keywords of the first and second sets of keywords (a subset of keywords of the plurality of keywords) have relevance scores for both the first topic and the second topic that satisfy a filter threshold (e.g., a numeric value from 0 to 1, 0 to 10, 0 to 100, or alphanumeric value, or letter grade). (see at least James8075, ¶49).
Likewise, it is noted, James8075 teaches an equivalence (mapping or correspondence) between topic and/or themes and/or categories, and keywords (and between topic/theme/category hierarchy and keywords hierarchy) (see at least James8075, ¶27-28) (a primary keyword group having a subset of keywords of the plurality of keywords).
For example, a car dealership can create a different content group for each brand of vehicle it carries, and may further create a different content group for each model of vehicle it carries. Examples of the content group themes that the car dealership can use include, for example, “Make A sports car” “Make B sports car,” “Make C sedan,” “Make C truck,” “Make C hybrid,” or “Make D hybrid.” An example content campaign theme can be “hybrid” and include content groups for both “Make C hybrid” and “Make D hybrid”, for example (see at least James8075, ¶27).
A keyword can include one or more terms or phrases. For example, the car dealership may include “sports car,” “V-6 engine,” “four-wheel drive,” “fuel efficiency,” as keywords for a content group or content campaign (see at least James8075, ¶28).
(a graphical user interface). User interface for providing recommendations for parameters associated with campaign management (see at least James8075, ¶32-33, 35).
(wherein the graphical user interface comprises a drop-down menu interface element for a user to select a keyword group). Drop down interface element (see at least James8075, ¶32).
System comprising computing devices, processors, servers, memory, computer readable media, interfaces and software instructions stored in memory that enable the system to execute the steps of the method over network communications and to enable interaction between participants and the system (see at least James8075, fig. 1, ¶19-23) (processor) (memory) (computer readable media).
James8075 does not disclose:
(plurality of modified bids that are different from the campaign-level bid).
(presenting the predicted performance metrics for the primary keyword group at the plurality of modified bids that are different from the campaign-level bid).
(displaying, in a first tabular interface element).
( responsive to a user interaction with a selectable interface element corresponding to one of the different override bids in the graphical user interface, modifying the graphical presentation).
(receiving, via a user interaction with a bid set interface element in the graphical user interface, a user selection of an override bid value for the subset of keywords that is different from the campaign-level bid ).
( 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).
However, Bardin2484 teaches these limitations.
In particular, Bardin2484 further discloses:
Systems and methods for integrated campaign management interfaces for online advertisements. A campaign management interface facilitates review of advertising placements (e.g., websites) and keywords, as well as provide a detailed analysis of subgroups (e.g., webpages within a website) in a single interface. (see at least Bardin2484, abstract, ¶16).
The advertiser can select a keyword, placement, or subgroup of a keyword or placement, and immediately see the performance of the selected keyword, placement, or subgroup of a keyword or placement (see at least Bardin2484, ¶34). Although the examples used herein focus on placements, the features and functions described below can also be applied to keywords (see at least Bardin2484, ¶35). The integrated campaign management interface 200A can also include a "Keywords" tab representation 215, which can show information similar to the "Placements" tab representation, except that the placement URLs are replaced with keywords. (see at least Bardin2484, ¶39).
The campaign management interface can also facilitate adding of selected keywords, placements, or subgroups thereof, to be considered for participation in an auction for an advertising slot associated with the selected keywords, placements, or subgroups thereof (a user interaction with one or more user interface elements) (see at least Bardin2484, ¶39). Participation in auctions associated with a subgroup of placements or keywords. (see at least Bardin2484, ¶59). (a keyword auction opportunity).
The automatic placements summary portion 265 of the interface 200A-D can include, for example: a "Modify bid" button representation 270 (“an override bid value” as claimed) (see at least Bardin2484, fig. 2C, ¶46). The "Modify Bid" button representation 270 can facilitate the modification of a default bid or the addition of a specific bid for placements having a selected checkbox representation 277, 278 (see at least Bardin2484, fig. 2C, ¶46). The bid modification pop-up representation 310 can be provided to the advertiser as a result of the advertiser selecting a "Modify Bid" button representation 270 (e.g., button representation 270 of FIG. 2C) from within an integrated campaign management representation 300A, or in response to adding a placement from the automatic placements to the selected placements (see at least Bardin2484, fig. 3A, ¶51).
In some implementations, selected placements can be distinguished from automatic placements through the presence of a user modified bid (e.g., automatic placements can use a single default bid to identify placements) (see at least Bardin2484, fig. 3A, ¶51).
The "Modify placement bid" button representation 367 can facilitate modification of a bid associated with a selected subgroup of the placement or keyword (see at least Bardin2484, ¶59).
Bardin2484 further discloses:
Campaign management graphical user interface (GUI) configured to display campaign data in tabular format (tables and tabular elements) in response to user interactions (responsive to a user interaction with a bid landscape interface element); (a first tabular interface element); (a second tabular interface element) (see at least Bardin2484, fig. 2-3, “200A-D” and “300A-C”, ¶36-50)
Campaign-management GUI comprising interactive buttons including drop-down menu buttons (a drop-down menu interface element) (see at least Bardin2484, fig. 2-3, ¶44, 46, 56).
Campaign-management GUI comprising interactive buttons which enable an advertiser to customize display of campaign data, and to display default campaign data values as well as plurality of modified campaign data values, and if-then scenarios, including bid data and keyword data (see at least Bardin2484, fig. 2-3, “200A-D” and “300A-C”, ¶36-50) (a first tabular interface element; a second tabular interface element, and so forth); (a set of selectable interface elements).
Bardin2484 therefore teaches: (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).
It is further noted that since at its core, James8075 teaches: bidding for (or associated with) keywords, then any output or recommendations in reference to keywords, are also implicitly in reference to bids for these keywords. Further as explained above, James8075 teaches keyword groups and subsets, and further teaches content provider manually defines/selects campaign-level keywords and/or bids for keywords (¶37), and further teaches machine learning technique that receives as input a set of keywords and provides as output a ranked set of keyword recommendations, representative of predicted keyword performance metrics wherein as it was noted, bids associated with said ranked set of keyword recommendations are implicit. Accordingly, when James8075 is expanded with Bardin2484 these above features taught by James8075 will be likewise taught in any combination of James8075 in view of Bardin2484
James8075 and Bardin2484 they both teach campaign management user interface and system generated keyword recommendations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to expand the bids associated with the set of keywords generated by the machine learning in the system of James8075, to include the above-mentioned elements taught by Bardin2484, such as: the graphical/tabular display and interactive features of Bardin’s interface, and modifying bids associated with selected keywords (override), and the ability in Bardin2484 of displaying bids on a GUI. One of ordinary skill in the art would have been motivated to expand in this way, to facilitate visualization and understanding of campaign results, and/or to shift control to the advertiser (e.g., if the advertiser is not fully satisfied with the system generated output, or to simply experiment with campaign bidding strategies), and in so doing, gaining the overall benefit of a more flexible bidding functionality.
Regarding the limitation: (determining predicted performance metrics for the primary keyword group), as the bid landscape interface (i.e., the campaign management GUI) in the above James8075/Bardin2484 combination appears to be silent about “predicting”, even though its dynamic modification features can be taken to represent predicting tools.; and in addition, the teaching in James8075 (see ¶37) that the data processing system 120 generates an output with a ranked set of keyword recommendations, is a determination of which keyword recommendations are expected (“predicted” as claimed) to do better.
However, even if it could be argued that James8075 and/or Bardin2484, separate or combined, do not specifically teach:
(predicted performance metrics for the subset of keywords of the primary keyword group).
(displaying, in a first tabular interface element, predicted performance metrics for the subset of keywords of the primary keyword group).
(displaying, via a graphical interface element in the graphical user interface, a graphical presentation of at least one predicted performance metric at different modified bids ).
Collins7760 discloses:
User interfaces used to facilitate management and optimization of ad campaigns. Advertisers use these interfaces to access ad campaign information and ad campaign performance information, search the information, analyze the information, obtain reports, summaries, etc. Advertisers may also change listings or bidding strategies using these interfaces, which changes are updated in the campaign data store 105. Furthermore, these interfaces may be used to perform comparisons of the performance of components of ad campaigns. (see at least Collins7760, fig. 2A-2D, 31, ¶27). Graphical user interface 190 displays bidding and performance data/metrics in table formats and in graphs (tabular) (the graphical presentation illustrating a change) (see at least Collins7760, fig. 22-24B).
Forecasting component 190, operable to predict keywords trends, forecast volume of visitor traffic based on the ad's position, as well as estimating bid value for certain ad positions (predicted performance metric at different modified bids). (see at least Collins7760, fig. 2A-2D, ¶38). Campaign optimizer component 175 uses data received from the channel server 150, forecasting component 190, third party analytics feed component 190, quality score component 185, and BIG 155 to determine how much to bid on which ads (see at least Collins7760, ¶42).
In one embodiment, the campaign optimizer component 175 analyzes the obtained analytics data, including ad campaign information, ad campaign performance information, as well as potentially other information, such as user information, to facilitate an ad campaign strategy forecasted or anticipated to be optimal or likely to be optimal. In some embodiments, optimizing is performed with respect to parameters, or a combination of parameters, specified by an advertiser, supplied automatically or partially automatically by the ad campaigns facilitation program, or in other ways. (see at least Collins7760, ¶43).
Therefore, as per above, Collins7760 teaches: user interface configured to display campaign metrics, search the information, analyze the information, obtain reports, summaries, etc; and bidding and performance data illustrated in graphs and in table formats; and changes to the at least one predicted performance metric (these interfaces may be used to update and perform comparisons of the performance of components of ad campaigns); and further teaches: predicted performance metric at different modified bids.
Further, Collins7760 and Bardin2484 (and/or the above James8075/Bardin2484 combination) both teach user interfaces used to facilitate management and optimization of ad campaigns and tabular interface elements. Collins7760 further teaches the further improvement of displaying campaign prediction data in the campaign management GUI.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the ability in Bardin2484 of displaying bids on a GUI (i.e., default bids) and of modifying bids associated with selected keywords in the previously formulated James8075/Bardin2484 combination, further in view of the feature in Collins of illustrating with tables and other graphics on a GUI a change to the at least one predicted performance metric. One of ordinary skill in the art would have been motivated to expand in this way, since the GUI already displays campaign metrics, and then graphically illustrating on the GUI, a change to the at least one predicted performance metric may facilitate understanding of the comparisons of the performance of components of ad campaigns, and is already part of the features of analyzing information and obtaining reports/summaries.
Moreover, one of ordinary skill in the art at the time of the invention would have been motivated to expand in this way, since doing so is applying a known technique (displaying forecasting data on a user interface) to improve a similar method for displaying campaign data on a user interface in the same way, wherein this improved functionality is a predictable result within the capabilities of one of ordinary skill in the art.
Regarding the limitations:
(presenting, in a graphical user interface element in a graphical user interface, a graphical presentation including a first plot illustrating a relationship between actual campaign performance metrics and corresponding campaign-level bid values for the plurality of keywords), and
(a second plot illustrating a relationship between predicted performance metrics and corresponding bid values for the primary keyword group);
as just explained, the above formulated James8075/Bardin2484/Collins7760 combination, teaches (i) actual campaign performance metrics, and also teaches (ii) corresponding campaign-level bid values for the plurality of keywords, and also teaches (iii) predicted performance metrics, and also teaches (iv) corresponding bid values for the primary keyword group. Moreover, particularly in view of the 30+ figures of Collins7760 (see at least Collins7760, fig. 3-11, 14-31) that explicitly depict tabular and plotted campaign data and plotted correspondence between various plurality of tabular data, this above formulated James8075/Bardin2484/Collins7760 combination teaches plots as well as tables of all types of campaign related data.
Accordingly, given that tabular/plotted campaign data and plotted correspondence between various plurality of tabular campaign data, and the specific campaign related data the limitations (i) through (iv), are limitations that already implemented in the above formulated James8075/Bardin2484/Collins7760 combination, then it would have been obvious to try, by one of ordinary skill in the art before the effective filing date of the claimed invention, for the above formulated combination, to teach the claimed first and second plot limitations, since these limitations are some of a finite number of identified, predictable plotted correspondence scenarios between tabular campaign data (a finite number of identified, predictable potential solutions), to the recognized need of plotting available campaign data, and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success.
The examiner further notes, that in the dynamic, graphical campaign management system in the above formulated James8075/Bardin2484/Collins7760 combination, the campaign data and metrics are continuously updated, otherwise such dynamic, graphical campaign management system would be useless and would totally lack utility. Therefore, this above formulated combined system teaches: (to update the second plot to illustrate a revised relationship between predicted performance metrics and corresponding override bid values for the primary keyword group).
Regarding claims 2, 9, 16, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claim 1; claim 8; and claim 15; respectively) as per the above rejection statements.
James8075 further discloses: The data processing system 120 can filter out or remove one or more keywords to generate a subset of keywords for which to generate aggregate relevancy scores (see at least James8075, ¶49). For example, the data processing system 120 can identify a first set of five keywords that are each associated with a first topic (primary category) (primary keyword group), and a second set of five keywords that are associated with a second topic (a second keyword group). The data processing system 120 can determine that one or more of the keywords of the first and second sets of keywords (a subset of keywords of the plurality of keywords) have relevance scores for both the first topic and the second topic that satisfy a filter threshold (e.g., a numeric value from 0 to 1, 0 to 10, 0 to 100, or alphanumeric value, or letter grade). (see at least James8075, ¶49).
(a scoring of the plurality of keywords). (see at least James8075, ¶16, 49).
The examiner notes that the ML methodology of James8075 is directed to- and implemented for the purpose of enabling not just one trained model, for one topic and/or one keyword group, and subsets (which would comprise quite a useless, futile and infeasible system), but instead, to many different ML models, topics and/or keyword groups, and subsets; in fact, as many as desired and needed by each advertiser.
Therefore, James8075 teaches: “first trained computer model”, “a first category”, “a first keyword group having a first subset of keywords”; and “second trained computer model”, “a second category”, “a second keyword group having a second subset of keywords”; and so forth.
Regarding claims 3, 10, 17, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claim 1; claim 8; and claim 15; respectively) as per the above rejection statements.
As articulated in the rejection of claim 1, the obviousness modification of James8075 in view of Bardin2484 teaches: Receiving and displaying override bid information via interface 200A-D (see at least Bardin2484, fig. 2C, ¶46, 51, 59). Therefore, James8075 in view of Bardin2484 teaches: (causing the keyword subset with the predicted performance metrics to be displayed on a user device; and receiving the override bid for the keyword subset from the user device; wherein setting the override bid is performed responsive to receiving the override bid).
Regarding claims 4, 11, 18, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claim 1; claim 8; and claim 15; respectively) as per the above rejection statements.
James8075 further discloses: (wherein the scoring of the plurality of keywords with respect to the primary category comprises a label indicating a relevance of a keyword to a content item of the content campaign). Ranked set of keyword recommendations (see at least James8075, ¶37), said ordered set indicative of “labels”.
Regarding claims 7, 14, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claim 1; and claim 8; respectively) as per the above rejection statements.
James8075 discloses: (predicted metrics for a portion of the keywords of the keyword group and aggregating the metrics for the keyword group). (see at least James8075, fig. 4, ¶38, 47, 49-53, 90-101).
Claims 5-6, 12-13, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over James et al. (US 2018/0218075) (hereinafter “James8075”), in view of Bardin et al. (US 2010/0262484) (hereinafter “Bardin2484”), and further in view of Collins et al. (US 2007/0027760) (hereinafter “Collins7760”), and further in view of Goto et al. (US 11,663,623) (hereinafter “Goto3623”).
Regarding claims 5, 12, 19, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claims 1-2; claims 8-9; and claims 15-16; respectively) as per the above rejection statements.
James8075 does not disclose: (wherein the scoring of the plurality of keywords with respect to the second category comprises a label indicating a keyword frequency of keyword auction opportunities), but Goto3623 discloses this limitation.
Goto3623 discloses: Performance metrics associated with marketing campaigns and machine learning methodology for predicting campaign performance (see at least Goto3623, fig. 1, ¶1:30-54).
The calculation unit 152 calculates, using an already learned model, a predicted occurrence rate (hereinafter, a predicted CV rate) that is an occurrence rate of purchase actions or predetermined actions and the predicted frequency of occurrence (hereinafter, the prediction number of CVs) that is the frequency of occurrence of purchase actions or predetermined actions from the presence or absence of CVs and the number of pieces of data (see at least Goto3623, fig. 1, 16, ¶8:7-21, 9:44-50).
James8075 teaches methodology to predict campaign performance outcomes in association with scoring keyword auction opportunities. Goto3623further teaches a known technique of predicting a frequency of a campaign objective, wherein this known technique is applicable to the methodology of James8075 as they both share characteristics and capabilities, namely, they both teach “comparable” methodology to optimize advertising campaigns and to predict campaign performance outcomes.
Accordingly, a person of ordinary skill in the art before the effective filing date of the claimed invention, would have recognized that applying the known technique of Goto3623 to the campaign methodology of James8075 (and/or of James8075 in view of Bardin2484) would have yielded predictable results and resulted in an improved system, and that such combination would have been within the capabilities of POSITA for applying a known technique to improve a similar method, in the same way.
Regarding claims 6, 13, 20, James8075 in view of Bardin2484 and Collins7760 discloses: All the limitations of the corresponding parent claims (claim 1; claim 8; and claim 15; respectively) as per the above rejection statements.
Goto3623 discloses: Historic campaign performance data is used in training the ML models (see at least Goto3623, abstract, ¶2:62-3:6) (training the primary computer model based on a set of prior content campaigns by the sponsor).
James8075 teaches methodology to predict campaign performance outcomes in association with keywords with different bids and scoring keyword auction opportunities. Goto3623 further teaches a known technique of predicting a frequency of a campaign objective, wherein this known technique is applicable to the methodology of James8075 as they both share characteristics and capabilities, namely, they both teach “comparable” methodology to optimize advertising campaigns and to predict campaign performance outcomes.
Accordingly, a person of ordinary skill in the art before the effective filing date of the claimed invention, would have recognized that applying the known technique of Goto3623 to the campaign methodology of James8075 (and/or of James8075 in view of Bardin2484) would have yielded predictable results and resulted in an improved system, and that such combination would have been within the capabilities of POSITA for applying a known technique to improve a similar method, in the same way.
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:
These additional elements dynamically modify a graphical presentation in response to user interactions with interface elements, which provide a technical improvement to the functioning of a computer system.
In Example 37, the eligible claim recited a method for dynamically rearranging icons in a user interface based on user interaction history. The USPTO found that the invention improved computer functionality by adjusting icon positions based on usage frequency, thereby enhancing user experience.
The present claims are analogous to Example 37.
As in Example 37, the claimed method does more than present generic data; it defines a particular manner of interaction between the user, the GUI, and the underlying computing system. The claimed update mechanism improves the computer's operation by enabling real- time, in-place plot modifications. This is a functional improvement in the way the computer system processes and displays information.
In response:
Applicant’s remarks pretty much repeat with slightly different words, the same remarks previously presented in the pending 09/11/2024 response, again, equating the claimed invention to example 37. Therefore, the present Examiner’s response is accordingly reiterated here.
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,
The claims recite 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.
The claims recite a user interface that is configured to graphically display metrics, wherein the metrics are the results of this bidding optimization, and the amended limitations now further recite more interactivity details. While being more descriptive, these newly amended GUI limitations merely narrow the displaying (emphasis added) concept using the same general-purpose computers and these interactivity features still do not recite technological improvements.
The examiner disagrees that the claimed invention “it defines a particular manner of interaction between the user, the GUI, and the underlying computing system”. Besides plotting particular sets of available campaign data using general-purpose computers, any interactivity features still do not recite technological improvements.
Unlike Example 37 where the additional elements provided a specific improvement over prior systems resulting in an improved user interface for electronic devices; the technology of displaying graphs and tables of data in the instant claimed invention is not improved in any way, and the specific data that is displayed/plotted or 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. The new limitations are a further step of displaying data. Even if the particular abstract idea of reporting or displaying the results of bidding optimization analysis using graphical depictions and/or graphs “displaying bidding metrics” as claimed, represent an improvement or innovation, “the claims here are ineligible because their innovation is an innovation in ineligible subject matter.
Although the various “displaying” and “data entry” limitations in the as now amended claims, as explained in the rejection, are part of the abstract idea and do not require a determination in Step 2B that they are well-understood, routine, conventional activity, because US 20070027760 Collins, (which teaches the claimed graphical features of the campaign management interface (see fig. 22-24B)), performed in PE2E Search Tool and constrained by the 3/31/2023 priority date of the instant application: <("2007/0027760").urpn. AND (PGPB | USPT | USOC).dbnm. and @ad<"20230331">, yields ninety (90) hits of US patents alone (not even counting PGPubs or other types of documents) by tens of different assignees and tens of different inventors, spread over a time period of 2007-2022 prior to the instant invention, that have relied in part on this prior art and are therefore indicative of features that have been well-understood, routine and conventional in the field before the filing of the instant invention.
35 U.S.C. 103
New grounds of rejection are presented to address the amended limitations.
Conclusion
The prior art previously made of record and not relied upon is considered pertinent to applicant's disclosure. US 20150324865 (Illowsky).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO IOSIF whose telephone number is (571) 270-7785. The examiner can normally be reached on Mon-Wed, 9:00am-4:00pm teleworking.
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/Mario C. Iosif/ Primary Examiner, Art Unit 3621