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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 07/30/2025, Applicant, on 10/30/2025, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 10/30/2025 have been fully considered, but they are not fully persuasive. The 35 USC § 102 rejection has been overcome. However, the updated 35 USC § 103 and 101 rejections of claims 1-20 are applied in light of Applicant's amendments.
The Applicant argues “Pending claim 1 is analogous to Example 37 in the 2019 Guidance. Example 37 relates to providing an improved graphical user interface that automatically moves icons based on a user selection and a specific criteria.” (Remarks 10/30/2025)
In response, the Examiner respectfully disagrees. The Applicant’s invention is not analogous to Example 37 as it does not follow the same fact pattern Example 37 is deemed eligible “by providing a method for rearranging icons on a graphical user interface (GUI), wherein the method moves the most used icons to a position on the GUI, specifically, closest to the “start” icon of the computer system, based on a determined amount of use. In a first preferred embodiment, the amount of use of each icon is automatically determined by a processor that tracks the number of times each icon is selected or how much memory has been allocated to the individual processes associated with each icon over a period of time (e.g., day, week, month, etc.)” (see, 2019 PEG). This combination of features is integrated into the technology and provides a practical application by automatically moving icon positions in the GUI; thus automatically manipulating/changing the GUI in response to a determination/calculation. Example 37 was found eligible because the claims do not recite any of the judicial exceptions enumerated in the 2019 PEG. However, the Applicant’s invention is not analogous to Example 37. The Examiner does not see any similarities to between the Applicant’s invention and Example 37, as the Applicant’s invention does not show an automatic manipulation to the GUI, and thus the Applicant’s invention does not follow a similar fact pattern (displaying calculated information on a screen is not the same). Thus, the Applicant’s invention is not analogous to Example 37.
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 firstitem, the first anomaly being a deviationfrom a threshold characteristic of the firsbased on a causal estimation value and a refutal p-value; determine… a plurality of causal estimation values, each of the plurality of causal estimate values being associated with each of the plurality of causal attribute; identify a second item based on the plurality of causal estimation values, the second item being different than the firsitem. 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; 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.; 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; 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.; 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 “generating a plurality of causal refutal values, each of the plurality of causal refutal values being associated with each of the plurality of causal estimating values and each of the plurality of causal attributes; 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 1-20 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 20200027033 to (hereinafter “Garg”) 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 comprisingand 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 obtain, from the database, the historical data, the historical data associated withcharacteristics of the first item; (Flexibility may be realized with multiple hierarchies applied to the same database, the ability to create new custom hierarchies and views, rapid addition of new measures and dimensions, and the like, para [0147-0152]);
identify a first anomaly in the historical data of the first item, the first anomaly being a deviation …of the first product 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];
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, Garg teaches:
from a threshold characteristic…; See Garg 0079-0121, “Updated local parameters determined by parameter update module 128, along with any suitable information, such as an iteration number, threshold used to determine an updated proxy parameter, normalization parameters, such as scaling vector h.sub.k and integer η.sub.k, parameter updates such as Δz.sub.k.sup.i+1 and Δu.sub.k.sup.i+1, and the like, used by or calculated by parameter update module 128 are stored in storage 120 and made available to modules of machine learning application 116. In one example, parameter update module 128 provides updated local parameters including Δz.sub.k.sup.i+1 and Δu.sub.k.sup.i+1 to parameter sending module 130 and updated model parameters w.sub.k.sup.i+1 to data serving module 132. Additionally or alternatively, parameter update module 128 provides scaling vector h.sub.k and integer η.sub.k to parameter sending module 130.”
Hunt 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 with the aforementioned teachings from Garg with a reasonable expectation of success, by adding steps that allow the software to utilize threshold data with the motivation to more efficiently and accurately organize and analyze information [Garg 0079].
As per claim 2, Hunt and Garg teach all the limitations of claim 1.
In addition, Hunt teaches:
generate a plurality of causal refutal values, each of the plurality of causal refutal values being associated with each of the plurality of causal estimating values and each of the plurality of causal attributes; filter 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 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]).
As per claim 3, Hunt and Garg 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 and Garg 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 and Garg 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 and Garg 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 and Garg 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 and Garg 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 teaches all the limitations of claim 1.
Hunt may not explicitly teach the following. However, Garg teaches:
wherein the plurality of causal estimation values are generated using double machine learning;Garg discloses wherein the plurality of causal estimation values are generated using double machine learning (Updating and synchronizing the parameters of the machine learning model on the edge servers based on client data reaching the global server induces significant latency in updating the machine learning model parameters used to serve data to a client relative to the client data used to determine the parameters, para [0018]).
Hunt 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 with the aforementioned teachings from Garg with a reasonable expectation of success, by adding steps that allow the software to utilize machine learning with the motivation to more efficiently and accurately organize and analyze data [Garg 0018].
As per claim 10, Hunt teaches all the limitations of claim 1.
Hunt 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 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 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].
Claims 11-18, and 20 is directed to the method and CRM for performing the system of claims 1-8 above. Since Hunt and Garg teach the method and CRM, the same art and rationale apply.
Claim 19 is directed to the method for performing the system of claim 9 above. Since Hunt 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.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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