DETAILED ACTION
Status of Claims
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-Final office action in response to communication received on 04/03/2026. Claims 1-18 are pending and examined herein.
Priority
2. The examiner acknowledges priority benefits being claimed by the Applicant for U.S. Provisional Application No. 63/313,609 filed on February 24, 2022. However the claims as supported by Figs. 7-9 and their associated disclosure are not entitled to the priority benefit as that disclosure was filed in the Non-provisional 18/172,634 filed on 02/22/2023.
Claim Rejections - 35 USC § 101
3. 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 1-18 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. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-8 are a non-transitory computer readable medium; and claims 9-18 are a method. Thus, each claim 1-18, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-8, and 9-18 [i.e. recitation with the exception of additional elements as noted and analyzed under step 2A prong two and step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of predicting performance using a model during a target time period, by training said model on past or historic or a time preceding the target time period, and comparing the predicted performance during the target time period without advertising campaign with performance of the company with the advertising campaign during the target time period to indicate difference in performance or to evaluate impact of the advertising campaign which is certain methods of organizing human activity.
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-18 at least are a non-transitory medium, a processor of a computing device, digital presentation on an interface to visually and programmatically depict data, and machine learning algorithm/model (claim 1); and a computer program executing on a computing device and machine learning algorithm/model implemented in real-time (claim 9); selection through an interface (claim 15); REST API or database connector (claim 16); digital presentation of an interface to upload files (claim 17); CSV or spreadsheet files (claim 18). Remaining claims, either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements including machine learning algorithm/model are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 6-7 and 10 and their associated disclosure and see at least as filed spec. paras. [0013]-[0031] and [0045]-[0050]; and see [0025]-[0028] as it pertains to high level description of machine learning model.
The processor executing the "apply it" instruction is further able to sending/receiving/upload data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) which is considered insignificant extra solution activity (see MPEP 2106.05(g)). Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group).
The abstract idea is intended to be merely carried out in a technical environment such as collecting/communicating data via a network and analyzing data via a generic processor executing the machine learning model in real-time to evaluate impact of marketing campaign during a target time period by comparing it with control (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of predicting performance during a target time period and comparing the predicted performance during the target time period without advertising campaign with performance of the company with the advertising campaign during the target time period to indicate difference in performance or to evaluate impact of the advertising campaign which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-18, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of predicting performance during a target time period and comparing the predicted performance during the target time period without advertising campaign with performance of the company with the advertising campaign during the target time period to indicate difference in performance or to evaluate impact of the advertising campaign which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here datasets are received]; and
(ii) (a)Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)."; and (b) (ii) Collecting and analyzing information to detect misuse and notifying a user when misuse is detected [similarly here datasets are presented on interface and/or interface is provided for dataset selection; and also based on abstract evaluation/comparison of plotted data sets for with and without marketing campaign a notification is provided if the campaign is terminated as a result of the evaluation].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
(i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
4. 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.
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 1-8 are rejected under 35 U.S.C. 103(a) as being unpatentable over Achin et al. (Pub. No.: US 2018/0046926) referred to hereinafter as Achin, in view of Kay H. Brodersen. Fabian Gallusser. Jim Koehler. Nicolas Remy. Steven L. Scott. "Inferring causal impact using Bayesian structural time-series models." Ann. Appl. Stat. 9 (1) 247 - 274, March 2015. https://doi.org/10.1214/14-AOAS788 referred to hereinafter as Brodersen, in view of Devdas et al. (Pub. No.: US2018/0012166) referred to hereinafter as Devdas.
As per claim 1, Achin teaches a non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising (see [0477]):
(a) obtaining a dataset that includes a series of values, in temporal order, that are indicative of performance of a company over an interval of time (see Fig. 9 and its associated disclosure; [0119]; [0334]; [0355]-[0356]); segmenting the dataset into (see [0191]; [0192] “the exploration engine 110 suggests data partitions”; [0193]; [0334]) —
(i) a first dataset corresponding to a first period of time […], wherein the first period of time is representative of a subset of the interval of time (see Fig. 9 and its associated disclosure; [0014]; [0119]-[0120]; [0335]; [0345]-[0354]), and
(c) applying the machine learning model to the second dataset, […] as values are generated over the second period of time, so as to produce a third dataset that is indicative of predicted performance during the second period of time in the absence of the advertising campaign (see Fig. 10 and its associated disclosure; [0003]; [0007]; [0014]; [0030]; [0333]-[0334]; [0344]-[0346]);
(e) […] falls beneath a threshold, generating a notification that prompts termination of the advertising campaign (see [0245]-[0247]; [0298]; [0386]; [0404]).
Achin suggests predicting see Fig. 9 and its associated disclosure; [0014]; [0119]-[0120]; [0356]; [0368], however Achin expressly does not teach (a*) […] preceding an introduction of an advertising campaign […] (ii) a second dataset corresponding to a second period of time over which the advertising campaign occurs, wherein the second period of time is representative of another subset of the interval of time; (b) providing the first dataset, but not the second dataset, to a machine learning algorithm that uses the first dataset, to train a machine learning model to predict performance of the company in the absence of the advertising campaign; (d) causing digital presentation of the second and third datasets on an interface as separate traces, so as to visually and programmatically indicate a difference between performance of the company with the advertising campaign and predicted performance of the company without the advertising campaign (see Fig. 1 and its associated disclosure).
Brodersen teaches (a*) […] preceding an introduction of an advertising campaign […] (see Pgs. 248-249; Pg. 261 “To simulate the effect of advertising, the post-intervention portion of the preceding series was multiplied by 1+e, where e (not to be confused with ε) represented the true effect size specifying the (uniform) relative lift during the campaign period. An example is shown in Figure 3(a)”)
(ii) a second dataset corresponding to a second period of time over which the advertising campaign occurs, wherein the second period of time is representative of another subset of the interval of time (see pgs. 248-249; note Fig. 1 and its description on pg. 249);
(b) providing the first dataset, but not the second dataset, to a machine learning algorithm that uses the first dataset, to train a machine learning model to predict performance of the company in the absence of the advertising campaign (see Figs. 1 and 5 and their associated disclosure; pages 248-249; page 258)
(d) causing digital presentation of the second and third datasets on an interface as separate traces, so as to visually and programmatically indicate a difference between actual performance of the company with the advertising campaign and predicted performance of the company without the advertising campaign (see pgs. 248-249; note Fig. 1 and its description on pg. 249);
(e) in response to a determination that the difference in the actual performance of the company with the advertising campaign and the predicted performance of the company without the advertising campaign […] (see Figs. 1 and 5 and their associated disclosure; pages 248-249; page 258).
Therefore (as it applies to (a*), (b), (d), (e)) it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to segment time series data into subsets utilized for training a predictive model, applying the model, and compare it with the actual performance of advertising campaign, see pg. 248 note “Here, we focus on measuring the impact of a discrete marketing event, such as the release of a new product, the introduction of a new feature, or the beginning or end of an advertising campaign, with the aim of measuring the event’s impact on a response metric of interest (e.g., sales). The causal impact of a treatment is the difference between the observed value of the response and the (unobserved) value that would have been obtained under the alternative treatment, that is, the effect of treatment on the treated”.
(c*) Achin suggests real-time capabilities, see [0103]; [0211]; [0306]; [0327], however lacks teaching of machine learning implementing in real-time […], however in view of compact prosecution the Examiner relies on an additional reference to more expressly teach, i.e. Achin in view of Brodersen expressly does not teach […] in real-time […].
Devdas teaches […] in real-time […] (see [0040] "The data input processor 202 receives data from various sources such as including but not limited to machine data 312, which is an example of machine data 104, social data 314 (e.g., text, images, voice), which is an example of human data 102, and transactional data 316 (see FIG. 3). It is to be noted that input data can be obtained from any possible sources which can provide data relevant to the business entities and their associated entities such as suppliers, manufacturers, customers, etc. Also, the input data can be of any suitable type or can carry any information that can be used for making transaction forecast such as demand forecast for any business entity.";
[0041] "In an example, data received from machine sources (e.g. transactional) are structured data while data received from human sources are unstructured data (e.g. speech, expressions, etc.). The data input processor 202 is configured to convert unstructured data related to the business transactions (e.g., demand information, supply information, information about products and services, customer information and pricing information) into structured information. For instance, the unstructured data may be converted to obtain structured texts, images, voices, and/or videos, among other types of data. The structured data may be processed in order to analyze/understand customer's behavior or sentiment towards a product. Such data may be obtained from third party servers and data centers, such as, shopping centers, supermarkets, service centers and customer feedback platforms, social media platforms, among others. As an example, the data input processor 202 may be Google's open source sentiment analysis (of text, speech and visual) to determine customer sentiment, such as, likes, dislikes about products and offerings.";
[0069] "the system 200 may be provided as a SaaS solution and set of algorithms including the data model that helps with the continuous improvement of the forecast accuracy. The system 200 is capable of receiving huge volumes of continuous feed of data and interpreting the same data flows in adjusting the forecast accuracy as frequently as needed in business real-time mode. The key driver of the system 200 is the forecast accuracy which is the most critical component of any operations process used by current enterprises serving all industries. The system 200 not only processes huge and multiple streams of data volumes but also uses self-learning techniques to ensure that the model that is most relevant and gives the highest forecast accuracy, is chosen. This in turn drives the demand and supply balancing algorithms that are extremely crucial for operating any business operations process with the maximum efficiency (highest output at most optimized cost). The purpose of the system 200 is to help large enterprises as well as small and medium business customers to improve forecast accuracy of their demand so that they can plan the right amount of supply and thereby improve profitability. The system 200 is capable of taking inputs that are continuously processed with results being processed in near business real-time mode.";
[0149] "provide forecast determination continuously, accurately, and automatically. In addition, at least some advantages of the present disclosure include ability to input massive amount of data, ability to process massive amount of data, ability to have self-learning algorithms that maintain high forecast accuracy, ability of forecast accuracy auto-pilot to continuously get feeds from human and machine data and improve forecast accuracy, ability to provide auto-pilot capability for a business process via actionable triggers that are business real-time, ability to reduce human labor involved in managing forecast accuracy, ability to provide a visual aid on the overall impact to the demand and supply with the adjusted forecast and there by the value chain impact visibility, and ability to tie the forecast accuracy to desired result indicators for the business processes. Further, various embodiments of the present disclosure have ability to provide single visual that showcases the impact of forecast accuracy across value chain, ability to input large data volumes from multitude of data sources (not limited to machine data, social data, transactional data), ability to execute highly sophisticated pattern search, pattern match, pattern recognition algorithms involving enormous amounts of data in business real-time, ability to execute self-learning algorithms to enable selection of the right predictive formulae that result in highest forecasting accuracy without the interaction of a human being, ability for continuous improvement of forecast accuracy using machine learning and machine data along with human data with a feature “Forecast Accuracy Auto Pilot”, ability to leverage the impacts of the forecast accuracy improvement across the value chain example but not limited to demand (opportunity, sales order), supply, components planning, delivery and revenue plan with appropriate signals for human intervention based on selected set of desired result parameters, and ability to handle continuous feed of machine data and data from other sources (including non-traditional sources) to improve forecast accuracy and have it drive related business operations processes"
Therefore it would be obvious to modify Achin in view of Brodersen's foregoing suggestions further in view of Devdas's teachings pertaining to processing large volume of data and interpreting said large volume of data to make adjustments in real-time mode through pattern recognition and self-learning algorithms that are able to adjust forecast accuracy as frequently as needed in real-time mode and provide auto-pilot capability via actionable triggers, see at least [0069] and [0149].
As per claim 2, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 1. Achin teaches wherein the interface also includes the first dataset that is presented as a trace (see [0187]).
As per claim 3, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 1. Achin teaches wherein the operations further comprise: tuning the machine learning model for the company in an autonomous manner using a statistical modeling technique (see [0119]; [0135]; [0255]; [0264]).
As per claim 4, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 3. Achin suggests see [0174], however Achin expressly does not teach wherein the statistical modeling technique is a Bayesian structural time series. Brodersen teaches wherein the statistical modeling technique is a Bayesian structural time series (see pg. 251 “we use a fully Bayesian approach to inferring the temporal evolution of counterfactual activity and incremental impact. One advantage of this is the flexibility with which posterior inferences can be summarised.”; pg. 252).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to use structural time-series models as they are flexible and modular, pg. 252.
As per claim 5, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 1. Achin teaches wherein the machine learning model includes one or more state variables that, as part of an inferencing operation, are summed in a weighted manner to establish predicted performance (see [0146]).
As per claim 6, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 5. Achin teaches wherein the machine learning model includes separate state variables for trend, seasonality, and regression, and wherein for each state variable, a corresponding weight is learned through analysis of the first dataset provided to the machine learning algorithm for training purposes (see [0049]; [0246]; [0346]; [0417]-[0419]).
As per claim 7, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 1. Achin suggests [0469], however Achin expressly does not teach wherein the operations further comprise: employing a Monte Carlo algorithm to find a posterior distribution of an output produced by the machine learning model upon being applied to the second dataset, wherein the Monte Carlo algorithm produces, as output, a sequence of random samples; and using the sequence of random samples to estimate integrals with respect to a target distribution, thereby computing expected values for a key performance indicator by which performance is measured.
Brodersen teaches wherein the operations further comprise: employing a Monte Carlo algorithm to find a posterior distribution of an output produced by the machine learning model upon being applied to the second dataset, wherein the Monte Carlo algorithm produces, as output, a sequence of random samples; and using the sequence of random samples to estimate integrals with respect to a target distribution, thereby computing expected values for a key performance indicator by which performance is measured (see pg. 251 note “The approach described in this paper inherits three main characteristics from the state-space paradigm […] Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting. The remainder of this paper is organised as follows. Section 2 describes the proposed model, its design variations, the choice of diffuse empirical priors on hyperparameters, and a stochastic algorithm for posterior inference based on Markov chain Monte Carlo (MCMC).”; pgs. 265-269).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to reuse the samples from the posterior to obtain credible intervals for all summary statistics of interest. Such statistics include, for example, the average absolute and relative effect caused by the intervention as well as its cumulative effect., pg. 269.
As per claim 8, Achin in view of Brodersen and Devdas teaches the claim limitations of claim 7. Achin teaches wherein the key performance indicator is sales, revenue, virality, relevance, or traffic (see [0342]).
5. Claims 9-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Achin et al. (Pub. No.: US 2018/0046926) referred to hereinafter as Achin, in view of Kay H. Brodersen. Fabian Gallusser. Jim Koehler. Nicolas Remy. Steven L. Scott. "Inferring causal impact using Bayesian structural time-series models." Ann. Appl. Stat. 9 (1) 247 - 274, March 2015. https://doi.org/10.1214/14-AOAS788 referred to hereinafter as Brodersen.
As per claim 9, Achin teaches a method performed by a computer program executing on a computing device, the method comprising (see [0016]):
Achin teaches temporal arranging datasets with their corresponding times, i.e. teaches providing (i) a first dataset that includes a first series of values, that are arranged in temporal order and that are indicative of performance of a company over a first interval of time that precedes an advertising campaign and (ii) a second dataset that includes a second series of values, that are arranged in temporal order and that are indicative of performance of the company over a second interval of time that succeeds the advertising campaign (see at least Achin [0029] and [0030]), however the Examiner relies on Brodersen to teach […] a machine learning algorithm that uses the first and second datasets to train a machine learning model to predict performance of the company in the absence of the advertising campaign (see the rejection above for claim 1 limitation (b));
(b) applying the machine learning model to a third dataset that includes a third series of values, that are arranged in temporal order and that are indicative of performance of the company over a third interval of time over which the advertising campaign occurs, so as to produce an output (see Fig. 10 and its associated disclosure; [0014]; [0030]; [0344]; [0346]);
Achin expressly does not teach (c) applying a Monte Carlo algorithm to the output produced by the machine learning model to obtain a series of random samples distributed across a target probability distribution; and (d) estimating, based on the series of random samples, integrals with respect to the target probability distribution, thereby computing expected values for a key performance indicator by which performance of the company is measured.
Brodersen teaches (c) applying a Monte Carlo algorithm to the output produced by the machine learning model to obtain a series of random samples distributed across a target probability distribution (see pg. 251 note “The approach described in this paper inherits three main characteristics from the state-space paradigm […] Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting. The remainder of this paper is organised as follows. Section 2 describes the proposed model, its design variations, the choice of diffuse empirical priors on hyperparameters, and a stochastic algorithm for posterior inference based on Markov chain Monte Carlo (MCMC).”; pgs. 265-269); and
Brodersen teaches (d) estimating, based on the series of random samples, integrals with respect to the target probability distribution, thereby computing expected values for a key performance indicator by which performance of the company is measured (see pg. 251 note “The approach described in this paper inherits three main characteristics from the state-space paradigm […] Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting. The remainder of this paper is organised as follows. Section 2 describes the proposed model, its design variations, the choice of diffuse empirical priors on hyperparameters, and a stochastic algorithm for posterior inference based on Markov chain Monte Carlo (MCMC).”; pgs. 265-269).
Therefore (as it applies to claim limitations (c) and (d)) it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to reuse the samples from the posterior to obtain credible intervals for all summary statistics of interest. Such statistics include, for example, the average absolute and relative effect caused by the intervention as well as its cumulative effect., pg. 269.
As per claim 10, Achin in view of Brodersen teaches the claim limitations of claim 9. Achin teaches wherein the target probability distribution corresponds to the second interval of time (see pg. 248 note “compute the posterior distribution of the counterfactual time series given the value of the target series”; pg. 249; pg. 258).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to reuse the samples from the posterior to obtain credible intervals for all summary statistics of interest. Such statistics include, for example, the average absolute and relative effect caused by the intervention as well as its cumulative effect for the second interval of time, pg. 269.
As per claim 11, Achin in view of Brodersen teaches the claim limitations of claim 9. Achin teaches wherein the Monte Carlo algorithm is based on a Markov chain Monte Carlo approach to sampling from the target probability distribution (see pg. 251 note “The approach described in this paper inherits three main characteristics from the state-space paradigm […] Third, we use a regression component that precludes a rigid commitment to a particular set of controls by integrating out our posterior uncertainty about the influence of each predictor as well as our uncertainty about which predictors to include in the first place, which avoids overfitting. The remainder of this paper is organised as follows. Section 2 describes the proposed model, its design variations, the choice of diffuse empirical priors on hyperparameters, and a stochastic algorithm for posterior inference based on Markov chain Monte Carlo (MCMC).”; pgs. 265-269).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to reuse the samples from the posterior to obtain credible intervals for all summary statistics of interest. Such statistics include, for example, the average absolute and relative effect caused by the intervention as well as its cumulative effect., pg. 269.
As per claim 12, Achin in view of Brodersen teaches the claim limitations of claim 9. Achin teaches wherein the machine learning model includes one or more state variables that, as part of an inferencing operation, are summed in a weighted manner to establish predicted performance (see [0146]).
As per claim 13, Achin in view of Brodersen teaches the claim limitations of claim 12. Achin teaches wherein for each state variable, a corresponding weight is learned through analysis of the first dataset provided to the machine learning algorithm as part of a training operation (see [0231]), Achin expressly does not teach […] in which a spike-and-slab prior is used for each state variable to allow the machine learning model to regularize and perform feature selection. Brodersen teaches […] in which a spike-and-slab prior is used for each state variable to allow the machine learning model to regularize and perform feature selection (see pgs. 248-249 note “framework of our model allows us to choose from among a large set of potential controls by placing a spike-and-slab prior on the set of regression coefficients and by allowing the model to average over the set of controls [George and McCulloch (1997)]. We then compute the posterior distribution of the counterfactual time series given the value of the target series in the pre-intervention period, along with the values of the controls in the postintervention period”).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify Achin in view of Brodersen with motivation to use spike and slab methodology for performing feature selection, pg. 248.
As per claim 14, Achin in view of Brodersen teaches the claim limitations of claim 13. Achin teaches wherein for each state variable, a corresponding spike-and-slab prior is representative of a generative model in which that state variable either attains a fixed value or is drawn toward another value (see pg. 248 note “framework of our model allows us to choose from among a large set of potential controls by placing a spike-and-slab prior on the set of regression coefficients and by allowing the model to average over the set of controls [George and McCulloch (1997)]. We then compute the posterior distribution of the counterfactual time series given the value of the target series”; pg. 256 “When faced with many potential controls, we prefer letting the model choose an appropriate set. This can be achieved by placing a spike-and-slab prior over coefficients [George and McCulloch (1993, 1997), Polson and Scott (2011), Scott and Varian (2014)]. A spike-and-slab prior combines point mass at zero (the “spike”), for an unknown subset of zero coefficients, with a weakly informative distribution on the complementary set of nonzero coefficients (the “slab”). Contrary to what its name might suggest, the “slab” is usually not completely flat”; pg. 257).
As per claim 15, Achin in view of Brodersen teaches the claim limitations of claim 9. Achin teaches further comprising: receiving input that is indicative of a selection, made by a user through an interface, of the first and second datasets or another dataset of which the first and second datasets are a part; and obtaining, in response to said receiving, the first and second datasets or the other dataset (see [0179]).
As per claim 16, Achin in view of Brodersen teaches the claim limitations of claim 15. Achin teaches wherein the first, second, and third datasets or the other dataset are acquired via a Representational State Transfer (REST) application programming interface (API) or a database connector (see [0179]; [0284]; [0292]).
As per claim 17, Achin in view of Brodersen teaches the claim limitations of claim 9. Achin teaches further comprising: causing digital presentation of an interface through which a user is able to directly upload one or more files that include the first, second, and third datasets (see [0223]).
As per claim 18, Achin in view of Brodersen teaches the claim limitations of claim 17. Achin teaches wherein the one or more files are comma-separated value (CSV) files or spreadsheet files (see [0481]).
Response to Applicant’s Arguments
6. Regarding Interview, note the Examiner Interview Summary of record 06/20/2025.
Regarding 101, the “Applicant submits that the claims are not directed to "commercial interactions" or "advertising, marketing, or sales activities or behaviors," as alleged by the Office. Seep. 3 of the Office Action. Rather, the Office's characterization improperly overgeneralizes the claims and fails to account for the specific, recited technical manner in which the results are achieved.
Independent claim 1, for example, recites a specific computer-implemented technique for generating a counterfactual prediction, namely, predicting performance during a time period in which an advertising campaign occurred, under the constraint that the prediction is generated using data from that campaign period. This is achieved by (i) segmenting time-series data into pre-campaign and campaign periods, (ii) training a machine learning model exclusively on pre-campaign data, and (iii) applying the trained model to campaign data to generate a predicted performance profile for the campaign period in the absence of the campaign.
This is not a method of organizing human activity. Instead, it addresses a technical problem in data modeling. That is, how to estimate a performance outcome for a scenario (i.e., absence of an intervention) for which no direct observational data exists. The claimed invention imposes a specific training constraint and produces a counterfactual dataset that cannot be obtained through conventional data analysis or human judgment.
While the resulting output may be used to inform actions such as modifying or terminating an advertising campaign, such downstream use does not render the claimed invention abstract. The focus of the claims is on the technical process for generating the counterfactual prediction, not on any business decision that may follow.
Moreover, the operations of independent claim 1 cannot be performed in the human mind or through mere mental processes. The claims require training and applying a machine learning model to temporally segmented datasets in order to generate a counterfactual time series representing unobserved performance. Such operations are not practically performable in the human mind - or even using pen and paper. In fact, the fact is simply not practical for a human to perform outside of guessing.
Accordingly, Applicant submits that the claims are directed to a specific technical improvement and not to "commercial interactions" or "advertising, marketing, or sales activities or behaviors.” However, the Examiner respectfully disagrees.
However, the determination is based on BRI in light of the as-filed specification based on abstract recitation not additional elements (which the Applicant appears to equate with “technical manner in which the results is achieved”) at least under prong one analysis, as such, the Applicant’s argument in view of “technical manner in which the results is achieved” is unpersuasive.
Further, as explained under prong two “claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-18 at least are a non-transitory medium, a processor of a computing device, digital presentation on an interface to visually and programmatically depict data, and machine learning algorithm/model (claim 1); and a computer program executing on a computing device and machine learning algorithm/model implemented in real-time (claim 9); selection through an interface (claim 15); REST API or database connector (claim 16); digital presentation of an interface to upload files (claim 17); CSV or spreadsheet files (claim 18). Remaining claims, either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements including machine learning algorithm/model are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 6-7 and 10 and their associated disclosure and see at least as filed spec. paras. [0013]-[0031] and [0045]-[0050]; and see [0025]-[0028] as it pertains to high level description of machine learning model.
The processor executing the "apply it" instruction is further able to sending/receiving/upload data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) which is considered insignificant extra solution activity (see MPEP 2106.05(g)). Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group).
The abstract idea is intended to be merely carried out in a technical environment such as collecting/communicating data via a network and analyzing data via a generic processor executing the machine learning model in real-time to evaluate impact of marketing campaign during a target time period by comparing it with control (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of predicting performance during a target time period and comparing the predicted performance during the target time period without advertising campaign with performance of the company with the advertising campaign during the target time period to indicate difference in performance or to evaluate impact of the advertising campaign which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).”.
Also, for instance note (i) offer-based price optimization (OIP Tech); (ii) collecting and comparing known information (Classen); (iii) comparing information regarding a sample or test subject to a control or target data (Ambry/Myriad CAFC); (iv) collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) -certain result here is a tailored content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent); (v) Collecting and analyzing information to detect misuse and notifying a user when misuse is detected (FairWarning); (vi) diagnosing an abnormal condition by performing clinical tests and thinking about the results (Grams); and (vii) obtaining and comparing intangible data (CyberSource) – all note that merely carrying out an experiment or evaluating or comparing data and notifying a user of poor performance or price adjustment or misuse as an output or result fail to integrate the abstract idea into practical application and/or lack significantly more as explained under step 2B analysis in view of the claimed additional elements considered both singularly and in-combination.
Therefore the Examiner respectfully maintain the rejection.
Regarding 103, the Examiner respectfully disagrees with very limited characterization of the cited prior art references. The rejection has been updated in view of filed claim amendments . The Applicant generally discusses each reference individually and then appears to attack each reference individually. Achin already teaches temporally partitioning of datasets and holdout, see at least. The Examiner finds the Applicant’s argument unpersuasive as data prior to January 2014 is in absence of any marketing intervention and post January 2014 is with intervention. Furthermore Brodersen clearly teaches “prediction” i.e. outcome without intervention “a prediction of what would have happened in Y had the intervention not taken place (posterior predictive expectation of the counterfactual with pointwise 95% posterior probability intervals). (b) The difference between observed data and counterfactual predictions is the inferred causal impact of the intervention.” Indeed a PHOSITA would be able to properly construe the rejection as relied upon especially Brodersen as depicting the performance of the marketing campaign and comparing it with simultaneously plotted a do-nothing scenario, i.e. in absence of marketing campaign. Indeed the primary reference also teaches when certain KPIs are not met the decision making party can be informed/notified/alerted to terminate and/or make adjustments. The Applicant is requested to not only see Fig. 1, but once again requested to note Broderson Figs. 5-7 and their associated disclosure. Therefore, the Examiner respectfully disagrees and maintains the rejection.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
*Being noted initially
- Husain et al. (Pub. No.: US2018/0285748) - Pub. No.: US2018/0285748 see [0054] "The online system 112 provides 312 the extracted new feature vector to a machine learning model 122 that generates a predicted performance metric for a content item for each time period of several time periods based on a feature vector extracted from the content item. The machine learning model 122 is trained based on the stored information describing the delivery of the content items and feature vectors extracted from the content items. The machine learning model 122 generates a performance metrics vector 124 (a predicted performance metric for the new content item for each of the plurality of time periods) based on the new feature vector."
- Pub. No.: US2012/0323674 see [0196] "the second model may replace the primary model as the active model responding to purchase requests. Further, the replacement may be based on a prediction that the second model may perform better than the primary model under the current market conditions. In embodiments of the invention, the prediction may be based at least in parts on machine learning, historical advertising performance data 130, historical event data, and real-time event data 160." - WO2005/106656A2 note “The invocation of the link to the cumulative lift chart causes display of a cumulative lift chart. The invocation of the link to the cumulative lift chart causes display of a non-cumulative lift chart. A user is enabled to choose interactively at least one performance criterion change or transformation or interaction of variables to improve model validation process. The user interface enables the user to select at least one machine automated model development process applied to the entire dataset for a validated model process. The user interface enables the user to point and click to cause display of information about the performance of the validated model process applied to the entire set of historical data. The information about the model performance for two independent data subsets includes at least one of: a statistical report card with a link to the statistical report chart, a cumulative lift chart with a link to the cumulative lift chart, a non-cumulative lift chart with a link to the non-cumulative lift chart. The invocation of the link to the statistical report card causes display of the statistics of model process validation. The invocation of the link to the cumulative lift chart causes display of a cumulative lift chart. The invocation of the link to the cumulative lift chart causes display of a non- cumulative lift chart. The final model and the model process validation results are stored persistently. In general, in another aspect, a machine-based method includes, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, for example, displaying to a user a lift chart, monotonicity, and concordance scores associated with each step in a step-wise model fitting process. Implementations may include one or more of the following features. The user is enabled to observe changes in the fit of the model as variables associated with the data are added or removed from a predictor set of the variables. The user is enabled to terminate the fitting of the model when the fitting process reaches an optimal point. In general, in another aspect, a machine-based method includes, for a predictive model based on historical data about a system being modeled, generating measures of the fit of the model to samples of the data, the fit measures being generated separately in percentile segments.”
*Previously noted
- WO2005/106656A2 note “The invocation of the link to the cumulative lift chart causes display of a cumulative lift chart. The invocation of the link to the cumulative lift chart causes display of a non-cumulative lift chart. A user is enabled to choose interactively at least one performance criterion change or transformation or interaction of variables to improve model validation process. The user interface enables the user to select at least one machine automated model development process applied to the entire dataset for a validated model process. The user interface enables the user to point and click to cause display of information about the performance of the validated model process applied to the entire set of historical data. The information about the model performance for two independent data subsets includes at least one of: a statistical report card with a link to the statistical report chart, a cumulative lift chart with a link to the cumulative lift chart, a non-cumulative lift chart with a link to the non-cumulative lift chart. The invocation of the link to the statistical report card causes display of the statistics of model process validation. The invocation of the link to the cumulative lift chart causes display of a cumulative lift chart. The invocation of the link to the cumulative lift chart causes display of a non- cumulative lift chart. The final model and the model process validation results are stored persistently. In general, in another aspect, a machine-based method includes, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, for example, displaying to a user a lift chart, monotonicity, and concordance scores associated with each step in a step-wise model fitting process. Implementations may include one or more of the following features. The user is enabled to observe changes in the fit of the model as variables associated with the data are added or removed from a predictor set of the variables. The user is enabled to terminate the fitting of the model when the fitting process reaches an optimal point. In general, in another aspect, a machine-based method includes, for a predictive model based on historical data about a system being modeled, generating measures of the fit of the model to samples of the data, the fit measures being generated separately in percentile segments.”
- CN109886747 see Abstract “Embodiments of the present invention provide a kind of Method for Sales Forecast method, medium, Method for Sales Forecast device and calculate equipment. The Method for Sales Forecast method includes: the history sales volume time series obtained in first time section; By history sales volume time series be input on ordinary days Method for Sales Forecast model to obtain the prediction sales volume time series in the second time interval; By advertising campaign information input to promotion Method for Sales Forecast model to obtain the promotion sales volume data of promotion period node; Wherein, promotion period node is located in the second time interval; It will predict that the prediction sales volume data for corresponding to promotion period node in sales volume time series replace with promotion sales volume data. Method of the invention can be realized the integrated Method for Sales Forecast to steady sales volume on ordinary days and promotion peak value sales volume, have stronger generalization ability and adaptive adjustment capability by establishing Method for Sales Forecast model on ordinary days and promotion Method for Sales Forecast model.”
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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