DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 2/03/2025 submission filled in Application 18/517,716. Claims 1-20 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments filed 2/3/2025 with respect to claim(s) the prior art have been considered but are moot because the arguments do not apply to the new grounds of rejection that was necessitated by amendment.
Applicant's remaining arguments have been fully considered but they are not persuasive.
Regarding the rejection under 101, the Applicant argues “(April 24, 2025, Office Action, page 5). In addition, "Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." (October Update, 2019 Guidance, Page 7). Here, Applicants' claims are not directed to an abstract idea, such as a mental process, because the claimed invention is directed to solving a problem particular to the use of computers.”
The Examiner respectfully disagrees. The Examiner maintains that the limitations of formulating one or more constraints of a multi-objective hierarchical linear optimization; in response to formulating the one or more constraints, gather one or more flexible input parameters and formulating one or more objectives of the multi-objective hierarchical linear optimization; in response to formulating the one or more objectives; solving, the multi-objective hierarchical linear optimization; while solving the multi-objective hierarchical linear optimization; crawling through and analyzing, data stored in the plan explanation datastore to determine one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization; and collating and translating the determined one or more root causes cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” nothing in the claim element precludes the steps from being performed in the human mind.
Regarding the rejection under 101, the Applicant argues “Applicants thus respectfully submit that the claimed invention is solving a problem that is particular to computer processing of supply chain solutions and not directed to an abstract idea.
In DDR Holdings, LLC v. Hotels.com, L.P., the court stated:
As an initial matter, it is true that the claims here are similar to the claims in the cases discussed above in the sense that the claims involve both a computer and the Internet.
But these claims stand apart because they do not merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet. Instead, the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks.
(Emphasis Added). Applicants respectfully submit that the claimed invention is not directed to the abstract idea of merely performing a linear optimization, rather, the claimed invention is directed to solving a problem arising from the use of computers to solve complex, hierarchical supply chain problems by providing a way for a supply chain planner to interact with a processing log in order to, among other things, understand how and why a computerized solver reached a particular solution and to identify exceptions that occurred in the computer processing (such as discussed in paragraphs [0053] and [0054] above.”
The Examiner respectfully disagrees. Page 20 of the DDR Holdings, LLC v. Hotels.com Federal Circuit decision states, "But these claims stand apart because they do not merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet. Instead, the claimed solution is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks." The Examiner respectfully disagrees with Applicant’s assertion that the claims present a solution necessarily rooted in the technology in order to overcome a problem specifically arising in the computer network (or other technological) realm. Applicant’s claims seek to address a problem that existed and continues to exist outside of the realm of the technology associated with the additionally recited elements. The proposed solution is one that could have been implemented directly by a human performing analogous functions by hand and/or with the assistance of a general purpose computer applied to facilitate the functions at a high level of generality or with the assistance of additional elements performing well-known, conventional functions. In Applicant’s claims, the central processor could be substituted with a human user and the underlying invention would result in a similar solution to the problem at hand. The rejected claims do not adhere to the same fact pattern seen in the DDR Holdings, LLC v. Hotels.com decision. In the DDR Holdings decision, the manner in which the network itself operated was changed to improve network operations. There is no actual improvement made to the operations or physical structure of the additional elements claimed in the instant application.
Regarding the rejection under 103, the Applicant argues Iorio does not teach “crawl through and analyze”.
The Examiner respectfully disagrees. The claim does not define crawl through and analyze. As such the Examiner has applied the broadest reasonable interpretation. Iorio para. 66 teaches “persons skilled in the art may notice that the general approach described herein of classifying a problem and then executing a corresponding solution could also be applied recursively to select classification algorithms. In other words, an optimization algorithm could be applied to search a problem/solution space that includes other optimization algorithms, as described in greater detail below in conjunction with FIG. 8.” The Examiner considers searching a problem/solution space to be crawling through and analyzing.
Regarding the rejection under 103, the Applicant argues “As noted by the Examiner in the rationale, Pinkerton's teachings regarding "analyze the risk exposure", as explained by the Examiner, is simply not a relevant field of endeavor. Nothing in Applicants' disclosure addresses "risk exposure" per se, in the manner taught by Pinkerton. Accordingly Pinkerton is not a relevant field of endeavor.
Further, Applicants respectfully submit that Pinkerton is not "reasonably pertinent", at least because the risk management teachings of Pinkerton would not have commended themselves to the inventors' attention. Accordingly, Pinkerton is not "reasonably pertinent".”
The Examiner respectfully disagrees. The Applicant invention is directed to “a system and method of root cause analysis for goal violations of supply chain network plans”. Para. 30 discloses goal violations as for example, demand shorted, demand lated, and/or material or resource shortage Para. 58 describes actions taken based on various root causes of goals and/or objective violations. Pinkerton is also directed to taking appropriate mitigation measures to avoid types of losses including operational interruptions and supply chain failures (see para. 22). As such, the Examiner maintains that Pinkerton is analogous art because it is from the same field of endeavor as the claimed invention (even if it addresses a different problem); and it is reasonably pertinent to the problem faced by the inventor.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case Claims 1-7 are directed toward a system for determining one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization. Claims 8-14 are directed toward a method for determining one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization. Claims 15-20 are directed toward a computer program product for determining one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 is directed to the abstract idea of determining one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization which falls into the abstract idea categories of certain methods of organizing human activity and mental processes.
The elements of Claim 1 that represent the Abstract idea include:
A system, comprising:
formulate, one or more constraints of a multi-objective hierarchical linear optimization;
in response to formulating the one or more constraints, gather one or more flexible input parameters and
formulate, one or more objectives of the multi-objective hierarchical linear optimization;
in response to formulating the one or more objectives;
solve, the multi-objective hierarchical linear optimization;
while solving the multi-objective hierarchical linear optimization;
crawl through and analyze, data stored in the plan explanation datastore to determine one or more root causes of one or more plan exceptions resulting from solving the multi-objective hierarchical linear optimization wherein the one or more root causes comprise at least one objective violation occurring during the solving; and
collate and translate, by the business level interpreter, the determined one or more root causes in a form of a machine-readable output
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the limitations of performing multi-objective hierarchical linear optimization to determine one or more root causes of one or more plan exceptions are directed to business relations and fundamental economic principles such as determining root causes to exceptions to improve business operations.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
In the instant case, the limitations of formulating one or more constraints of a multi-objective hierarchical linear optimization; in response to formulating the one or more constraints, gather one or more flexible input parameters and formulating one or more objectives of the multi-objective hierarchical linear optimization; in response to formulating the one or more objectives; solving, the multi-objective hierarchical linear optimization; while solving the multi-objective hierarchical linear optimization; crawling through and analyzing, data stored in the plan explanation datastore to determine one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective hierarchical linear optimization; and collating and translating the determined one or more root causes cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” nothing in the claim element precludes the steps from being performed in the human mind.
MPEP 2106.04(a)(2) states:
The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010).
The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199. In the past, the Supreme Court sometimes described mathematical concepts as laws of nature, and at other times described these concepts as judicial exceptions without specifying a particular type of exception. See, e.g., Benson, 409 U.S. at 65, 175 USPQ2d at 674; Flook, 437 U.S. at 589, 198 USPQ2d at 197; Mackay Radio & Telegraph Co. v. Radio Corp. of Am., 306 U.S. 86, 94, 40 USPQ 199, 202 (1939) (‘‘[A] scientific truth, or the mathematical expression of it, is not patentable invention[.]’’). More recent opinions of the Supreme Court, however, have affirmatively characterized mathematical relationships and formulas as abstract ideas. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 218, 110 USPQ2d 1976, 1981 (2014) (describing Flook as holding "that a mathematical formula for computing ‘alarm limits’ in a catalytic conversion process was also a patent-ineligible abstract idea."); Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (noting that the claimed "concept of hedging, described in claim 1 and reduced to a mathematical formula in claim 4, is an unpatentable abstract idea,").
In the instant case, the step of solving the multi-objective hierarchical linear optimization is directed to mathematical calculations. This is similar to Example 47 of the USPTO Subject Matter Eligibility Example that states “When given their broadest reasonable interpretation in light of the background, the backpropagation algorithm and gradient descent algorithm are mathematical calculations. The plain meaning of these terms are optimization algorithms, which compute neural network parameters using a series of mathematical calculations”. In the instant case the solving of the multi-objective hierarchical linear optimization is a mathematical calculation.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
a server, comprising a plan solver, a cause and effect accumulator, a violation analyzer, a business level interpreter, wherein the server is connected to a plan explanation datastore, the server configured to:
store the one or more flexible input parameters in a machine-readable log in the plan explanation datastore;
store, by the cause and effect accumulator, one or more variables and one or more parameters in the machine-readable log in the plan explanation datastore;
store, by the cause and effect accumulator, one or more optimal objective values and one or more optimal variable values in a machine-readable log in the plan explanation datastore as part of a plan explanation workflow
the output translation is machine actionable upon output by the business level interpreter.
However, the computer elements (a server, comprising a plan solver, a cause and effect accumulator, a violation analyzer, a business level interpreter, wherein the server is connected to a plan explanation datastore) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer.
Further, the storing of data is recited broadly. Under the broadest reasonable interpretation, the limitations amounts to data storage which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g). Further, the output is also recited broadly and amounts to insignificant post solution activity.
Viewing the generic computer elements in combination with the storing data and outputting data does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea.
In step 2B, the examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
As discussed with respect to Step 2A Prong Two, the additional element of a server that is connected to a plan explanation datastore amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, nothing in the claim indicates that the retrieval and storage of information is anything other than conventional. See MPEP 2106.05(d) that states “Receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed in a merely generic manner (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Also see MPEP 2106.05(d) that states storing and retrieving information in memory is conventional when claimed in a merely generic manner (see 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).
Further, the Examiner takes official notice that outputting the result of an analysis in the manner claimed in well-known an conventional.
Further Claims 2-7 further limit the abstract idea of an analysis that can be performed mentally or certain methods of human activity that were already rejected in claim 1, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Further, Claim 6 further recites the additional elements of storing errors logged. However this amounts to mere data storage which is insignificant post solution activity and not beyond what is well known and conventional.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 2-7 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 1 otherwise styled as a method or computer program product, for example, would be subject to the same analysis. Therefore, Claims 8-20 are rejected for the same rational that applied to claims 1-7.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1, 4, 8, 11, 15, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iorio US 20140149331 A1 in view of “Solving bi-level programming with multiple linear objectives at lower level using particle swarm optimization” (hereinafter Matroud) in view of Crawford US 5,943,244 A.
As per Claim 1 Iorio teaches A system, comprising:
a server, comprising a plan solver, a cause and effect accumulator, a violation analyzer, a business level interpreter, wherein the server is connected to a plan explanation datastore, the server configured to: (see Iorio Fig. 1).
formulate, by the plan solver, one or more constraints of a multi-objective optimization;
(Iorio para. 30 teaches domain 200 represents a particular class of optimization problems, such as "structural," "fluid dynamics," "logistics," and so forth. Parameters 201 represent a set of mathematical values that are to be optimized via the optimization process described herein. Constraints 202 represent limitations on the solution to the optimization problem. Constraints 202 could be explicit ranges for parameters 201, or a set of functions of those parameters 201 with specific output ranges, among other possibilities. Objectives 203 represent a set of functions to be maximized or minimized via the optimization process described herein. Objectives 203 could include, e.g., a set of cost functions that depend on parameters 201, among other types of objective functions.)
in response to formulating the one or more constraints, gather one or more flexible input parameters and store the one or more flexible input parameters in a machine-readable log in the plan explanation datastore; (Iorio para. 55 teaches As shown, a method 600 begins at step 601, where strategy execution engine 205 receives a stored solution strategy 208 from classification engine 204. In practice, strategy execution engine 205 may receive multiple stored solution strategies 208 from classification engine 204 and execute each one in parallel with the others. However, for the sake of simplicity, the execution of just one solution strategy 208 is discussed herein. At step 602, strategy execution engine 205 executes an optimization algorithm 300 with the associated hyperparameters 301. In doing so, strategy execution engine 205 may increment or decrement parameters 201, within the limitations provided by constraints 202, in order to meet objectives 203. At step 603, strategy execution engine 205 dynamically adjusts hyperparameters 301 to improve the convergence rate of the current optimization algorithm 300. In one embodiment, stored solution strategy 208 includes data indicating sensitivities of the current optimization algorithm 300 to changes in hyperparameters 301, and strategy execution engine 205 adjusts hyperparameters 301 according to those sensitivities.)
formulate, by the plan solver, one or more objectives of the multi-objective optimization; (Iorio para. 5 teaches mathematical optimization includes a wide variety of different techniques for identifying a combination of parameters that meets one or more objectives. The combination of parameters could relate to any particular domain, including engineering problems such as structural design, financial problems such as portfolio optimization, and so forth. Generally, techniques for performing mathematical optimization include applying different algorithms that vary a set of parameters until an objective function is maximized or minimized. Para. 30 teaches Domain 200 represents a particular class of optimization problems, such as "structural," "fluid dynamics," "logistics," and so forth. Parameters 201 represent a set of mathematical values that are to be optimized via the optimization process described herein. Constraints 202 represent limitations on the solution to the optimization problem. Constraints 202 could be explicit ranges for parameters 201, or a set of functions of those parameters 201 with specific output ranges, among other possibilities. Objectives 203 represent a set of functions to be maximized or minimized via the optimization process described herein. Objectives 203 could include, e.g., a set of cost functions that depend on parameters 201, among other types of objective functions. Para. 55 teaches As shown, a method 600 begins at step 601, where strategy execution engine 205 receives a stored solution strategy 208 from classification engine 204. In practice, strategy execution engine 205 may receive multiple stored solution strategies 208 from classification engine 204 and execute each one in parallel with the others. However, for the sake of simplicity, the execution of just one solution strategy 208 is discussed herein. At step 602, strategy execution engine 205 executes an optimization algorithm 300 with the associated hyperparameters 301. In doing so, strategy execution engine 205 may increment or decrement parameters 201, within the limitations provided by constraints 202, in order to meet objectives 203. At step 603, strategy execution engine 205 dynamically adjusts hyperparameters 301 to improve the convergence rate of the current optimization algorithm 300. In one embodiment, stored solution strategy 208 includes data indicating sensitivities of the current optimization algorithm 300 to changes in hyperparameters 301, and strategy execution engine 205 adjusts hyperparameters 301 according to those sensitivities.)
in response to formulating the one or more objectives, store, by the cause and effect accumulator, one or more variables and one or more parameters in the machine-readable log in the plan explanation datastore; (Iorio para. 32 teaches Once optimization engine 124 pre-processes problem specification 115 in the fashion described above, a classification engine 204 within optimization engine 124 is configured to map problem specification 115 into problem/solution space 141. Problem space 141 includes a collection of stored problem specifications 207 and a corresponding collection of stored solution strategies 208. Each stored problem specification 207 is associated with an optimization problem that optimization engine 124 previously attempted to solve. Each corresponding stored solution strategy 208 represents a previously implemented approach to solving that optimization problem and information that reflects the degree to which that approach was successful. Para. 35-36 teaches FIG. 3 illustrates a stored solution strategy 208 that may be executed by strategy execution engine 205 within optimization engine 124 to generate a solution to an optimization problem, according to one embodiment of the present invention. As shown, stored solution strategy 208 includes a sequence of optimization algorithms 300-0 through 300-M, a corresponding sequence of hyperparameters 301-0 through 301-M, and a corresponding sequence of termination conditions 302-0 through 302-M. Stored solution strategy 208 also includes performance criteria 303, which may be subdivided into performance criteria 303-0 through 303-M.
An optimization algorithm 300 could be, for example, a gradient descent algorithm, a simulated annealing algorithm, a genetic algorithm, or any other single-objective or multi-objective optimization algorithm. Hyperparameters 301 includes various values that influence the performance of the corresponding optimization algorithm 300. For example, hyperparameters 300 associated with a gradient descent algorithm could indicate a step size for traversing a solution space. In another example, hyperparameters 300 associated with a genetic algorithm could indicate a starting population size or number of generations to evaluate. Termination conditions 302 indicate when the execution of the corresponding optimization algorithm 300 should be terminated. For example, termination conditions 302 associated with a simulated annealing algorithm could indicate that the execution of that algorithm should be terminated after 10 minutes. Any given set of termination conditions 302 may also indicate a particular standard by which convergence may be detected, such as, e.g., a minimum difference between a current solution and an objective 203 that should be achieved.
solve, by the plan solver, the multi-objective optimization; Iorio Abstract teaches he optimization engine selects one or more solution strategies associated with similar optimization problems, and then executes those solution strategies to solve the optimization problem.
while solving the multi-objective optimization, store, by the cause and effect accumulator, one or more optimal objective values and one or more optimal variable values in a machine-readable log in the plan explanation datastore as part of a plan explanation workflow; Iorio para. 32 teaches Once optimization engine 124 pre-processes problem specification 115 in the fashion described above, a classification engine 204 within optimization engine 124 is configured to map problem specification 115 into problem/solution space 141. Problem space 141 includes a collection of stored problem specifications 207 and a corresponding collection of stored solution strategies 208. Each stored problem specification 207 is associated with an optimization problem that optimization engine 124 previously attempted to solve. Each corresponding stored solution strategy 208 represents a previously implemented approach to solving that optimization problem and information that reflects the degree to which that approach was successful. Para. 35-36 teach FIG. 3 illustrates a stored solution strategy 208 that may be executed by strategy execution engine 205 within optimization engine 124 to generate a solution to an optimization problem, according to one embodiment of the present invention. As shown, stored solution strategy 208 includes a sequence of optimization algorithms 300-0 through 300-M, a corresponding sequence of hyperparameters 301-0 through 301-M, and a corresponding sequence of termination conditions 302-0 through 302-M. Stored solution strategy 208 also includes performance criteria 303, which may be subdivided into performance criteria 303-0 through 303-M.
An optimization algorithm 300 could be, for example, a gradient descent algorithm, a simulated annealing algorithm, a genetic algorithm, or any other single-objective or multi-objective optimization algorithm. Hyperparameters 301 includes various values that influence the performance of the corresponding optimization algorithm 300. For example, hyperparameters 300 associated with a gradient descent algorithm could indicate a step size for traversing a solution space. In another example, hyperparameters 300 associated with a genetic algorithm could indicate a starting population size or number of generations to evaluate. Termination conditions 302 indicate when the execution of the corresponding optimization algorithm 300 should be terminated. For example, termination conditions 302 associated with a simulated annealing algorithm could indicate that the execution of that algorithm should be terminated after 10 minutes. Any given set of termination conditions 302 may also indicate a particular standard by which convergence may be detected, such as, e.g., a minimum difference between a current solution and an objective 203 that should be achieved.
crawl through and analyze, by the violation analyzer, data stored in the plan explanation datastore to determine one or more root causes of one or more plan exceptions resulting from the plan solver solving the multi-objective optimization; and Iorio para. 66 teaches “persons skilled in the art may notice that the general approach described herein of classifying a problem and then executing a corresponding solution could also be applied recursively to select classification algorithms. In other words, an optimization algorithm could be applied to search a problem/solution space that includes other optimization algorithms, as described in greater detail below in conjunction with FIG. 8.”
collate and translate, by the business level interpreter, the determined one or more root causes in a form of a machine-readable output such that the output translation is machine actionable upon output by the business level interpreter. Iorio para. 45 teaches At step 405, if classification engine 204 determines that problem specification 115 does not correlate sufficiently with multiple stored problem specifications 207, then classification engine 204 proceeds to step 407. At step 407, classification engine 204 selects all stored solution strategies 208 within problem/solution space 141. Classification engine 204 proceeds to step 407 when problem specification 115 does not correlate sufficiently to any stored problem specifications 207 in problem/solution space 141. Classification engine 204 then proceeds to step 409 and initiates the execution of the selected solution strategies 408. An exemplary scenario where problem specification 115 does not correlate sufficiently to any stored problem specifications 207 is described, by way of example, below in conjunction with FIG. 5C.
Iorio does not explicitly disclose wherein the one or more root causes comprise at least one objective violation occurring during the solving However, Crawford column 8 lines 10-25 teach Each of resolver modules 70-74 is operable to communicate with solver module 64 and access interaction coefficient memory 66. The functionality of resolver modules 70-74 can be performed by any suitable processor, such as a mainframe, file server, or workstation, running appropriate software. Such processor can be the same or separate from the processor or processors performing the functionality of constraint checker module 62 and solver module 64. Resolver module 70-74 can be invoked by solver module 64. Each resolver modules 70-74 functions to resolve the violation of a particular constraint within network plan 22, such violation having been detected by constraint checker module 62. For example, in a supply chain environment, if it is determined that a factory has violated a constraint by not outputting enough units of a finished product, a resolver module 70-74 may function to increase raw materials being received at such factory. Both Iorio and Crawford are directed to supply chain management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Iorio to include wherein the one or more root causes comprise at least one objective violation occurring during the solving as taught by Crawford to more easily identify issues in a supply chain (as suggested by Crawford column 1, lines 15-35)
Iorio does not teach the use of hierarchical linear optimization. However, Matroud Introduction section teaches the bi-level programming problem is an optimization problem in which the constraints are implicitly determined by another optimization problem. In other words, it is a hierarchical optimization problem consisting of two levels. At the upper level, the decision maker (leader) has to choose first a strategy and then lower level decision maker (follower) has to select a strategy that minimizes its own objective function parameterized by . Anticipating the reaction of the follower, the leader intends to find such values for its variables which together with the follower's reaction minimize its objective function.
In this study, we are concerned with bilevel programming problems where the lower level is a multiobjective optimization problem (BPMLO). Bi-level problems with multiple objectives at the lower level have been considered in the literature by Bonnel [3] that provides first order necessary conditions for the solution of the bilevel problem when dealing with weakly efficient and properly efficient solution of the lower level. Calvete and Gale [6] proved that the feasible region consists of faces of the polyhedron defined by the constraints. Then, by assuming that the upper level objective function is quasi concave, concluded that feasible region of this problem consists of faces of the polyhedron define by the constraints and there is an extreme point of this polyhedron which solves the problem. Finally, based on this property, two algorithm have presented for solving the problem. Particle swarm optimization (PSO) is an optimization algorithm proposed by Kennedy and Eberhart in 1995[12]. It is a relatively novel heuristic algorithm inspired by the choreography of a bird flock, which has been found to be quite successful in a wide variety of optimization tasks [11]. Due to its high speed of convergence and relative simplicity, the PSO algorithm has been employed by many researchers for solving bi-level linear programming problems. In this paper, a PSO algorithm is presented for solving BPMLO by focused on decreasing computational time. The rest of the paper is organized as follows: In section 2, the problem formulation is provided. The proposed algorithm for solving bilevel programming with multiple linear objective functions at lower level is presented in section 3. In section 4, some numerical examples are given to demonstrate the proposed algorithm, while the conclusion is reached in section 5. This known technique is applicable to the system of Iorio as they are both directed to solving optimization problems. One of ordinary skill in the art before the effective filing date of the Applicant’s invention the Applicant’s invention would have recognized that applying the known technique of Matroud would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Matroud to the teachings of Iorio would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate hierarchical linear optimization into similar systems. Further, incorporating hierarchical linear optimization taught by Matroud to the system taught by Iorio would result in an improved system that improves speed of convergence and provide relatively simple solution (see Matroud Introduction).
As per Claim 4 Iorio teaches the system of Claim 1, wherein the server is further configured to: analyze, by the business level interpreter, variables and objective functions stored in the plan explanation datastore and provide an output in response to a submitted query. Iorio para. 40-41 as shown, a method 400 begins at step 400, where classification engine 204 within optimization engine 124 receives problem specification 115. At step 402, classification engine 204 maps problem specification 115 into problem/solution space 141. In doing so, classification engine 204 may generate a correlation value between problem specification 115 and each stored problem specification 207 in problem/solution space 141. A given correlation value reflects a degree of similarity between problem specification 115 and a given stored problem specification 207 in problem space 141.
At step 403, classification engine 204 determines whether problem specification 115 correlates sufficiently with only one stored problem specification 207 in problem/solution space 141. In the context of this disclosure, when the correlation value between problem specification 115 and a stored problem specification 207 exceeds a threshold value, then that correlation value is considered "sufficient." Further, para. 66 teaches persons skilled in the art may notice that the general approach described herein of classifying a problem and then executing a corresponding solution could also be applied recursively to select classification algorithms. In other words, an optimization algorithm could be applied to search a problem/solution space that includes other optimization algorithms, as described in greater detail below in conjunction with FIG. 8.
Claims 8, 11 are rejected for similar reason to those recited in claims 1, 4. Further, Iorio teaches A computer-implemented method, comprising: providing a server, (see Iorio Fig. 1).
Claims 15, 18 are rejected for similar reason to those recited in claims 1, 4. Further, Iorio teaches A non-transitory computer-readable medium embodied with software, the software when executed is configured to: perform the recited steps (see Iorio para. 72).
Claim(s) 5, 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iorio US 20140/149331 A1 in view of “Solving bi-level programming with multiple linear objectives at lower level using particle swarm optimization” (hereinafter Matroud) in view of Crawford US 5,943,244 A and in further view of US Pinkerton 20170178039 A1.
As per Claim 5 Iorio does not teach The system of Claim 4, wherein the submitted query is a user query or a standard batch query. However, Pinkerton para. 42-43 teach A risk query 218 is then received by the system 100. The risk query, described above, includes various dimensions by which the system 100 will analyze the risk exposure posed by the sets of agreements. The information received 204-218 is used to identify an appropriate calculator to calculate the risk exposure for each agreement of each set. This is accomplished by using uniform agreement definition language and risk query language so that an appropriate calculator is identified for each agreement. A calculator is selected based on a number of features and the relationship of the features of the query and the agreements being queried. Examples of features used to select one or more specific calculators include the output sought (as indicated in the risk query language statement) and the specific metrics sought as output. One benefit of this, as explained above, is that the user need not have a sophisticated knowledge of computer science or risk modeling to pose the risk query. Another feature used to select a calculator is a type of statistical calculation to be performed on modeled losses. Both Iorio and Pinkerton are directed to enterprise management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Iorio to include wherein the submitted query is a user query or a standard batch query as taught by Pinkerton to allow all parties involved to take appropriate mitigation measures (see abstract).
Claims 12 are rejected for similar reason to those recited in claims 5. Further, Iorio teaches A computer-implemented method, comprising: providing a server, (see Iorio Fig. 1).
Claims 19 are rejected for similar reason to those recited in claims 5. Further, Iorio teaches A non-transitory computer-readable medium embodied with software, the software when executed is configured to: perform the recited steps (see Iorio para. 72).
Conclusion
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
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