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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 14-18 are new.
Claims 1-18 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) (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.
Preliminary Amendment
The present Office Action is based upon the original patent application filed on 03/28/2025 as modified by the preliminary amendment filed on 05/28/2025.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. Independent claims X, Y, and Z all contain the same inventive scope. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn.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 1-18 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method, system/apparatus, and computer readable medium for an objective function solving method.
Claim 1 recites [a]n objective function solving method, applied to an electronic device wherein the method comprises: receiving a solving requirement input by a user, wherein the solving requirement comprises an objective function; determining to solve the objective function using a simplex method; 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, determining, based on an objective improvement on the objective function by current solving in the simplex method, a second pricing strategy for solving the objective function in a next iteration.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-6 recite a method and, therefore, are directed to the statutory class of a process. Claims 7-12 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claims 13-18 recite a non-transitory computer readable medium/computer product and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. An objective function solving method, applied to an electronic device wherein the method comprises:
receiving a solving requirement input by a user, wherein the solving requirement comprises an objective function;
This limitation includes the step(s) of: receiving a solving requirement input by a user, wherein the solving requirement comprises an objective function.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving a requirement) to facilitate an objective function solving method which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
determining to solve the objective function using a simplex method; and
This limitation includes the step(s) of: determining to solve the objective function using a simplex method.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., solving a function) to facilitate an objective function solving method which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
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, determining, based on an objective improvement on the objective function by current solving in the simplex method, a second pricing strategy for solving the objective function in a next iteration.
This limitation includes the step(s) of: 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, determining, based on an objective improvement on the objective function by current solving in the simplex method, a second pricing strategy for solving the objective function in a next iteration.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., determining an improvement) to facilitate an objective function solving method which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of an objective function solving method, which, pursuant to MPEP 2106.04, is aptly categorized as a mathematical concept, mental process, and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The method claims do NOT recite any additional elements. Consequently, at least the method claims must be construed as abstract and capable of being performed mentally and/or manually with just pen and paper. The Office encourages Applicant to positively claim the structural features necessary to perform each individual method step and feature.
Next, the claims recite additional functional elements that are associated with the judicial exception, including: a computing device and computer readable medium for implementing the CRM claims (Claims 13-18, CRM claims), and processor and memory for implementing the system/apparatus claims (Claims 7-12, system/apparatus claims). Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The claims also recite additional technical elements including: a computing device and computer readable medium for implementing the CRM claims (Claims 13-18, CRM claims), and processor and memory for implementing the system/apparatus claims (Claims 7-12, system/apparatus claims). These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent system/apparatus claim 7 and CRM claim 13 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer component, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-6, 8-12, and 14-18 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims receiving a solving requirement, solving a function, determining a pricing strategy, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from implementing an objective function solving method. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Examiner’s Response to Arguments
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
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
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.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 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.
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.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
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 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 mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
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
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
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