DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims 19 and 20 are new.
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 03/28/2025 (original papers) and 05/28/2025 (preliminary amendment) and 07/28/2026 (currently filed amendment) (effective filing date 09/29/2022).
Information Disclosure Statement
The information disclosure statement(s) submitted: 05/13/2025 and 04/08/2026, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on 03/28/2025 as modified by the amendments filed on 05/28/2025 (preliminary amendment) and 07/28/2026 (currently filed amendment).
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims 1-20 are potentially allowable over the prior-art subject to 35 USC §101 (patent eligibility) rejection below. Independent claims 1, 7, and 13 all contain the same inventive scope. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed: Claim 1. (Currently Amended) A method executed by a computing device for determining load distribution of computing resources in a computing device cluster using a simplex method, wherein the method comprises: receiving a computing-task allocation requirement for allocating computing tasks among computing devices in the computing device cluster, wherein the computing-task allocation requirement comprises an objective function and corresponding constraint conditions representing the computing-task allocation requirement; and in a process of solving the objective function using the simplex method, performing a current iteration of the simplex method according to a first pricing strategy; determining an objective improvement of the objective function representing the computing-task allocation requirement resulting from the current iteration; selecting, from a plurality of pricing strategies of the simplex method and based on the objective improvement, a second pricing strategy configured to produce a greater objective improvement during a subsequent iteration of the simplex method; and performing the subsequent iteration of the simplex method according to the second pricing strategy; and after solving the objective function, determining, based on the solution of the objective function, the load distribution of the computing resources by allocating the computing tasks among the computing devices in the computing device cluster.
The closest prior-art (Boyd et al. 2005/0256778, Guthrie et al. 2012/0059680, Mohanty et al. 2013/0166355, LPAKO, Wunderling 2013/0036085, Kalagnanam et al. 2013/0238546) teach the features as disclosed in Non-final Rejection (04/28/2026), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining load distribution of computing resources in a computing device cluster using a simplex method, wherein the method comprises: receiving a computing-task allocation requirement for allocating computing tasks among computing devices in the computing device cluster, wherein the computing-task allocation requirement comprises an objective function and corresponding constraint conditions representing the computing-task allocation requirement; and in a process of solving the objective function using the simplex method, performing a current iteration of the simplex method according to a first pricing strategy; determining an objective improvement of the objective function representing the computing-task allocation requirement resulting from the current iteration; selecting, from a plurality of pricing strategies of the simplex method and based on the objective improvement, a second pricing strategy configured to produce a greater objective improvement during a subsequent iteration of the simplex method; and performing the subsequent iteration of the simplex method according to the second pricing strategy; and after solving the objective function, determining, based on the solution of the objective function, the load distribution of the computing resources by allocating the computing tasks among the computing devices in the computing device cluster.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
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 a judicial exception (an abstract idea) without significantly more. The following analysis applies the framework of Alice Corp. v. CLS Bank Int'l, 573 U.S. 208 (2014), and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012), as implemented in MPEP § 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance.
I. INDEPENDENT CLAIMS 1, 7, AND 13
Claims 1 (method), 7 (apparatus comprising a processor and memory), and 13 (non-transitory computer-readable storage medium) recite substantially identical subject matter directed to the same underlying operations, differing only in statutory category and generic hardware recitation. They are addressed together; the analysis applies equally to each.
Step 1 (MPEP § 2106.03) — Statutory Category
Claim 1 recites a "method executed by a computing device," and is thus a process. Claim 7 recites an "apparatus" comprising "a processor" and "a memory," and is thus a machine. Claim 13 recites a "non-transitory computer-readable storage medium comprising computer program instructions," and is thus an article of manufacture. Each therefore falls within a statutory category under 35 U.S.C. § 101, and Step 1 is satisfied for all three claims. The analysis proceeds to Step 2A.
Step 2A, Prong One (MPEP § 2106.04) — Does the Claim Recite an Abstract Idea?
Claim 1 recites the following limitations, which, under their broadest reasonable interpretation, set forth mathematical relationships, mathematical formulas, and mathematical calculations:
"an objective function and corresponding constraint conditions representing the computing-task allocation requirement";
"performing a current iteration of the simplex method according to a first pricing strategy";
"determining an objective improvement of the objective function ... resulting from the current iteration";
"selecting, from a plurality of pricing strategies of the simplex method and based on the objective improvement, a second pricing strategy configured to produce a greater objective improvement during a subsequent iteration of the simplex method"; and
"after solving the objective function, determining, based on the solution of the objective function, the load distribution of the computing resources."
The "simplex method" is a well-known mathematical algorithm for solving linear-programming optimization problems (i.e., maximizing or minimizing an "objective function" subject to "constraint conditions"), and "performing an iteration" of the simplex method "according to a ... pricing strategy" refers to the mathematical rule (e.g., Dantzig's rule, Bland's rule, or another pivoting/pricing rule) used to select which variable enters or leaves the basis at each iteration — a mathematical relationship/calculation at the heart of the algorithm. "Determining an objective improvement ... resulting from the current iteration" and "selecting ... a second pricing strategy configured to produce a greater objective improvement" are mathematical comparisons and calculations used to choose among known mathematical pivoting rules. These limitations therefore fall within the "mathematical concepts" grouping of abstract ideas identified in MPEP § 2106.04(a)(2)(I) (mathematical relationships, mathematical formulas or equations, and mathematical calculations).
This conclusion is consistent with controlling precedent addressing claims to mathematical optimization algorithms and the use of a computed value to select or adjust a subsequent parameter or step. :
Parker v. Flook, 437 U.S. 584, 594–95 (1978) (a claim reciting a mathematical formula for computing an updated "alarm limit," and using that computed value to trigger a subsequent step, is directed to an unpatentable mathematical algorithm; "the discovery of [a] phenomenon cannot support a patent unless there is some other inventive concept in its application").
Bilski v. Kappos, 561 U.S. 593, 611 (2010) (claims to a mathematical formula for hedging risk are directed to an unpatentable abstract idea).
SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167–68 (Fed. Cir. 2018) ("[A] process of gathering and analyzing information of a specified content, then displaying the results, ... is abstract as an ancillary part of such data collection and analysis[,] ... [and even] a claim purporting to improve [a mathematical] technique itself, e.g., in a data-manipulation process by making it more efficient or precise ... is directed to the abstract idea itself.").
Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350–51 (Fed. Cir. 2014) (a process of combining data through mathematical relationships to produce a new data set, without more, is an unpatentable abstract idea).
In re Killian, 45 F.4th 1373, 1381–83 (Fed. Cir. 2022) (claims reciting a mathematical algorithm applied to determine an output used to inform or trigger a subsequent action are directed to an abstract idea absent an additional element reflecting a specific technical improvement).
Claim 1 further recites "determining, based on the solution of the objective function, the load distribution of the computing resources by allocating the computing tasks among the computing devices." This limitation recites only the result to be achieved from the mathematical calculation — an allocation determined "based on the solution" — without reciting any specific technical mechanism by which that allocation is implemented, communicated to, or enforced upon the computing devices. Reciting the use of a mathematical result to make or inform a downstream determination, without more, does not remove a claim from the mathematical-concepts grouping. Flook, 437 U.S. at 594–95; In re Killian, 45 F.4th at 1382 (use of a mathematical formula's output to select or generate a subsequent parameter remains part of the abstract idea).
Because claim 1, considered as a whole, recites limitations that fall into the mathematical-concepts grouping of MPEP § 2106.04(a)(2)(I), and these limitations are the focus of the claim rather than being merely incidental to it, claim 1 recites an abstract idea. Claims 7 and 13 recite the identical mathematical relationships and calculations (differing only in that claim 7 recites a "processor" configured to "invoke ... instructions" to perform the steps and claim 13 recites "computer program instructions" that, when executed, perform the steps), and recite an abstract idea for the same reasons. The analysis proceeds to Prong Two for all three claims.
Step 2A, Prong Two (MPEP §§ 2106.04(d), 2106.05(a)–(c), (e)) — Integration into a Practical Application
The additional elements recited in claim 1 beyond the mathematical concept itself are: (i) a "computing device"; (ii) a "computing device cluster"; and (iii) "computing devices" among which "computing tasks" are "allocat[ed]." Claim 7 additionally recites a generic "processor" and "memory ... configured to store ... instructions." Claim 13 additionally recites a generic "non-transitory computer-readable storage medium" and a "computing device cluster."
Generic computer components performing generic functions. The "computing device," "processor," "memory," and "computing device cluster" are recited at a high level of generality and are described only in terms of their well-understood, generic functions of storing instructions, executing instructions, and receiving/processing data ("receiving a computing-task allocation requirement," "performing a current iteration," "determining," "selecting"). No specific hardware architecture, data structure, or technical mechanism is recited for how the "computing device cluster" carries out the claimed mathematical operations, or for how the resulting allocation is communicated to or executed upon the individual computing devices. Merely using generic computer hardware as a tool to perform mathematical calculations does not integrate a mathematical concept into a practical application. MPEP § 2106.05(f); SAP Am., 898 F.3d at 1170 ("[C]laiming the improved speed or efficiency inherent with applying [a mathematical process] on a computer" does not integrate the abstract idea, "because [that] speed and efficiency [come] not from a[ny] improved computer or network, but from the use of [the] mathematics" itself.).
Result-oriented "post-solution" application. The final limitation — "determining ... the load distribution ... by allocating the computing tasks among the computing devices in the computing device cluster" — is post-solution activity: it recites only that the mathematical result (the "solution of the objective function") is used to determine an allocation, without reciting any particular technical means of implementing that allocation (e.g., a specific scheduling algorithm, a specific data structure for tracking device availability or load, or any specific communication protocol for effectuating task assignment across the cluster). Simply appending a generic, unspecified application of a mathematical result to a field of use — here, distributing tasks in a computer cluster — is insufficient to integrate a mathematical concept into a practical application. Flook, 437 U.S. at 590 ("The notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance."); Bilski, 561 U.S. at 611–12; MPEP § 2106.05(g).
Distinguishable from Diamond v. Diehr. Unlike the claims held eligible in Diamond v. Diehr, 450 U.S. 175, 187 (1981), which recited that a computed cure time (derived from the Arrhenius equation) was used to automatically and continuously open a specifically identified rubber-molding press at the calculated moment, thereby directly controlling a specific physical apparatus to achieve a physical transformation of raw, uncured rubber, claim 1 does not recite any comparably specific control of, or transformation effected upon, any physical apparatus. The claim recites only a generic, functionally described "allocat[ion]" of "computing tasks among computing devices," without identifying any particular physical mechanism (e.g., a specific scheduler, dispatcher, or hardware controller) through which that allocation is carried out, and without reciting that the allocation itself effects any specific physical or technical change in the operation of the cluster (e.g., a measurable change in processing latency, throughput, or power consumption tied to a specifically claimed technical mechanism). The claim is accordingly more analogous to the ineligible post-solution "alarm limit" adjustment in Flook than to the eligible, physically transformative press-control step in Diehr.
No recited improvement to computer functionality. Claim 1 does not recite any improvement to the functioning of the computing devices, the cluster, or any communication mechanism between them (contrast MPEP § 2106.05(a), citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016)). While the Specification may assert that dynamically switching pricing strategies improves the speed of solving the objective function, any such improvement is, at most, an improvement to the mathematical algorithm itself (i.e., faster convergence of the simplex method), which is not the same as a technical improvement to the computer or network executing that algorithm. SAP Am., 898 F.3d at 1168 ("[E]ven if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract."); see also Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016).
No particular machine, no transformation. The claims are not tied to any particular machine beyond generic computing hardware described functionally, and do not themselves effect a transformation of an article to a different state or thing; any "transformation" is of numerical/data values (an "objective function," a "strategy reference value") rather than a physical article. MPEP § 2106.05(b), (c).
Because the additional elements, viewed individually and as an ordered combination, amount to no more than generic computer implementation of a mathematical algorithm together with an unspecified, result-oriented post-solution application of that algorithm's output, claims 1, 7, and 13 do not integrate the recited mathematical concept into a practical application. The claims are therefore directed to an abstract idea, and the analysis proceeds to Step 2B.
Step 2B (MPEP § 2106.05) — Inventive Concept ("Significantly More")
Considered individually, the additional elements (a generic computing device/computing device cluster in claim 1; a generic processor and memory in claim 7; and a generic non-transitory computer-readable storage medium in claim 13) are well-understood, routine, and conventional computing components used for their ordinary purposes of storing and executing instructions and receiving and outputting data. Executing a mathematical algorithm on a general-purpose processor and using the resulting computed value to inform a subsequent allocation decision, are conventional computer functions. See Alice, 573 U.S. at 225–26; SAP Am., 898 F.3d at 1170 (invoking a computer merely as a tool to perform a mathematical process more quickly does not supply an inventive concept, because the speed/efficiency gain flows from the mathematics, not from any improvement to the computer).
Considered as an ordered combination, the claimed steps — receive an allocation requirement comprising an objective function and constraints, perform an iteration of the simplex method under a first pricing strategy, compute an objective improvement, select a second pricing strategy based on that computed value, perform a subsequent iteration under the second strategy, and determine an allocation based on the ultimate solution — do no more than apply, in their ordinary and expected sequence, the well-known simplex optimization algorithm together with a conventional strategy-selection/comparison step, followed by a generic, unspecified application of the algorithm's output to a real-world allocation problem. This ordered combination does not achieve any claimed technological result beyond the mathematics itself. BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290–91 (Fed. Cir. 2018) (sequencing conventional/abstract steps in a logical order does not supply an inventive concept); buySAFe, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
To the extent Applicant contends that dynamically switching among simplex pricing strategies based on a computed "objective strategy reference value" was not, at the time of filing, a well-understood, routine, or conventional technique for solving linear-programming problems, that determination raises a question of fact under Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018). However, the Specification as filed does not identify the "computing device," "processor," "memory," or "user terminal" as anything other than generic, off-the-shelf computing hardware used for its known purpose, and the claims themselves recite the pricing-strategy-selection technique only in purely mathematical/functional terms (a weighted calculation of an "objective improvement," a "basis exchange degeneracy," and an "execution duration proportion"), without any technical implementation detail beyond the underlying mathematics. No factual support has been identified establishing that the claimed hardware, or its arrangement, was unconventional; any unconventionality argued by Applicant appears to reside solely in the mathematical strategy-selection technique itself, which — under SAP America — does not supply an inventive concept even where the mathematical technique is itself asserted to be novel or more efficient.
Accordingly, claims 1, 7, and 13 do not recite significantly more than the abstract idea itself, and are rejected under 35 U.S.C. § 101.
II. DEPENDENT CLAIMS 2–6, 8–12, AND 14–18
Claims 2–6 depend from claim 1; claims 8–12 depend from claim 7 and recite substantially the same limitations as claims 2–6, respectively, adapted to apparatus form; and claims 14–18 depend from claim 13 and likewise recite substantially the same limitations as claims 2–6, adapted to storage-medium form. These parallel claim sets are addressed together below by subject matter; each is rejected for the reasons given with respect to its counterpart(s), for at least the reasons set forth above with respect to the corresponding independent claim, because none remedies the deficiencies identified above.
Claims 2, 8, and 14 (determining a "basis exchange degeneracy" and an "execution duration proportion," and selecting the second pricing strategy "based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion") recite further mathematical calculations (a degeneracy metric and a duration ratio) and a further mathematical, multi-factor selection criterion, falling within the same mathematical-concepts grouping as claim 1, with no additional element beyond the same generic computing hardware. MPEP § 2106.04(a)(2)(I).
Claims 3, 9, and 15 ("determining an objective strategy reference value based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion," and determining the second pricing strategy "based on a correspondence between a strategy reference value and a pricing strategy") recite a further mathematical calculation (deriving a composite reference value) and a mathematical/table look-up correspondence, which is itself an abstract data-association operation. Digitech, 758 F.3d at 1350–51; Elec. Power Grp., 830 F.3d at 1354.
Claims 4, 10, and 16 ("obtaining a first weight coefficient ..., a second weight coefficient ..., and a third weight coefficient," and "performing weighted calculation ... to obtain the objective strategy change value") recite an express mathematical formula — a weighted-sum calculation — which is a paradigmatic mathematical relationship/calculation under MPEP § 2106.04(a)(2)(I), implemented with no additional technical element.
Claims 5, 11, and 17 ("the first weight coefficient is greater than the second weight coefficient, and the second weight coefficient is greater than the third weight coefficient") recite a purely mathematical relationship (an inequality/ordering among coefficients) with no additional element whatsoever.
Claims 6, 12, and 18 ("sending the second pricing strategy to a user terminal," "receiving a pricing strategy confirmation notification," and "solving the objective function according to the second pricing strategy") add only conventional data transmission/receipt to and from a generic "user terminal" — well-understood, routine, and conventional network communication functions. buySAFe, 765 F.3d at 1355 (sending and receiving information over a network is purely conventional); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015).
None of claims 2–6, 8–12, or 14–18, whether considered individually or in combination with their respective independent claim's limitations, integrates the recited mathematical concept into a practical application or supplies an inventive concept sufficient to transform the claim into patent-eligible subject matter. These claims are therefore rejected under 35 U.S.C. § 101.
III. DEPENDENT CLAIMS 19 AND 20
Claim 19 depends from claim 1, and claim 20 depends from claim 7; both recite that "determining the load distribution comprises generating, based on the solution of the objective function, an allocation of respective computing tasks to respective computing devices in the computing device cluster."
This limitation further describes, at the same high level of generality as the parent claims, the post-solution application of the mathematical result — an "allocation" is "generat[ed]" "based on the solution" — without reciting any specific technical mechanism for generating that allocation (e.g., a particular scheduling algorithm, data structure, or communication mechanism for assigning tasks to specific devices). This remains insignificant, result-oriented post-solution activity of the type held ineligible in Flook, 437 U.S. at 590, and does not integrate the mathematical concept into a practical application or supply an inventive concept, for the same reasons discussed above with respect to independent claims 1 and 7. MPEP § 2106.05(g).
Claims 19 and 20 are therefore rejected under 35 U.S.C. § 101.
IV. SUMMARY / CONCLUSION
Claims 1–20 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (a mathematical concept comprising mathematical relationships, formulas, and calculations — namely, iteratively solving an objective function using the simplex method and dynamically selecting among pricing strategies based on weighted mathematical calculations of an objective improvement, a basis exchange degeneracy, and an execution duration proportion) without significantly more. The additional elements recited — a generic computing device, computing device cluster, processor, memory, non-transitory storage medium, and user terminal — are generic computing and network components performing only their well-understood, routine, and conventional functions of executing instructions, storing data, and communicating over a network, and the claimed "allocat[ion]" of computing tasks based on the mathematical solution is unclaimed, result-oriented post-solution activity. Whether considered individually or as an ordered combination, these additional elements do not integrate the abstract idea into a practical application or provide an inventive concept sufficient to transform the claims into patent-eligible subject matter. Alice, 573 U.S. 208; Mayo, 566 U.S. 66; Flook, 437 U.S. 584; SAP Am., 898 F.3d 1161; MPEP §§ 2106–2106.05.
Applicant may overcome this rejection by amending the claims to recite a specific technical mechanism by which the computed allocation is implemented and that produces a specific, claimed technical effect on the operation of the computing device cluster (e.g., a specific task-dispatch or scheduling mechanism tied to measurable hardware performance parameters), consistent with MPEP § 2106.05(a) and cases such as Diamond v. Diehr, 450 U.S. 175, and Enfish, 822 F.3d 1327, and/or by identifying, with support in the Specification as filed, a specific unconventional hardware arrangement that was not well-understood, routine, or conventional at the time of filing, consistent with Berkheimer, 881 F.3d 1360.
----- Examiner’s Response to Arguments -----
Examiner’s Response: Claim Rejections – 35 USC § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Applicant's arguments filed 07/28/2026, directed to amended independent claims 1, 7, and 13, have been fully considered but are not persuasive. The rejection of claims 1–20 under 35 U.S.C. § 101 is maintained for the reasons set forth below.
1. Applicant's Argument That the Amended Claims Are "No Longer Susceptible to Being Characterized as Generally Directed to 'Objective Function Solving'"
Applicant argues that, as amended, independent claims 1, 7, and 13 recite a method for "determining a load distribution of computing resources in a computing-device cluster," including receiving a computing-task allocation requirement comprising an objective function and constraint conditions, performing iterative simplex operations using "dynamically selected pricing strategies," and, after solving the objective function, "determining the load distribution ... by allocating the computing tasks among the computing devices," and that these amendments remove the claims from the abstract-idea characterization applied in the outstanding rejection.
Examiner respectfully disagrees. The limitations Applicant identifies as amended — receiving a computing-task allocation requirement comprising an objective function and constraint conditions; performing simplex iterations under a first and then a second, dynamically selected pricing strategy; and determining a load distribution "based on the solution of the objective function" by "allocating the computing tasks among the computing devices" — are the same limitations already considered in the rejection of record and addressed under Step 2A, Prong One as mathematical relationships, formulas, and calculations (the simplex method, the "objective improvement," and the pricing-strategy selection), and under Step 2A, Prong Two and Step 2B as an unclaimed, generic, result-oriented application of that mathematical output. Examiner has compared the claim language quoted and paraphrased in Applicant's remarks against the previously considered claim language and is unable to identify any new positive claim limitation reciting a specific technical mechanism — e.g., a particular task-dispatch process, scheduler, data structure, or communication protocol — by which the "allocat[ion]" of computing tasks to computing devices is actually carried out. Applicant's remarks characterize the claims as being "integrated into a practical technological application" and as "controlling the allocation of computing tasks," but attorney argument and characterization of what a claim purportedly accomplishes cannot substitute for corresponding structure or technical detail actually recited in the claim language itself. In re Van Geuns, 988 F.2d 1181, 1184 (Fed. Cir. 1993) ("[A]ttorney argument [is] not evidence" and cannot take the place of a factual or claim-language basis in the record); MPEP § 2106.04(d) (the Step 2A, Prong Two inquiry evaluates the additional elements recited in the claim, not the invention as characterized in the specification or remarks). To the extent Applicant intends to rely on claim amendments not fully reflected in the quoted remarks, Examiner invites Applicant to identify, by claim element, the specific new claim language relied upon.
2. Applicant's Argument That the Simplex Iterations Are "Integrated into a Practical Technological Application Directed to Distributed Computing Resource Management" and "Not Performed in Isolation"
Applicant argues that the simplex iterations are not performed in isolation but are integrated into "a technological operation of a computing-device cluster," and that the claims are directed to "improving the operation of a computing-device cluster."
Examiner respectfully disagrees. Reciting that the mathematical simplex calculations are performed in connection with — rather than divorced from — a "computing-device cluster," and that the ultimate numerical solution is thereafter used to "allocat[e]" tasks among the devices of that cluster, is a field-of-use and technological-environment limitation, not a technical improvement to the cluster itself. Limiting the application of an abstract mathematical algorithm to a particular technological field or environment, without reciting a specific technical means by which that field or environment is improved, does not integrate the abstract idea into a practical application. MPEP § 2106.05(h); Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1258–59 (Fed. Cir. 2016) (confining an abstract idea to a particular technological environment does not confer eligibility); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) ("[L]imiting the claims to [a] particular technological environment ... [is not] sufficient to transform them into patent-eligible applications of the abstract idea."). Nor does the assertion that the claims "improve the operation of a computing-device cluster" find support in claim language reciting any measurable technical effect — e.g., reduced task-completion latency, increased throughput, or reduced inter-device communication overhead — tied to a specifically claimed mechanism. As the Federal Circuit has explained in the closely analogous context of claimed improvements to a mathematical technique's efficiency, "[e]ven if a process of collecting and analyzing information is limited to particular content[,] ... that limitation does not make [it] other than abstract," and any speed or efficiency gain that flows from the underlying mathematics — as opposed to from a claimed improvement to the computer or network architecture itself — does not integrate the idea into a practical application. SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168, 1170 (Fed. Cir. 2018).
3. Applicant's Argument Regarding "Structural Features"
Applicant argues that the amended claims address the prior characterization that the claims fail to "positively recite structural features," pointing to the recitation of "a computing-device cluster, computing devices within the cluster, computing-task allocation requirements, corresponding constraint conditions, allocation of computing tasks among the computing devices, and determination of a load distribution."
Examiner respectfully disagrees that these recitations constitute structural features sufficient to integrate the claims into a practical application. A "computing-device cluster" and "computing devices" are generic hardware components recited functionally and at a high level of generality, without any structural detail (e.g., a specific interconnect topology, a specific device-selection or task-dispatch controller, or any other specifically claimed hardware arrangement) distinguishing them from any general-purpose networked computing environment. "Computing-task allocation requirements," "constraint conditions," "allocation of computing tasks," and "determination of a load distribution" are not structural features at all, but rather data (the objective function and constraints operated upon by the mathematical algorithm) and the functional result of that algorithm (an allocation/distribution), respectively. Reciting the data operated upon by a mathematical algorithm and the result produced by that algorithm — without reciting the specific technical means by which that result is produced or implemented — is precisely the type of purely functional, result-based claiming the Federal Circuit has held insufficient to supply the structural or technical specificity required to integrate a mathematical concept into a practical application. Two-Way Media Ltd. v. Comcast Cable Commc'ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017) ("The claim's requirement for functional results ... is simply a description of the concept ... not a description of how to solve it."); Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1345 (Fed. Cir. 2018) (purely functional claim language, without more, does not provide the requisite specificity).
The claims remain distinguishable from Diamond v. Diehr, 450 U.S. 175, 187 (1981), in which the claimed process recited that a computed cure time was used to automatically and continuously operate a specifically identified rubber-molding press, opening it at the calculated moment to directly effect a physical transformation of raw rubber into a cured, finished product. The pending claims recite no comparably specific structural mechanism through which the computed "load distribution" is used to control any particular device or component of the cluster; they recite only that computing tasks are, in some unspecified manner, "allocat[ed] among the computing devices" — a generic, result-oriented outcome rather than a specifically claimed technical control mechanism.
4. Applicant's General "Considered as a Whole" Argument
Applicant argues that, considered as a whole, the claims describe "improving the operation of a computing-device cluster by determining load distribution through allocation of computing tasks using adaptive simplex pricing strategies," and that the mathematical operations are "integrated into this practical technological application and are not claimed in the abstract."
Examiner respectfully disagrees for the reasons discussed in Sections 1–3 above and in the rejection of record. Considering the claims as a whole does not change the outcome: the claims recite (a) a mathematical algorithm (the simplex method, together with a mathematical, weighted-calculation-based strategy-selection technique) operating on (b) generic, functionally recited computing hardware, to produce (c) an unclaimed, generic, result-oriented "allocation" of tasks. Neither the ordered combination of these elements, nor any individual element, recites a specific technical mechanism that improves the functioning of the computing-device cluster itself, as opposed to improving (at most) the efficiency of the underlying mathematics. This is squarely the situation addressed in SAP America, where the Federal Circuit rejected the argument that a claimed improvement in the efficiency or precision of a mathematical technique, without more, integrates that technique into a practical application. 898 F.3d at 1170. Simply asserting, in remarks, that the claims are "not claimed in the abstract" does not alter the actual scope and content of the claim language, which remains directed to the abstract mathematical concept and its generic, unspecified application. In re Van Geuns, 988 F.2d at 1184.
CONCLUSION - 35 U.S.C. § 101
For the foregoing reasons, and for the reasons stated in the rejection of record, Applicant's arguments are not persuasive, and the rejection of claims 1–20 under 35 U.S.C. § 101 is maintained. Applicant is again invited to consider amending the claims to recite a specific technical mechanism by which the computed load distribution is implemented — for example, a specific task-dispatch, scheduling, or communication mechanism tied to a claimed and measurable technical effect on the operation of the computing-device cluster (e.g., a specific reduction in task-completion latency, inter-device communication overhead, or similar performance parameter directly attributable to the claimed mechanism) — together with corresponding support in the Specification as filed, consistent with the guidance of Diamond v. Diehr, 450 U.S. 175, Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016).
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Kalagnanam et al. 2013/0238546 [0002] The present disclosure relates generally to computational solution algorithms (and associated systems and methods) applied to a stochastic unit commitment problem. In one example, the computational solution algorithms (and associated systems and methods) may be applied to the energy industry.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
Y. Fu, Y. Hou, Z. Wang, X. Wu, K. Gao and L. Wang, "Distributed scheduling problems in intelligent manufacturing systems," in Tsinghua Science and Technology, vol. 26, no. 5, pp. 625-645, Oct. 2021, doi: 10.26599/TST.2021.9010009.
Wang, Yuan, Li, Hui, Ding, Zhenguo, Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization, Computational Intelligence and Neuroscience, 2018, 9745639, 21 pages, 2018. https://doi.org/10.1155/2018/9745639
THIS ACTION IS MADE FINAL
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW T. SITTNER whose telephone number is (571) 270-7137 and email: matthew.sittner@uspto.gov. The examiner can normally be reached on Monday-Friday, 8:00am - 5:00pm (Mountain Time Zone). Please schedule interview requests via email: matthew.sittner@uspto.gov
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah M. Monfeldt can be reached on (571) 270-1833.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
Claims 1, 7, 13 are rejected under 35 U.S.C. 103 as being unpatentable over: Boyd et al. 2005/0256778; in view of Guthrie et al. 2012/0059680; in further view of Mohanty et al. 2013/0166355.
19/094,696 – Claim 1. (Currently Amended) Boyd et al. 2005/0256778 teaches An objective function solving method, applied to an electronic device wherein the method comprises (Boyd et al. 2005/0256778 [0032 - strategic objective analyses] The promotion system may also perform strategic objective analyses in assessing and achieving strategic corporate objectives. A user generally does not know if 1) an objective is obtainable, and 2) how strategically she should approach achieving this objective using promotional incentives. Promotion system 100 can solve this problem by identifying 1) if the revenue target is feasible, and 2) if the target is feasible, what promotional incentive level will maximize profitability given this constraint. [0155 - communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet] In one embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server. In particular, the user may be in communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet, a Value Added Network ("VAN"), VPN and the like. The Internet browser may be, for example, Netscape Navigator or Microsoft Internet Explorer. Those skilled in the art will recognize that this invention may be physically implemented in a number of ways. [0246-0249 - objective function][0260]): receiving a solving requirement input by a user (Boyd et al. 2005/0256778 [0002 - configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0024 - illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze] As illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze promotion schemes. Among the inputs received by various embodiments of the promotion pricing system 100 are product information, consumer account information, commercial channel information, purchase/sales order information, competitor and competitor product information, and promotion/campaign information. [0031 - system 100 can solve this type of problem given certain inputs such as…] Another functionality of the promotion system 100 is mark-down optimization. A retailer may receive shipments of excess inventory to their stores. The retailer knows how much of this inventory is normally sold within a given period of time given historical information and general business knowledge. However, they do not know the optimal discount to set to achieve the objective of selling that inventory within the specified time period. In other words, the user does not want to overdiscount a product. promotion system 100 can solve this type of problem given certain inputs such as the target product, the total initial inventory for that product, and the amount of inventory that is to be sold for a given period. Promotion system 100 would then compute that discount which maximizes profit while clearing pre-identified excess inventory during the specified period.), wherein the solving requirement comprises an objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]); determining to solve the objective function using a simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]); and in a process of solving the objective function using the simplex method, after solving the objective function according to a first pricing strategy using the simplex method (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), determining, based on an objective improvement (Boyd et al. 2005/0256778 [0002; 0006 – improve the accuracy of the pricing optimizations calculations][0297 - algorithm accepts not only the movements improving the objective function, but also the movements corresponding to a deterioration in the objective function value]) on the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) by current solving in the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), a second pricing strategy for solving the objective function in a next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 7. (Original) Boyd et al. 2005/0256778 further teaches An objective function solving apparatus (Boyd et al. 2005/0256778 [0032 - strategic objective analyses] The promotion system may also perform strategic objective analyses in assessing and achieving strategic corporate objectives. A user generally does not know if 1) an objective is obtainable, and 2) how strategically she should approach achieving this objective using promotional incentives. Promotion system 100 can solve this problem by identifying 1) if the revenue target is feasible, and 2) if the target is feasible, what promotional incentive level will maximize profitability given this constraint. [0155 - communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet] In one embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server. In particular, the user may be in communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet, a Value Added Network ("VAN"), VPN and the like. The Internet browser may be, for example, Netscape Navigator or Microsoft Internet Explorer. Those skilled in the art will recognize that this invention may be physically implemented in a number of ways. [0246-0249 - objective function][0260]) comprising a processor, a memory (Boyd et al. 2005/0256778 [0034 - database]) , wherein the memory is configured to store one or more instructions (Boyd et al. 2005/0256778 [0241; Claim 1]), and the processor is configured to invoke the one or more instructions in the memory (Boyd et al. 2005/0256778 [0155 - embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server]) to: receive a solving requirement input by a user (Boyd et al. 2005/0256778 [0002 - configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0024 - illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze] As illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze promotion schemes. Among the inputs received by various embodiments of the promotion pricing system 100 are product information, consumer account information, commercial channel information, purchase/sales order information, competitor and competitor product information, and promotion/campaign information. [0031 - system 100 can solve this type of problem given certain inputs such as…] Another functionality of the promotion system 100 is mark-down optimization. A retailer may receive shipments of excess inventory to their stores. The retailer knows how much of this inventory is normally sold within a given period of time given historical information and general business knowledge. However, they do not know the optimal discount to set to achieve the objective of selling that inventory within the specified time period. In other words, the user does not want to overdiscount a product. promotion system 100 can solve this type of problem given certain inputs such as the target product, the total initial inventory for that product, and the amount of inventory that is to be sold for a given period. Promotion system 100 would then compute that discount which maximizes profit while clearing pre-identified excess inventory during the specified period.), wherein the solving requirement comprises an objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]); determine to solve the objective function using a simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]); and in a process of solving the objective function using the simplex method, after solving the objective function according to a first pricing strategy using the simplex method (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), determine, based on an objective improvement (Boyd et al. 2005/0256778 [0002; 0006 – improve the accuracy of the pricing optimizations calculations][0297 - algorithm accepts not only the movements improving the objective function, but also the movements corresponding to a deterioration in the objective function value]) on the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) by the current solving in the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), a second pricing strategy for solving the objective function in a next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 13. (Currently Amended) Boyd et al. 2005/0256778 further teaches A non-transitory computer-readable storage medium comprising computer program instructions (Boyd et al. 2005/0256778 [0155 - embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server]), wherein when the computer program instructions are executed by a computing device cluster that performs (Boyd et al. 2005/0256778 [0241; Claim 1]) an objective function solving method (Boyd et al. 2005/0256778 [0032 - strategic objective analyses] The promotion system may also perform strategic objective analyses in assessing and achieving strategic corporate objectives. A user generally does not know if 1) an objective is obtainable, and 2) how strategically she should approach achieving this objective using promotional incentives. Promotion system 100 can solve this problem by identifying 1) if the revenue target is feasible, and 2) if the target is feasible, what promotional incentive level will maximize profitability given this constraint. [0155 - communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet] In one embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server. In particular, the user may be in communication with the system 100 via electronic networks such as the Internet, an intranet, an extranet, a Value Added Network ("VAN"), VPN and the like. The Internet browser may be, for example, Netscape Navigator or Microsoft Internet Explorer. Those skilled in the art will recognize that this invention may be physically implemented in a number of ways. [0246-0249 - objective function][0260]), comprising: receiving a solving requirement input by a user (Boyd et al. 2005/0256778 [0002 - configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0024 - illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze] As illustrated in FIG. 1A, The promotion pricing system 100 receives various data inputs and processes these inputs to analyze promotion schemes. Among the inputs received by various embodiments of the promotion pricing system 100 are product information, consumer account information, commercial channel information, purchase/sales order information, competitor and competitor product information, and promotion/campaign information. [0031 - system 100 can solve this type of problem given certain inputs such as…] Another functionality of the promotion system 100 is mark-down optimization. A retailer may receive shipments of excess inventory to their stores. The retailer knows how much of this inventory is normally sold within a given period of time given historical information and general business knowledge. However, they do not know the optimal discount to set to achieve the objective of selling that inventory within the specified time period. In other words, the user does not want to overdiscount a product. promotion system 100 can solve this type of problem given certain inputs such as the target product, the total initial inventory for that product, and the amount of inventory that is to be sold for a given period. Promotion system 100 would then compute that discount which maximizes profit while clearing pre-identified excess inventory during the specified period.), wherein the solving requirement comprises an objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]); determining to solve the objective function using a simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]); and in a process of solving the objective function using the simplex method, after solving the objective function according to a first pricing strategy using the simplex method (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), determining, based on an objective improvement (Boyd et al. 2005/0256778 [0002; 0006 – improve the accuracy of the pricing optimizations calculations][0297 - algorithm accepts not only the movements improving the objective function, but also the movements corresponding to a deterioration in the objective function value]) on the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) by current solving in the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), a second pricing strategy for solving the objective function in a next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Claims 2, 8, 14 are rejected under 35 U.S.C. 103 as being unpatentable over: Boyd et al. 2005/0256778; in view of Guthrie et al. 2012/0059680; in further view of Mohanty et al. 2013/0166355; in view of Lim, S., & Park, S. (2002). LPAKO: A Simplex-based Linear Programming Program. Optimization Methods and Software, 17(4), 717–745. https://doi.org/10.1080/1055678021000049381 (hereinafter LPAKO).
19/094,696 – Claim 2. (Currently Amended) Boyd et al. 2005/0256778 further teaches The method according to claim 1, wherein after the solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) using the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), the method further comprises: determining a basis exchange degeneracy and an execution duration proportion that are associated with the solving performed according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), wherein the execution duration proportion is a proportion of duration of executing the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) by a computing device to total duration of performing solving by the computing device according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.); and wherein determining the second pricing strategy for solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) in the next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]) comprises determining, based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion, the second pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) for solving the objective function in the next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 8. (Currently Amended) Boyd et al. 2005/0256778 further teaches The apparatus according to claim 7, wherein the processor is further configured to invoke the one or more instructions in the memory (Boyd et al. 2005/0256778 [0034; 0155; 0241; Claim 1]) to: determine a basis exchange degeneracy and an execution duration proportion that areassociated with the solving performed according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), wherein the execution duration proportion is a proportion of duration of executing the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) by a computing device to total duration of performing solving by the computing device according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.); and determine, based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion, the second pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) for solving the objective function in the next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 14. (New) Boyd et al. 2005/0256778 further teaches The non-transitory computer-readable storage medium (Boyd et al. 2005/0256778 [0155 - embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server]) according to claim 13, wherein after the solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) according to a first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) using the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), the method further comprises: determining a basis exchange degeneracy and an execution duration proportion that are associated with the solving performed according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.), wherein the execution duration proportion is a proportion of duration of executing the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) by a computing device to total duration of performing solving by the computing device according to the first pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.); and the determining, based on an objective improvement on the objective function by the current solving in the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]), a second pricing strategy for solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) in the next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]) comprises: determining, based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion, the second pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) for solving the objective function in the next iteration (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Claims 3, 9, 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Boyd et al. 2005/0256778; in view of Guthrie et al. 2012/0059680; in further view of Mohanty et al. 2013/0166355; in view of Lim, S., & Park, S. (2002). LPAKO: A Simplex-based Linear Programming Program. Optimization Methods and Software, 17(4), 717–745. https://doi.org/10.1080/1055678021000049381 (hereinafter LPAKO).
19/094,696 – Claim 3. (Currently Amended) Boyd et al. 2005/0256778 further teaches The method according to claim 2, wherein for determining the basis exchange degeneracy, and the execution duration proportion, the second pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) for solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) in the next iteration comprises (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]): determining an objective strategy reference value based on the objective improvement (Boyd et al. 2005/0256778 [Abstract; 0002 - improve the accuracy of the pricing optimizations calculations]), the basis exchange degeneracy, and the execution duration proportion (Boyd et al. 2005/0256778 [0031 – period of time… specified time period interpreted as duration proportion]); and determining the second pricing strategy associated with the objective strategy reference value based on a correspondence (Boyd et al. 2005/0256778 [0022 - interacting and exchanging data using known communication and networking techniques]) between a strategy reference value and a pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 9. (Currently Amended) Boyd et al. 2005/0256778 further teaches The apparatus according to claim 8, wherein the processor is further configured to invoke the one or more instructions in the memory (Boyd et al. 2005/0256778 [0034; 0155; 0241; Claim 1]) to: determine an objective strategy reference value based on the objective improvement (Boyd et al. 2005/0256778 [Abstract; 0002 - improve the accuracy of the pricing optimizations calculations]), the basis exchange degeneracy, and the execution duration proportion (Boyd et al. 2005/0256778 [0031 – period of time… specified time period interpreted as duration proportion]); and determine the second pricing strategy associated with the objective strategy reference value based on a correspondence (Boyd et al. 2005/0256778 [0022 - interacting and exchanging data using known communication and networking techniques]) between a strategy reference value and a pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 15. (New) Boyd et al. 2005/0256778 further teaches The non-transitory computer-readable storage medium according to claim 14, wherein the determining, based on the objective improvement (Boyd et al. 2005/0256778 [Abstract; 0002 - improve the accuracy of the pricing optimizations calculations]), the basis exchange degeneracy, and the execution duration proportion (Boyd et al. 2005/0256778 [0031 – period of time… specified time period interpreted as duration proportion]), the second pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.) for solving the objective function (Boyd et al. 2005/0256778 [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions]) in the next iteration comprises (Boyd et al. 2005/0256778 [0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]): determining an objective strategy reference value based on the objective improvement (Boyd et al. 2005/0256778 [Abstract; 0002 - improve the accuracy of the pricing optimizations calculations]), the basis exchange degeneracy, and the execution duration proportion (Boyd et al. 2005/0256778 [0031 – period of time… specified time period interpreted as duration proportion]); and determining the second pricing strategy associated with the objective strategy reference value based on a correspondence (Boyd et al. 2005/0256778 [0022 - interacting and exchanging data using known communication and networking techniques]) between a strategy reference value and a pricing strategy (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions.).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Boyd et al. 2005/0256778 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Boyd et al. 2005/0256778 may not expressly disclose the “degeneracy” features, however, LPAKO teaches (pg. 719, ⁋ 3 “In Section 6, the pricing rule and anti-degeneracy technique adopted in LPAKO are presented.”; pg. 730, ⁋ 1 “In LPAKO, the objective improvement and the degree of degeneracy are monitored in every iteration, and the parameters in multiple-partial pricing are dynamically adjusted according to the monitoring results: if the rate of the objective improvement is continuously below 5% for 100 iterations, or the degree of degeneracy (which is computed as the portion of basic variables at their bounds) is continuously above 80% for 100 iterations, the size of candidates list (SP) and the size of multiple choice of entering columns (SM) are doubled…”; See also, pgs.729-733, Section 6 PRICING RULE AND ANIT-DEGENERACY TECHNIQUE; pg.732, Section 6.2 Anti-degeneracy Technique; pg. 734 ⁋ 1, “Normalized pricing such as the steepest-edge pricing requires the update of reduced costs to reduce the computational burden. So, the one artificial variable technique is more efficient than the (extended) composite simplex method when used with normalized pricing strategies.”). 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 Boyd et al. 2005/0256778 to include the features as taught by LPAKO. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Claims 6, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over: Boyd et al. 2005/0256778; in view of Guthrie et al. 2012/0059680; in further view of Mohanty et al. 2013/0166355.
19/094,696 – Claim 6. (Original) Boyd et al. 2005/0256778 further teaches The method according to claim 1, wherein after determining the second pricing strategy for solving the objective function in the next iteration (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions. [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions][0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]), the method further comprises: sending the second pricing strategy to a user terminal (Boyd et al. 2005/0256778 [0016 – graphical user interface (GUI); 0243 – GUI][0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]); receiving a pricing strategy confirmation notification sent by the user terminal (Boyd et al. 2005/0256778 [0016 - A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization] In another embodiment, the present invention provides a configurable pricing system that allows users to define or modify data used to analyze, evaluate, improve, and design pricing changes according to the user's need. A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization. [0243]); and solving the objective function according to the second pricing strategy (Boyd et al. 2005/0256778[0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]) using the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 12. (Original) Boyd et al. 2005/0256778 further teaches The apparatus according to claim 7, wherein the processor is further configured to invoke the one or more instructions in the memory (Boyd et al. 2005/0256778 [0241; Claim 1]) to: send the second pricing strategy to a user terminal (Boyd et al. 2005/0256778 [0016 – graphical user interface (GUI); 0243 – GUI][0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]); receive a pricing strategy confirmation notification sent by the user terminal (Boyd et al. 2005/0256778 [0016 - A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization] In another embodiment, the present invention provides a configurable pricing system that allows users to define or modify data used to analyze, evaluate, improve, and design pricing changes according to the user's need. A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization. [0243]); and solve the objective function according to the second pricing strategy (Boyd et al. 2005/0256778[0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]) using the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 18. (New) Boyd et al. 2005/0256778 further teaches The non-transitory computer-readable storage medium (Boyd et al. 2005/0256778 [0155 - embodiment depicted in FIG. 1B, the promotion system 100 is configured to operate over a distributed network such as the Internet. Specifically, the various modules of the promotion system 100 operate as JAVA or C applications that may be served or are executed at the server][0241; Claim 1]) according to claim 13, wherein after the determining the second pricing strategy for solving the objective function in the next iteration (Boyd et al. 2005/0256778 [0002 - present invention relates to a configurable price optimization application] The present invention relates to a configurable price optimization application which allows users to define or add additional boundaries and constraints as needed to better meet business concerns and to improve the accuracy of the pricing optimizations calculations. [0021 - FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies] As generally illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies. In particular, a user may employ the present invention to evaluate historical data to determine a more ideal promotional strategy to accomplish various business goals, such as increasing total sales volumes or increasing sales in certain desired market segments. The promotion pricing system functions to either propose a promotional strategy or to evaluate the expected effect of a promotional policy provided by the user. The promotion pricing system 100 works by defining the market by specifying the various products in the market, as well as the suppliers (i.e., sellers in the market) and demanders (i.e., consumers). The promotion pricing system 100 then looks to historical market data to create a market model which may be used to determine various information, such as profit or sales maximizing conditions. [0260-0261 – objective functions][0297; 0299; 0310-0318 – objective functions][0241 – pricing optimization system…][0275; 0276; 0286 – next iteration]), the method further comprises: sending the second pricing strategy to a user terminal (Boyd et al. 2005/0256778 [0016 – graphical user interface (GUI); 0243 – GUI][0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]); receiving a pricing strategy confirmation notification sent by the user terminal (Boyd et al. 2005/0256778 [0016 - A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization] In another embodiment, the present invention provides a configurable pricing system that allows users to define or modify data used to analyze, evaluate, improve, and design pricing changes according to the user's need. A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization. [0243]); and solving the objective function according to the second pricing strategy (Boyd et al. 2005/0256778[0021 - illustrated in FIG. 1A, the present invention provides a promotion pricing system 100 for producing and evaluating promotion pricing strategies]) using the simplex method (Boyd et al. 2005/0256778 [0262; 0271; 0272; 0273; 0276; 0286 – simplex methods]).
Boyd et al. 2005/0256778 may not expressly disclose the “solving the objective function …” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Boyd et al. 2005/0256778 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Claims 1, 7, 13 are rejected under 35 U.S.C. 103 as being unpatentable over: Wunderling 2013/0036085; in view of Guthrie et al. 2012/0059680; in further view of Mohanty et al. 2013/0166355.
19/094,696 – Claim 1. (Currently Amended) Wunderling 2013/0036085 teaches An objective function solving method (Wunderling 2013/0036085 [0003 – objective function]), applied to an electronic device (Wunderling 2013/0036085 [0061]) wherein the method comprises: receiving a solving requirement input by a user (Wunderling 2013/0036085 [0003 - Linear programming can be utilized for many engineering problems, but also for business-related problems]), wherein the solving requirement comprises an objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]); determining to solve the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) using a simplex method (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized. Linear programming can be utilized for many engineering problems, but also for business-related problems. The simplex method, in particular the simplex method in the context of mixed integer programming, herein also referred to as `traditional simplex method`, is one of the most important tools for solving linear programs]); and in a process of solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), after solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) according to a first pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), determining, based on an objective improvement on the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) by current solving in the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), a second pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) for solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) in a next iteration (Wunderling 2013/0036085 [0008; 0033 – simplex iterations][0040 - updated at each iteration of KSM][0047 - modified simplex iterations comprises executing a pricing step]).
Wunderling 2013/0036085 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Wunderling 2013/0036085 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Wunderling 2013/0036085 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Wunderling 2013/0036085 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 7. (Original) Wunderling 2013/0036085 further teaches An objective function solving apparatus (Wunderling 2013/0036085 [0003 – objective function]) comprising a processor, a memory, wherein the memory is configured to store one or more instructions, and the processor is configured to invoke the one or more instructions in the memory (Wunderling 2013/0036085 [0031-0032; 0060-0062; 0173; Fig. 1]) to: receive a solving requirement input by a user (Wunderling 2013/0036085 [0003 - Linear programming can be utilized for many engineering problems, but also for business-related problems]), wherein the solving requirement comprises an objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]); determine to solve the objective function using (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) a simplex method (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized. Linear programming can be utilized for many engineering problems, but also for business-related problems. The simplex method, in particular the simplex method in the context of mixed integer programming, herein also referred to as `traditional simplex method`, is one of the most important tools for solving linear programs]); and in a process of solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), after solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) according to a first pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), determine, based on an objective improvement on the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) by the current solving in the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), a second pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) for solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) in a next iteration (Wunderling 2013/0036085 [0008; 0033 – simplex iterations][0040 - updated at each iteration of KSM][0047 - modified simplex iterations comprises executing a pricing step]).
Wunderling 2013/0036085 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Wunderling 2013/0036085 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Wunderling 2013/0036085 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Wunderling 2013/0036085 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
19/094,696 – Claim 13. (Currently Amended) Wunderling 2013/0036085 further teaches A non-transitory computer-readable storage medium comprising computer program instructions (Wunderling 2013/0036085 [0031-0032; 0060-0062; 0173; Fig. 1]), wherein when the computer program instructions are executed by a computing device cluster that performs an objective function solving method (Wunderling 2013/0036085 [0003 – objective function]), comprising: receiving a solving requirement input by a user (Wunderling 2013/0036085 [0003 - Linear programming can be utilized for many engineering problems, but also for business-related problems]), wherein the solving requirement comprises an objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]); determining to solve the objective function using (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) a simplex method (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized. Linear programming can be utilized for many engineering problems, but also for business-related problems. The simplex method, in particular the simplex method in the context of mixed integer programming, herein also referred to as `traditional simplex method`, is one of the most important tools for solving linear programs]); and in a process of solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), after solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) according to a first pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) using the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), determining, based on an objective improvement on the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) by current solving in the simplex method (Wunderling 2013/0036085 [0002; 0003; 0007; 0020; 0034; 0060; 0062; 0081; 0085; 0086; 00138; 0171; 0172; Figs. 3 and 4] simplex methods), a second pricing strategy (Wunderling 2013/0036085 [0146 – pricing strategies]) for solving the objective function (Wunderling 2013/0036085 [0003 - Linear programming is a specific case of mathematical programming whereby an objective function, subject to linear equality and linear inequality constraints, is optimized; 0006 – objective function]) in a next iteration (Wunderling 2013/0036085 [0008; 0033 – simplex iterations][0040 - updated at each iteration of KSM][0047 - modified simplex iterations comprises executing a pricing step]).
Wunderling 2013/0036085 may not expressly disclose the “solving the objective function using the simplex method” features, however, Guthrie et al. 2012/0059680 teaches (Guthrie et al. 2012/0059680 [0109 - system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.)] Following block 1910 is block 1915, in which the IT assessment system mathematically solves the objective function according to the provided rules and constraints. The IT assessment system can be configured to execute any number of mathematical analyses to solve the objective function and identify an optimum (or improved) solution to the objective function, including, but not limited to, linear programming techniques (e.g., the Simplex Algorithm or the Hungarian Method, etc.), ranking, integer programming (e.g., the branch and bound method, etc.), non-linear programming (e.g., if interdependencies exist between variables, such as variables that multiply or divide on other variables, etc.), and the like. [0110 – the objective function may be solved utilizing the Simplex Algorithm] According to one embodiment, the objective function may be solved utilizing the Simplex Algorithm, for which a "starting matrix" is formulated. A starting matrix represents the objective function and the simultaneous equations that constrain the solution. The starting matrix is created according to the rules of the Simplex Method and combines the objective function variables and those constraint equations that relate the objective functions to each other and to other controlling values (limits, thresholds, non-negativity, etc.). The solution to the problem is the set of values assigned to each objective function variable. In the examples described herein, the objective function value, which refers to the sum of all the values assigned to objective function variables, can be the OIIV variance for the enterprise (e.g., the number of degrees above or below an acceptable operating range). [0111 - FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method] FIG. 21 illustrates an example starting matrix 2100 for solving the objective function by the Simplex Method, according to one embodiment. The "x" variables 2105 across the top of the starting matrix 2100 represent the objective function variables. The solution contains a unique value for each x variable, and the complete set of x variables is the solution to the complete problem of optimizing the objective function value. As used herein, optimizing may generally refer to improving, maximizing, minimizing, etc., depending on the formulation of the objective function and the desired assessment goals. According to one embodiment, as described above, the values for each of the "x" variables 2105 represent the amount of investment at the application level, by type of investment (e.g., improving category impact, improving classification impact, and improving cost impact), as well as optionally by the year of investment, such as if a multiple year analysis is being performed.). 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 Wunderling 2013/0036085 to include the features as taught by Guthrie et al. 2012/0059680. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
Wunderling 2013/0036085 may not expressly disclose the “strategy” features, however, Mohanty et al. 2013/0166355 teaches (Mohanty et al. 2013/0166355 [0022 - identify the best fit method for price calculation and dynamically synchronizing the objectives and constraints of multiple stakeholders][0023-0025; 0069 – pricing strategy][0080 - model proposes an Investment proportion scenario…][Claim 9 - using one of the one of the best fit pricing strategy and a stakeholder approved pricing strategy, and synchronizing objectives and constraints of each stakeholder of the plurality of stakeholders with dynamic environmental factors to collaboratively approve and determine…]). 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 Wunderling 2013/0036085 to include the features as taught by Mohanty et al. 2013/0166355. One of ordinary skill in the art would have been motivated to do so to implement well known tools and features useful for implementing an objective function solving method which should prove to improve user experience, maximize profits, and optimize revenue.
No Prior-art Rejection / Potentially Allowable
Claims 4, 5, 10, 11, 16, 17 cannot be rejected with prior-art. Individual claimed features are taught in the prior-art, however, the unique combination of features and elements are not taught by the prior-art without hindsight reasoning. These claims are further rejected to as being dependent upon a rejected base claim but might possibly be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
19/094,696 – Claim 4. (Currently Amended) The method according to claim 3, wherein determining the objective strategy reference value based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion comprises: obtaining a first weight coefficient associated with the objective improvement, a second weight coefficient associated with the execution duration proportion, and a third weight coefficient associated with the basis exchange degeneracy; and performing weighted calculation on the objective improvement, the execution duration proportion, and the basis exchange degeneracy based on the first weight coefficient, the second weight coefficient, and the third weight coefficient, to obtain the objective strategy change value.
19/094,696 – Claim 10. (Currently Amended) The apparatus according to claim 9, wherein the processor is further configured to invoke the one or more instructions in the memory to: obtain a first weight coefficient associated with the objective improvement, a second weight coefficient associated with the execution duration proportion, and a third weight coefficient associated with the basis exchange degeneracy; and perform weighted calculation on the objective improvement, the execution duration proportion, and the basis exchange degeneracy based on the first weight coefficient, the second weight coefficient, and the third weight coefficient, to obtain the objective strategy change value.
19/094,696 – Claim 16. (New) The non-transitory computer-readable storage medium according to claim 15, wherein the determining the objective strategy reference value based on the objective improvement, the basis exchange degeneracy, and the execution duration proportion comprises: obtaining a first weight coefficient associated with the objective improvement, a second weight coefficient associated with the execution duration proportion, and a third weight coefficient associated with the basis exchange degeneracy; and performing weighted calculation on the objective improvement, the execution duration proportion, and the basis exchange degeneracy based on the first weight coefficient, the second weight coefficient, and the third weight coefficient, to obtain the objective strategy change value.
19/094,696 – Claim 5. (Original) The method according to claim 4, wherein the first weight coefficient is greater than the second weight coefficient, and the second weight coefficient is greater than the third weight coefficient.
19/094,696 – Claim 11. (Original) The apparatus according to claim 10, wherein the first weight coefficient is greater than the second weight coefficient, and the second weight coefficient is greater than the third weight coefficient.
19/094,696 – Claim 17. (New) The non-transitory computer-readable storage medium according to claim 16, wherein the first weight coefficient is greater than the second weight coefficient, and the second weight coefficient is greater than the third weight coefficient.
Prosecution Insights