DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/08/2026 has been entered.
Response to Arguments
Applicants’ arguments filed 04/08/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 and 103 rejections of claims 1-20 are applied considering Applicant's amendments.
The Applicant argues “Applicant respectfully submits that claim 1, as amended, is not directed to an abstract idea or, in the alternative, the features of claim 1 form a practical application, as described by Prong 2 of the USPTO's 2019 subject matter eligibility guidance ("2019 Guidance"). Pending claim 1 is analogous to Example 47.” (Remarks 04/08/2026)
In response, the Examiner respectfully disagrees. The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations. For instance, consider a claim directed to " generate, using a machine learning model that accounts for heterogenous treatment." While this claim appears to have a practical application, a closer examination reveals that the core of the invention is the underlying mathematical model and its training process. Even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. Furthermore, “retrain the machine learning model” recites retraining at a high level, with no particular technique for how the model is retrained or how its operation is improved, and thus amounts to mere instructions to apply the abstract idea using a generic machine learning model (MPEP 2106.05(f)). See Recentive Analytics, Inc v. Fox Corp (Fed. Cir. 2025) (“Iterative training using selected training material and dynamic adjustments based on real time changes are incident to the very nature or machine learning.”).
The Applicant has not created a new learning algorithm, but rather optimizing existing algorithm(s) or the application of known techniques to a new dataset. Such incremental advancements, while potentially valuable for business, do not automatically confer patent eligibility or a technological improvement. As highlighted in the Alice framework, the mere recitation of known components or processes does not necessarily amount to an inventive concept.
The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group within the enumerated groupings of abstract ideas; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding item/product information. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing (modeling and projecting) data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea (mathematical concepts). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for products, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding a service system, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
Applicant’s arguments with respect to the rejection to the claims of 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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 non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry, it is first noted that the method (claims 11-19), computer program product (claims 20), and system (claims 1-10) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group within the enumerated groupings of abstract ideas; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing mathematics.
The limitations reciting the abstract idea(s), as set forth in exemplary claim 1, are: obtaining… historical data, the historical data associated with characteristics of a first item; identify a first anomaly in the historical data of the firstgenerate… that accounts for heterogenous treatment effects of a plurality of causal attributes from the historical data, causal estimation values and refutal p- values for each causal attribute of the plurality of causal attributes to link the plurality of causal attributes to the first anomaly based on the causal estimation value and the refutal p-value; identify a second item based on the causal estimation values, the second item being different than the first item; and receive feedback information based on the modified interactive graphic…;Independent claims 1 and 20 recite the system and CRM for performing the method of independent claim 11 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: from a database…; A system, comprising: a database storing historical data associated with a first product; using a machine learning model… a computing device comprising a processor and a non-transitory memory storing instructions that, when executed, cause the processor to… receive a request for an interface that displays information about the first item… and automatically transmit for display an interactive graphic that includes the first anomaly… and automatically transmit for display, in response to an input from a user, a modified interactive graphic to include a second anomaly that is linked to the plurality of causal attributes… and retrain the machine learning model based on the feedback information; A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising: obtaining, from a database… (as recited in the independent claims). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: from a database…; A system, comprising: a database storing historical data associated with a first product; using a machine learning model… a computing device comprising a processor and a non-transitory memory storing instructions that, when executed, cause the processor to… receive a request for an interface that displays information about the first item… and automatically transmit for display an interactive graphic that includes the first anomaly… and automatically transmit for display, in response to an input from a user, a modified interactive graphic to include a second anomaly that is linked to the plurality of causal attributes… and retrain the machine learning model based on the feedback information; A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising: obtaining, from a database (as recited in the independent claims) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0030]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-10 and 12-19) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 12-19 “filtering the plurality of causal attributes based on a comparison of each of the plurality of causal refutal values to a predetermined refutal threshold to generate a plurality of filtered causal attributes; and ranking the plurality of filtered causal attributes based on their respective plurality of causal estimation values; comparing the historical data to a predetermined threshold to identify the first anomaly; and generating an anomaly score based on the comparison; comparing the anomaly score to a predetermined anomaly threshold; and transforming the anomaly score to a binary representation based on the comparison of the anomaly score to the predetermined anomaly threshold; retrieving, from the database, a plurality of products; generating a ranking of a plurality of causal attributes of each of the plurality of products; comparing the ranking of the plurality causal attributes for each of the plurality of products to one another; and generating a plurality of similar products based on the comparison, the plurality of similar products being a subset of the plurality of products, each similar product in the plurality of similar products having an identical ranking of causal attributes; rendering, on a user interface, at least one insight related to one causal attribute of the plurality of causal attributes, the insight including textual data linking the one causal attribute to the first anomaly; rendering, on a user interface, a selection matrix configured to receive an input from a user, the selection matrix including a selection of the plurality of causal attributes; and in response to the input from the user, generating at least one insight related to one causal attribute of the plurality of causal attributes, the insight including textual data linking the one causal attribute to the first anomaly; rendering, on a user interface, an interactive graphic including the first anomaly; in response to an input from a user, modifying the interactive graphic to include a second anomaly; aggregating the first anomaly and the second anomaly to create a set of anomalies; and linking the plurality of causal attributes to the set of anomalies; wherein the plurality of causal attributes are linked to the first anomaly using a Non-combinatorial Optimization via Trace Exponential and Augmented Lagrangian for Structure learning algorithm”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (2-10) recite the system for performing the method of claims 12-19. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-9, 11-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20080319829 (hereinafter “Hunt”) et al., in view of U.S. PGPub 20200202382 to (hereinafter “Chen”) et al., in view of U.S. PGPub 20080319829 to (hereinafter “Hunt”) et al.
As per claim 1, Hunt teaches a system, comprising:
a database storing historical data associated with a first item (For example and without limitation, the staging table 164 may contain data from which historical information has been removed, data from multiple sources has been combined or aggregated, and so on, para [0356], Fig. 1);
a computing device comprising
identify a first anomaly in the historical data of the first item, the first anomaly being a deviation from a threshold characteristic of the first item, and automatically transmit for display an interactive graphic that includes the first anomaly; (The random effect measures how marketing response at a lower geographical level may deviate from total US (fixed) effect. Every time a marketing mix model is updated, the system will provide the user with a wide range of model diagnostics, such as goodness of fit, co-linearity between the independent variables, model stability, validation, standard errors of independent variables, residual plots, and the like, para [1151]). Hunt teaches, models and the like may be placed in competition, and anomalies between their performance used to optimize the models, and/or create a new model or plurality of models, para [0439]; 1151: “The random effect measures how marketing response at a lower geographical level may deviate from total US (fixed) effect…0417: Sales performance threshold amount, Sales performance threshold quantity, Sales performance threshold %, Sales performance variance amount, Sales performance variance %, Compensation amounts, Projected compensation amount, Target Sales Volume, Target Sales Units, Target Sales Dollars, Target Category Share, and the like.”
link a plurality of causal attributes to the first anomaly based on a causal estimation value and a refutal p-value; (In some embodiments, the promotion characteristic data set may include data attributes associated with the fact data stored in the promotion characteristic data set, para [1169]); Hunt teaches components of the bulk data extraction solution may include manual bulk data extraction, specific measure set and casuals, enabled client stubs, custom aggregates for product dimension, incorporation of basic SCI adjustments, adding additional causal fact sets, batch data request API, incorporation of new projections, or the like, para [0240]).
determine, by the processor, a plurality of causal estimation values, each of the plurality of causal estimate values being associated with each of the plurality of causal attribute; (The measure for a retail channel may be a growth opportunity channel, presented by fiscal quarter, presented by year, presented by month, presented by week, segmented by a product attribute, segmented by a consumer attribute, segmented by a venue, segmented by a time, segmented by a vendor, segmented by a manufacturer, segmented by a retailer, segmented by store, wherein the measure for a retail channel is an estimate of a consumer activity within a retail channel, and the like, para [1322]); and
identify a second product based on the plurality of causal estimation values, the second product being different than the first product (Product portfolio analysis may comprise comparing new product performance versus distribution to identify opportunities for rebalancing product portfolio and sales and marketing investments, para [0226]).
automatically transmit for display, in response to an input from a user, a modified interactive graphic to include a second anomaly that is linked to the plurality of causal attributes; See Hunt 0326-0400, “Generally, in embodiments, scanner-data-based products and services may primarily use two sources of data--movement data and causal data. Movement data may contain scanner-based information regarding unit sales and price. Based on these data, it may be possible to calculate volumetric measures (such as and without limitation sales, price, distribution, and so on). Causal data may contain detailed information in several types of promotions including--without limitation--price reductions, features, displays, special packs, and so on. In practice, information about the incidence of some of these types of promotions (i.e., price reductions and special packs) may be deduced from the scanner data. Also in practice, a field collection staff may gather information about other types of promotions (i.e. features and displays).”
Hunt may not explicitly teach the following. However, Chen teaches:
generate, using a machine learning model that accounts for heterogenous treatment effects of a plurality of causal attributes from the historical data, causal estimation values and refutal p- values for each causal attribute of the plurality of causal attributes to link [[a]] the plurality of causal attributes to the first anomaly based on [[a]] the causal estimation value and [[a]] the refutal p-value;0074: “ There are two estimates performed by the system: (a) estimating average treatment effects, and (b) estimating heterogeneous treatment effects. In both cases the present system estimates the requisite causal effects using meta-algorithms that are built on top of trained Machine Learning models…0138: Match attribution server 330 uses a sensitivity test on the average treatment effects. The sensitivity test computes the pvalue that the estimated ATE is nonzero as a function of how badly the RCT assumptions deteriorate. If the resulting pvalues are small, there is more evidence that the ad campaign has a nonzero effect on conversion.
Hunt and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hunt with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to utilize machine learning data with the motivation to more efficiently and accurately organize and analyze information [Chen 0074].
Hunt and Chen may not explicitly teach the following. However, Bates teaches:
and receive feedback information based on the modified interactive graphic, and retrain the machine learning model based on the feedback information.
0016: “Feedback from the user provides reinforced learning to the machine learning models…0138: The user is also provided an opportunity to provide feedback indicating whether or not the alert is valuable to the user. The feedback is provided via a feedback control or button 930.”
Hunt, Chen, and Bates are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hunt and Chen with the aforementioned teachings from Bates with a reasonable expectation of success, by adding steps that allow the software to utilize machine learning data with the motivation to more efficiently and accurately organize and analyze information [Bates 0016].
As per claim 2, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
filter the plurality of causal attributes based on a comparison of each of a plurality of causal refutal values to a predetermined refutal threshold to generate a plurality of filtered causal attributes; and rank the plurality of filtered causal attributes based on their respective plurality of causal estimation values; (Hunt teaches components of the bulk data extraction solution may include manual bulk data extraction, specific measure set and casuals, enabled client stubs, custom aggregates for product dimension, incorporation of basic SCI adjustments, adding additional causal fact sets, batch data request API, incorporation of new projections, or the like, para [0240 and 0278]).
As per claim 3, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
compare the historical data to a predetermined threshold to identify the first anomaly; and generate an anomaly score based on the comparison; (Hunt teaches, models and the like may be placed in competition, and anomalies between their performance used to optimize the models, and/or create a new model or plurality of models, para [0439]).
As per claim 4, Hunt, Chen, and Bates teach all the limitations of claim 3.
In addition, Hunt teaches:
compare the anomaly score to a predetermined anomaly threshold; and transform the anomaly score to a binary representation based on the comparison of the anomaly score to the predetermined anomaly threshold; (Hunt teaches, a binary facility 128 may be associated with the data mart 118. The binary 128 or bitmap index may be generated in response to a user input, such as and without limitation a specification of which dimension or dimensions should be flexible, para [0166], Fig. 1).
As per claim 5, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
retrieve, from the database, a plurality of products; generate a ranking of a plurality of causal attributes of each of the plurality of products; compare the ranking of the plurality causal attributes for each of the plurality of products to one another; and generate a plurality of similar products based on the comparison, the plurality of similar products being a subset of the plurality of products, each similar product in the plurality of similar products having an identical ranking of causal attributes; (Hunt teaches, stakeholder reports may provide detailed evaluation and sales performance insights for each stakeholder (e.g., sales representatives, managers and executives) including plan tracking, account, product and geography snapshots, sales report cards, performance rankings, leader and laggard reporting, account and category reviews, para [0278]).
As per claim 6, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
render, on a user interface, at least one insight related to one causal attribute of the plurality of causal attributes, the insight including textual data linking the one causal attribute to the first anomaly; (Hunt teaches, a graphical user interface may be operatively coupled to or otherwise associated with the analytic server 134 so as to provide a user with a way of visually making the definition, para [0340], Fig. 1).
As per claim 7, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
render, on a user interface, a selection matrix configured to receive an input from a user, the selection matrix including a selection of the plurality of causal attributes; and in response to the input from the user, generate at least one insight related to one causal attribute of the plurality of causal attributes, the insight including textual data linking the one causal attribute to the first anomaly; (Hunt teaches, in certain optional embodiments the data mart 114 may include one or more of a security facility 118, a granting matrix 120, a data perturbation facility 122, a data handling facility, a data tuples facility 124, a binary handling facility 128, a dimensional compression facility 129, a causal bitmap fake facility 130 located within the dimensional compression facility 129, a sample/census integration facility 132 or other data manipulation facilities, para [0156], Fig. 1).
As per claim 8, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Hunt teaches:
render, on a user interface, an interactive graphic including the first anomaly; in response to an input from a user, modify the interactive graphic to include a second anomaly; aggregate the first anomaly and the second anomaly to create a set of anomalies; and link the plurality of causal attributes to the set of anomalies; (Hunt teaches, the analytic server 134 may be a scalable server that is capable of data integration, modeling and analysis. It may support multidimensional models and enable complex, interactive analysis of large datas, para [0172], Fig. 1).
As per claim 9, Hunt, Chen, and Bates teach all the limitations of claim 1.
In addition, Chen teaches:
wherein the causal estimation values are generated using double machine learning;0073: “the present system uses a Double Machine Learning model provides a regression-based estimator using a two-stage procedure: (a) first train a response model and treatment propensity model using machine learning algorithms, and (b) run linear regression on the out-of-sample residuals. This model is also based on observational causal inference. This is also applied to static-time data rolled-up to the user-level.”
Hunt and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hunt with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to utilize machine learning data with the motivation to more efficiently analyze data (Chen 0073).
Claims 11-18, and 20 are directed to the method and CRM for performing the system of claims 1-8 above. Since Hunt, Chen, and Bates teach the method and CRM, the same art and rationale apply.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20080319829 (hereinafter “Hunt”) et al., in view of U.S. PGPub 20200202382 to (hereinafter “Chen”) et al., in view of U.S. PGPub 20080319829 to (hereinafter “Hunt”) et al., in view of U.S. PGPub 20200027033 to (hereinafter “Garg”) et al.
As per claim 10, Hunt teaches all the limitations of claim 1.
Hunt may not explicitly teach the following. However, Garg teaches:
Hunt, Chen, and Bates may not explicitly teach the following. However, Garg teaches:
wherein the plurality of causal attributes are linked to the first anomaly using a Non-combinatorial Optimization via Trace Exponential and Augmented Lagrangian for Structure learning algorithm;Garg discloses wherein the plurality of causal attributes are linked to the first anomaly using a Non-combinatorial Optimization via Trace Exponential and Augmented Lagrangian for Structure learning algorithm (Optimization of the machine learning model is cast in a Lagrangian form that represents a constraint as a penalty, and the optimization is solved locally by each edge server based on fresh data at the edge server. Based on the Lagrangian form, parameters of the machine learning model include a proxy parameter that represents the machine learning model at convergence, para [0022]).
Hunt, Chen, Bates, and Garg are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Hunt, Chen, and Bates with the aforementioned teachings from Garg with a reasonable expectation of success, by adding steps that allow the software to utilize linking data with the motivation to more efficiently and accurately organize and analyze data [Garg 0022].
Claim 19 is directed to the method for performing the system of claim 10 above. Since Hunt, Chen, Bates, and Garg teach the method, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bateni; Arash. METHODS AND SYSTEMS FOR FORECASTING PRODUCT DEMAND USING A CAUSAL METHODOLOGY, .U.S. PGPub 20080154693 The present invention relates to methods and systems for forecasting product demand for retail operations, and in particular to the forecasting of future product demand for products experiencing price changes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625
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