DETAILED ACTION
This is in reference to communication received 23 September 2025. Claims 1 – 2, 4 – 7, 10 – 13, 15, 17 and 19 – 22 are pending for examination. This 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 – 2, 4 – 7, 10 – 13, 15, 17 and 19 – 22 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.
Independent claim 20, representative of claims 1 and 19, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 20 recites invention directed to monitoring activity generated by an advertising campaign, and if an anomaly in the performance of the campaign is detected, severity of the anomaly is determined and based if the anomaly severity above certain threshold value, a human campaign manager (e.g., is provided with suggestions), or else, certain targeting parameter is modified to affect the performance of the marketing campaign, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. These limitations describe marketing/sales/advertising activities.).
The independent claims further recite the additional functional element of configuring an anomaly detection model to detect anomalies in digital marketing data; applying the anomaly detection model to the set of digital marketing data, to one or more anomalies indicating that for a particular campaign metric, a subset of the set of users are outliers relative to the set of users; determining, that a severity of the anomaly does not exceed a maximum threshold, dynamically adjusting the particular digital marketing campaign. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 1 and 19, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 19), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 1).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2, 4 – 7, 10 – 13, 15, 17 and 21 – 22, these claims further describe the abstract idea, do not set forth further additional elements (unless specified previously, or present insignificant extrasolution activity. These limitations set forth a concept of sending and receiving data to optimize/control/ modify advertising/marketing campaigns. This concept falls within the methods of organizing human activity, commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) grouping identified by MPEP 2106. As such, the claims are determined to recite an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 2, 4 – 7, 10 – 13, 15, 17 and 19 – 22 are rejected under 35 U.S.C. 103 as being unpatentable over Salunke et al. US Publication 2018/0039555 in view of Yan et al. US Publication 2013/0151332.
Regarding claim 20 and representative claims 1 and 19, Salunke teaches system, method and non-transitory medium for generating baselines and monitoring time-series data for anomalies [Salunke, 0003], comprising:
at least one device comprising one or more hardware processors (Salunke, 0133) [Sohum, 0003],
One or more non-transitory machine-readable media storing instructions that, when executed by one or more processors, cause performance of operations (Salunke, 0137, 0138] comprising:
configuring an anomaly detection model to detect anomalies in digital marketing data, based at least on baseline data associated with one or more digital marketing campaigns (Salunke, Within detected seasonal patterns, the system may generate baseline models to represent expected system behavior. The system may leverage the learned behavior to generate baselines that are tailored to the specific environment under examination.) [Salunke, 0035];
receiving, by a digital marketing platform on an ongoing basis, a live stream of a set of digital marketing data that describes digital interactions of a set of users of a target computing platform (Salunke, Data collector 120 may provide the metrics on-demand, periodically, or on a streaming/continuous basis, depending on the particular implementation.) [Salunke, 0061].
Salunke does not explicitly teach data related to a marketing campaign. However, Yan teaches system and method to automated or semi-automated techniques for revising an advertising campaign based on an initial set of advertising results.) [Yan, 0001].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Salunke by adopting teachings of Yan to help advertisers accurately determine the target group that will be most receptive to advertisements in their marketing campaign.
Salunke in view of Yan teaches system, method and non-transitory medium further comprising:
receiving, by a digital marketing platform on an ongoing basis, a live stream of a set of digital marketing data that describes digital interactions of a set of users of a target computing platform [Salunke, 0061] with a particular digital marketing campaign that is currently being executed (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003];
while the particular digital marketing campaign is being executed:
applying, by the digital marketing platform, the anomaly detection model to the set of digital marketing data, to determine if the set of digital marketing data comprises an anomaly indicating that for a particular campaign metric, a subset of the set of users are outliers relative to the set of users (Salunke, If the evaluation data point falls outside the conforming range of values that are between the two limits, then the process classifies the evaluation data point as anomalous. Conversely, if the evaluation data point is within the limits, then the evaluation data point is not classified as anomalous.) [Salunke, 0082];
responsive to determining that the set of digital marketing data comprises the anomaly: identifying, by the digital marketing platform, a demographic micro-segmentation associated with the subset of the set of users (Yan, The campaign adjustment module 104 may additionally be used to select the best ads of a campaign to use for particular target demographics,) [Yan, 0050];
determining, by the digital marketing platform, that a severity of the anomaly does not exceed a maximum threshold for adjusting the particular digital marketing campaign without user input (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083];
responsive to determining that the severity of the anomaly does not exceed the maximum threshold (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083]:
generating, by the digital marketing platform without user input, an adjustment for increasing or decreasing targeting of the digital marketing campaign at the demographic micro-segmentation of the set of users (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003];
transmitting, by the digital marketing platform to the target digital platform without user input, one or more commands to apply the adjustment to the target computing platform (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003].
Regarding claim 2, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium further comprising obtaining the baseline data, wherein obtaining the baseline data comprises;
obtaining prior digital marketing data associated with the particular digital marketing campaign (Salunke, Receive historical time-series dataset) [Salunke, Fig. 3 and associated disclosure];
training a machine learning model to determine one or more baseline performance metrics for digital marketing campaigns (Salunke, when sufficient samples are available for mode, train the model) [Salunke, Fig. 3 and associated disclosure];
applying the machine learning model to the prior digital marketing data associated with the particular digital marketing campaign, to obtain the one or more baseline performance metrics for the particular digital marketing campaign (Salunke, Systems and methods are described herein for performing unsupervised baselining and anomaly detection in cloud and other computing platforms. In one or more embodiments, a baselining and anomaly detection system comprises a set of one or more machine-learning processes that automatically identify the predictability of observed resource behavior.) [Salunke, 0035].
Regarding claim 4, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein the live stream of the set of digital marketing data comprises data indicating one or more performance metrics associated with the particular digital marketing campaign (Salunke, a time series may be collected from one or more software and/or hardware resources and capture various performance metrics of the resources from which the data was collected.) [Salunke, 0042, 0038].
Regarding claim 5, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium further comprising obtaining the baseline data, wherein obtaining the baseline data comprises receiving a plurality of sets of digital marketing data associated, respectively, with a plurality of digital marketing campaigns, wherein at least two digital marketing campaigns in the plurality of digital marketing campaigns are associated with different tenants of a multi-tenant digital marketing platform (Salunke, Systems and methods are described herein for performing unsupervised baselining and anomaly detection in cloud and other computing platforms. In one or more embodiments, a baselining and anomaly detection system comprises a set of one or more machine-learning processes that automatically identify the predictability of observed resource behavior.) [Salunke, 0035].
Regarding claim 6, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein obtaining the baseline data further comprises determining one or more baseline campaign performance metrics, based at least on the digital marketing data associated with the plurality of digital marketing campaigns (Salunke, a time series may be collected from one or more software and/or hardware resources and capture various performance metrics of the resources from which the data was collected.) [Salunke, 0042, 0038].
Regarding claim 7, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium further comprising:
selecting the plurality of digital marketing campaigns from a plurality of available digital marketing campaigns, based at least on one or more campaign selection criteria, at least by training a machine learning model to identify similarities between digital marketing campaigns (Yan, The campaign adjustment module 104 then clusters 550 the combinations into groups based on degrees of similarity between the advertising metric, such as similarity of click-through rates, and computes an average value of the advertising metric for each cluster.) [Yan, 0045];
applying the machine learning model to the available digital marketing campaigns, to select the plurality of digital marketing campaigns (Yan, For example, if the advertising campaign includes two ads, either of which may be shown to users in the depicted target demographic group (i.e., users aged 29-32 and located in the southeast of the United States), selecting this option would effectively partition the targeting …. ) (Yan, 0043].
Regarding claim 10, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium further comprising obtaining the baseline data, wherein obtaining the baseline data obtaining one or more user-defined baseline campaign performance metrics (Salunke, a time series may be collected from one or more software and/or hardware resources and capture various performance metrics of the resources from which the data was collected.) [Salunke, 0042, 0038].
Regarding claim 11, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein: configuring the anomaly detection model comprises
training a machine learning model to detect anomalies in digital marketing data, using the baseline data as training data(Salunke, when sufficient samples are available for mode, train the model) [Salunke, Fig. 3 and associated disclosure];
applying the anomaly detection model to the set of digital marketing data comprises applying the machine learning model to the set of digital marketing data (Salunke, Systems and methods are described herein for performing unsupervised baselining and anomaly detection in cloud and other computing platforms. In one or more embodiments, a baselining and anomaly detection system comprises a set of one or more machine-learning processes that automatically identify the predictability of observed resource behavior.) [Salunke, 0035].
Regarding claim 12, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, the operations further comprising: updating the machine learning model based at least on the anomaly;
wherein updating the machine learning model based at least on the anomaly modifies the baseline data to reflect a change in baseline performance indicated by the live stream of the set of digital marketing data;
wherein a subsequent application of the machine learning model determines if a subsequent set of digital marketing data comprises an anomaly relative to the modified baseline data.
Regarding claim 13, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein configuring the anomaly detection model comprises configuring a set of one or more anomaly detection rules, based at least on the baseline data (Salunke, a baseline model may initially be trained to represent daily seasonality. As more data points are received, weekly patterns may be detected and modelled. To incorporate the newly learned seasonal patterns, the system may transition the baseline model may from a daily seasonal model to a weekly seasonal model. Thus, a baseline model may evolve and become more accurate over time.) [Salunke, 0040].
Regarding claim 15, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein the severity of the anomaly indicates that the anomaly is a good anomaly (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083]; and
dynamically adjusting the particular digital marketing campaign comprises increasing targeting of the particular digital marketing campaign at the demographic micro-segmentation of the set of users (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003].
Regarding claim 17, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium,
wherein the set of digital marketing data is a first set of digital marketing data, the anomaly is a first anomaly, and the severity is a first severity (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083];
the operations further comprising:
determining, by the digital marketing platform, that a second set of digital marketing data comprises a second anomaly;
determining, by the digital marketing platform, that a second severity of the second anomaly exceeds the maximum threshold for adjusting the particular digital marketing campaign without user input (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083];
responsive to determining that the second severity of the second anomaly exceeds the maximum threshold (Yan, only attribute values within the group defined by the initial targeting criteria are considered. For example, if the initial targeting criteria limit the target group to females in general, or to females over age 30 located in the western United States, statistics are not tracked for segments containing males. In other embodiments, statistics may be tracked for segments with attribute values falling outside of the initial targeting criteria, as well) [Yan, 0039]:
presenting, in a graphical user interface, information that describes a recommended action to address the second anomaly (Yan, Based on the advertising metric values for the various segments, the publishing system suggests a modification of the advertising campaign to the advertiser. Possible modifications to the advertising campaign include narrowing the initial targeting criteria to specify at least one of the segments as the modified target group, specifying a different ad for a low-performing segment, and adjusting the value of a bid for display of the ads in the campaign.) [Yan, 0004];
receiving, via the graphical user interface, a user instruction to execute the recommended action (Yan, If the advertiser 110 confirms the suggested modification option, the campaign is modified 360 accordingly.) [Yan, 0039];
executing, by the digital marketing platform, the recommended action to address the second anomaly responsive to receiving the user instruction (Yan, If the advertiser 110 confirms the suggested modification option, the campaign is modified 360 accordingly.) [Yan, 0039].
Regarding claim 21, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, wherein:
the severity of the anomaly indicates that the anomaly is a bad anomaly (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083]; and
dynamically adjusting the particular digital marketing campaign comprises decreasing targeting of the particular digital marketing campaign at the demographic micro-segmentation of the set of users (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003].
Regarding claim 22, as combined and under the same rationale as above, Salunke in view of Yan teaches system, method and non-transitory medium, the operations further comprising:
generating, by the digital marketing platform, an anomaly score associated with the anomaly (Salunke, One or more of these factors may compared to threshold values. If the thresholds are exceeded, then the deviation may be classified as statistically significant (e.g., user input is required). If the deviation is not statistically significant, then monitoring may continue without triggering a responsive action (e.g., user input is not required).) [Salunke, 0083];
wherein determining that the severity of the anomaly does not exceed the maximum threshold for adjusting the particular digital marketing campaign without user input comprises comparing the anomaly score with the maximum threshold (Yan, Based on the statistics, the campaign adjustment module 104 automatically or semi-automatically modifies the campaign to enhance its effectiveness, such as by changing the targeting criteria for ads within the campaign, by adding or removing ads from the campaign, by adjusting the bids for the ads in the campaign, or the like) [Yan, 0003].
Response to Arguments
Applicant’s arguments with respect to claims 1 – 2, 4 – 7, 10 – 13, 15, 17 and 19 – 22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/NARESH VIG/Primary Examiner, Art Unit 3622
February 20, 2026