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 .
DETAILED ACTION
Status of Claims
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/17/2026 has been entered.
Claims 1, 10 and 19 have been amended.
Claims 1-19 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on 02/17/2026 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1, 10 and 19 rejection under 35 USC § 112 second paragraph, Applicant has amended and/ or clarified the claims. Therefore, the claim rejection of claims 1, 10 and 19 under 35 USC § 112 second paragraph is withdrawn.
With regard to claims 1-19 rejection under 35 USC § 101:
Applicant argues that “ claims 1-19 are directed to patent eligible subject matter, at least in part, because they include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. In particular, the claims are directed to improvements in machine learning, which are recognized by the courts and confirmed by the USPTO as patent eligible subject matter. See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision).
Examiner disagrees. The instant claims bear no similarity to the Ex Parte Desjardins Holdings decision, because the instant claim merely provides "[p]roduct clustering," which "finds and combines product similarities to predict demand for new and unseen products," and " train AI system... that compares different elements from multiple products and takes a percentage-based similarity calculation to determine an overall cluster"; whereas Ex Parte Desjardins (claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values). Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about 2 previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification.
Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
As such claims 1-19 are NOT directed to patent eligible subject matter, at least in part, because they do not include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. In particular, the claims are NOT directed to improvements in machine learning as in Ex Parte Desjardins. Accordingly, the claim rejection of claims 1-19 rejection under 35 USC § 101 is maintained.
Applicant argues that “the claims are directed to patent eligible subject matter under at least Step 2A, Prong Two of the U.S. Supreme Court's framework for evaluating patent eligibility (i.e., "the Alice Mayo Test") as described in the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance ("the 2019 Guidance"). As described in the 2019 Guidance, under Step 2A, Prong Two of the Alice Mayo Test, a claim reciting a judicial exception is nevertheless directed to patent eligible subject matter if it "integrates a judicial exception into a practical application," such as if it includes an additional claim element that (i) "reflects an improvement in the functioning of a computer, or an improvement to other technology or technicalfield,"1 or (ii) "implements a judicial exception 1 See, e.g., MPEP 2106.05(a); DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59 (Fed. Cir. 2014); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299 (Fed. Cir. 2018); and Core Wireless Licensing, S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018). with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim."2 See the 2019 Guidance, pages 18-19 (emphasis added). To the extent that the present claims are directed to a judicial exception (which Applicant again does not concede), the claims would nevertheless include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. For instance, the claims expressly recite computer-specific elements that are integral to the claim, such as the use of an "an artificial intelligence (AI) system" to "adaptively cluster[] ... the first data structures according to item metrics into a plurality of clusters." In particular, the "first data structures" are obtained by "converting the input data into a plurality of first data structures," including "generating a plurality of data tables, wherein each of the data tables comprises a plurality of columns, wherein each of the plurality of columns has a respective data field name and represents a respective metric of the plurality of first times." These data structures represent computer-specific elements (page 4/8)”.
Examiner disagrees. Since, adaptively cluster[] ... the first data structures according to item metrics into a plurality of clusters, the "first data structures" are obtained by "converting the input data into a plurality of first data structures," including "generating a plurality of data tables, wherein each of the data tables comprises a plurality of columns, wherein each of the plurality of columns has a respective data field name and represents a respective metric of the plurality of first times” ; are part of the abstract idea itself, they are not capable of transforming the abstract idea into a practical application under Step 2a, Prong 2 and not capable of being considered "significantly more" under Step 2b.
Only technological improvements rooted in the "additional elements" of a claim are capable of transforming an abstract idea into a practical application under Step 2a, Prong 2, and only "additional elements" are capable of being considered "significantly more" under Step 2b. Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are “computer and AI system”, which are just general-purpose computers with generic computing components upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b.
Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied.
Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract).
Furthermore, In the Enfish decision the "additional element" of the self-referential database was considered an improved database, invented by the inventor, that operated in a manner different from traditional databases. In the Core Wireless Decision, the "additional elements" of the user interface and its functions was an improved user interface, invented by the inventor, that performed a combination functions not found in traditional user interfaces. Thus, in each of these cases, the improvement to the computer technology was rooted in the "additional elements" of the claim, wherein the additional elements were implemented using software.
In contrast, the purported improvements in a computer technology by practicing the claims of the instant invention are rooted solely in the abstract idea itself which is merely applied using a general-purpose computer with generic computer components executing software which is an improvement to an abstract idea and, as such, an improvement in ineligible subject matter (see the SAP V. Investpic decision and the Recentive Analytics decision). Thus, the rejections have been maintained.
That is the additional elements of computer and AI system and/ or the the additional elements recited in the claims of “host, virtual machine, hardware storage device, artificial intelligence (AI) system, computer model” fails to i)"reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, or (ii) "implement a judicial exception into practical application. Accordingly, the claim rejection of claims 1-19 rejection under 35 USC § 101 is maintained.
Applicant argues that “In the Kim Memo, Mr. Kim indicated that, pursuant to Ex Parte Desjardins, "examples that may show an improvement in computer functionality" include at least: (i) "An improved way of training a machine learning model that protected the model's knowledge about previous tasks while allowing it to effectively learn new task," and (ii) "Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams." Kim Memo, page 4. The claimed subject matter is consistent with both of these examples. Indeed, as expressly described in the Specification, the claimed subject matter provides "[p]roduct clustering," which "finds and combines product similarities to predict demand for new and unseen products," and "involves an advanced AI system discussed ... that compares different elements from multiple products and takes a percentage-based similarity calculation to determine an overall cluster" Specification, page 9, third full paragraph. Accordingly, the system is trained in a manner that both protects its knowledge about previous tasks (e.g., to identify similarities based on previously seen products) while also allowing it to effectively learn new tasks (e.g., to "predict demand for new and unseen products") (page 6-7/8)”.
Examiner disagrees. The instant claims bear no similarity to the Ex Parte Desjardins Holdings decision, because the instant claim merely provides "[p]roduct clustering," which "finds and combines product similarities to predict demand for new and unseen products," and/ or " protects its knowledge about previous tasks (e.g., to identify similarities based on previously seen products) while also allowing it to effectively learn new tasks (e.g., to "predict demand for new and unseen products"); whereas Ex Parte Desjardins (claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values). Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about 2 previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification.
Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
As such claims 1-19 are NOT directed to patent eligible subject matter, at least in part, because they do not include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim. In particular, the claims are NOT directed to improvements in machine learning as in Ex Parte Desjardins.
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (protects its knowledge about previous tasks (e.g., to identify similarities based on previously seen products) while also allowing it to effectively learn new tasks (e.g., to "predict demand for new and unseen products") and not in the operations of any additional elements or technology.
Accordingly, the claim rejection of claims 1-19 rejection under 35 USC § 101 is maintained.
Applicant argues that “Further, the claimed subject matter provides this benefit, at least in part, by performing "performing mixed integer programming mathematic optimization," including "linearizing the target function by selectively restricting at least one of the binary variables in the target function to a value of1 in a subset of conditions, and using a computerized mixed integer programming solver to solve the linearized target function." That is, the claimed process adjusts certain parameters of a computerized model by selectively restricting certain variables to certain values, in order to address computer-specific problems with respect to the computerized model. Further, the operations are not feasible and/or are impractical for a human to perform, and allow a computer system to automatically generate graphical user interfaces in an objective manner without relying on subject human input or manual human intervention. See McRO, Inc. v. Bandai, 837 F.3d 1299, 1313-16 (holding that claims that improve the prior art by applying rules rather than human subjectivity are not "directed to" ineligible subject matter). Accordingly, even if the claims were directed to a judicial exception, the claims would nevertheless be directed to patent eligible subject matter, at least because the claims include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim (page 8/8)”.
Examiner disagrees. Since performing mixed integer programming mathematic optimization," including "linearizing the target function by selectively restricting at least one of the binary variables in the target function to a value of1 in a subset of conditions, and using a computerized mixed integer programming solver to solve the linearized target function and/ or adjusts certain parameters of a computerized model by selectively restricting certain variables to certain values”, are part of the abstract idea itself, they are not capable of transforming the abstract idea into a practical application under Step 2a, Prong 2 and not capable of being considered "significantly more" under Step 2b.
Only technological improvements rooted in the "additional elements" of a claim are capable of transforming an abstract idea into a practical application under Step 2a, Prong 2, and only "additional elements" are capable of being considered "significantly more" under Step 2b.
Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are “host, virtual machine, hardware storage device, artificial intelligence (AI) system, computer model “ which are just general-purpose computers with generic computing components upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b.
Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied.
Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.).
Furthermore, the McRO decision overcame 35 USC 101 under Step 2a, Prong 1 because they did not recite an abstract idea. While this makes an analysis under Step 2a, Prong 2 unnecessary, the court did find that the claims recite an improvement to a computer-related technology which is a consideration under Step 2a, Prong 2. Since, the claims did not recite an abstract idea, each and every limitation of the claim would be an "additional element" of the claim. The execution of these "additional elements" resulted in an improvement to a computer-related technology and, as such, would also overcome the 35 USC 101 rejection under Step 2a, Prong 2.
In contrast, the claims of the instant invention do recite an abstract idea. under Step 2a, Prong 2., the only "additional elements" of the instant claims are a general-purpose computer with generic computer components executing software. Thus, the instant claims merely apply the abstract idea using the general-purpose computer with generic computer components which is insufficient to transform an abstract idea into a practical application. Hence, the purported improvement of the instant claims is an improvement to an abstract idea which is an improvement in ineligible subject matter (see the SAP V. Investpic decision and the Recentive Analytics decision).
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (adjusts certain parameters of a computerized model by selectively restricting certain variables to certain values) and not in the operations of any additional elements or technology.
Accordingly, the claim rejection of claims 1-19 rejection under 35 USC § 101 is maintained.
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 1-19 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception subject matter, specifically an abstract idea. The analysis for this determination is explained below: Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. In this case, Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. In this case, claims 1-9 are directed to a machine (i.e. a system); claim (s) 10-18 are directed to a process (i.e. a method), and claim 19 is directed to a manufacture (i.e. a non transitory computer medium).
The claimed invention is directed to at least one judicial exception (i.e a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 for instance recite(s) the following abstract idea of: “ receiving, from a storage, input data comprising: historical data representing a plurality of first items and a plurality of stores associated with the plurality of first items; converting the input data into a plurality of first data structures, wherein converting the input data into the plurality of first data structures comprises generating a plurality of data tables, wherein each of the data tables comprises a plurality of columns,wherein each of the plurality of columns has a respective data field name and represents a respective metric of the plurality of first times; configuring an algorithm to predict demand for items not presented by the historical data by adaptively clustering, the first data structures according to item metrics into a plurality of clusters, wherein clustering the first data structures comprises: determining, by the Al system, a plurality of points in N-dimensional space based on the first data structures, wherein each of the points represents a respective one of the first items, wherein determining the plurality of points in N- dimensional space comprises determining a plurality of N-dimensional vectors representing the plurality of points in the N-dimensional space,wherein for each of the dimensions of the N-dimensional vector:the dimension corresponds to a respective one of the columns of the plurality of data tables, andthe value of the vector in the dimension corresponds to a value of the data field in that column;determining, by the AI system, distances between respective pairs of the points, wherein determining distances between respective pairs of the points comprises determining distances between respective pairs of the vectors in the N- dimensional space,comparing, by the AI system, the distances to a threshold value, generating, by the AI system, the plurality of clusters based on the distances, anddetermining, by the AI system, a respective centroid for each of the plurality of clusters in the N-dimensional space, wherein each of the centroids represents each of the points in a respective one of the clusters;classifying, by the AI system, a plurality of second items into the plurality of clusters based on the centroids; for each of the plurality of clusters:determining, using a predictive computer model, a markdown plan for at least one of the first items or the second items associated with that cluster,optimizing the markdown plan with respect to one or more optimization goals and constraints for that cluster, wherein optimizing the markdown plan comprises performing a mixed integer programming mathematical optimization process with respect to a target function, wherein the target function represents margin penalized by leftover stock of at least one of the first items or the second items associated with that cluster, and wherein performing mixed integer programming mathematic optimization comprises linearizing the target function by selectively restricting at least one of the binary variables in the target function to a value of 1 in a subset of conditions, and using a computerized mixed integer programming solver to solve the linearized target function, andgenerating and storing, using the hardware storage device, a second data structure presenting the optimized markdown plan for at least one of the first items or the second items associated with that cluster. ”.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes). This judicial exception is not integrated into a practical application because the claim only recites the additional elements of “virtual machine, hardware storage device, artificial intelligence (AI) system, computer model”. The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception 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 (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. MPEP Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using the additional elements of “host, virtual machine, hardware storage device, artificial intelligence (AI) system, computer model” to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component. “Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computers communicating over a general purpose network (as evidenced from page 2/25) therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations are considered insignificant extra solution activity as they are directed to merely receiving, storing and/or transmitting data:
receiving, from a hardware storage device, input data comprising: historical data representing a plurality of first items and a plurality of stores associated with the plurality of first items;
storing, using the hardware storage device, a second data structure presenting the optimized markdown plan for at least one of the first items or the second items associated with that cluster.
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e.MPEP Step 2B=No).
For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same Judicial analysis is applied here to independent claims 10 and 19.
The dependent claims 2-9 and 11-19 appears to merely further limit the abstract idea of Certain methods of organizing Human Activity” as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations), by adding the additional steps of : the historical data represents historical pricing data (claims 2 and 11); stock and stock-out data regarding the plurality of first items at the one or more stores (claims 3 and 12); receiving transaction data and inventory data regarding the plurality of first items and the plurality of second items; re-optimizing, based on a react engine (claims 4 and 13); clustering the first data structures (claims 5 and 14); item information is arranged in one or more data vectors ( claims 6 and 15); data vectors represents a respective stock keeping unit (SKU) (claims 7 and 16); markdown plan represents a plurality of adjustments to a price of at least one of the first items or the second items associated with that cluster over time (claims 8 and 17); markdown plan is optimized based on a function (claims 9 and 18); which is considered part of the abstract idea and therefore only further limit the abstract idea (i.e. MPEP Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. MPEP Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. MPEP Step 2B=No). Thus, the dependent claims further narrows the abstract idea and/or recite additional elements previously rejected in the independent claims 1 and 10.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Possible Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter, none of the cited reference discloses the claimed features of independent of claims 1, 10 and19. As such, the examiner, has been unable to find prior art that discloses the combination of the claimed features. Thus, the claims contain subject matter that would be allowable over the prior art if the applicant to be able to overcome the Claim rejections under 35 USC § 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Trenholm, US Pub No: 20190019061 A1, teaches system and method for increasing data quality in a machine learning process.
Tulika, US Pub No: 20230196278 A1, teaches method and system for automatic sale forecasts using machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622