DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the application filed on 03/22/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/22/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Notes
Examiner cites particular paragraphs, figures, and line number in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
As a disclaimer, the use of underlining in direct quotes is done by the examiner for emphasis. Direct quotes are not originally underlined in the published references cited.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1, line 8, “the processor” lacks proper antecedent basis.
Claim 3, line 2, replace “solution” with --solutions--. Claims 10 and 17 have the same issue.
Claim 5, 12, and 19 “python programming” should be capitalized to --Python programming-- because it is a proper noun that references an official programming language.
Claims 7 and 14 “the sequence generation module” in dependent Claims are assumed to be a typo for --the sequence generation model-- directed to the corresponding element in independent Claims 1 and 8.
Claim 8, line 6, delete “perform one or more operations, the operations comprising”.
Claim 15, line 3, delete “cause the processors to preform operations to”.
Claims 2, 4, 6, 9, 11-13, 16, 18, and 20 depend on the objected claims and inherit the same issues.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4, 9-11, and 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2, line 4, “the model” is unclear whether it refers to “a sequence generation model” in line 12 or “the trained sequence generation model” in line 15 of the claim 1. For the examination purposes, it will be treated as --the sequence generation model--. Claim 9 and 16 have the same issue.
Claim 4, line 4, “these inputs and solutions” is not clear. For the examination purposes, they will be treated as --the plurality of inputs and the optimization solutions--. Claims 11 and 18 have the same issue.
Claims 3, 10, and 17 depend on the rejected claims and inherit the same issue.
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.
Step 1 Analysis:
Claims 1-7 are directed to a computer-implemented method and falls within the statutory category of processes; Claims 8-14 are directed to a computer system and fall within the statutory category of processes; Claims 15-20 are directed to a computer program product and fall within the statutory category of manufacture. Therefore, "Are the claims to a process, machine, manufacture or composition of matter?" Yes. In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon, or an abstract idea (see MPEP § 2106.04).
Regarding Claims 1, 8, 15:
Step 2A Prong 1 Analysis:
The claim limitation recites, generating an optimization solution verification [], wherein the optimization solution verification [] determines if an input satisfies one or more constraints associated with an optimization issue and responsive to the input satisfying the one or more constraints, calculate a value for the input associated with an objective function based on the one or more constraints; This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. For example, one of ordinary skill in the art may create steps with pen and paper outlining the optimization solution verification requirements to evaluate the value based on the constraint(s). Therefore, this limitation recites a mental process. (See MPEP 2106.04(a)(2), subsection III).
converting automatically, [], a [] of the optimization solution verification [] into a loss function incorporating the objective function calculation and constraint satisfaction; This limitation recites mathematical concepts in the form of an equation. For example, the phrase "loss function" is merely using a textual replacement for the particular equation
(x* ∈ arg minx ϵ χ∩Ω(p) f (x, p)). Therefore, this limitation recites a mathematical concept (See MPEP 2106.04(a)(2), subsection I).
generating, [], a plurality of random inputs to the optimization issue; This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. For example, a person may generate a plurality of random inputs to be used for the optimization issue with the assistance of pen of paper. Therefore, this limitation recites a mental process. (See § MPEP 2106.04(a)(2), subsection III).
Step 2A Prong 2 Analysis:
generating, [], an optimized solution for the optimization issue based on the trained sequence generation model; This limitation recites additional elements that merely recite instructions to implement the abstract idea of mathematical concepts on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
training, [], a sequence generation model with the loss function and the plurality of random inputs; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. The abstract ideas of the loss function and plurality of random inputs are merely applied to train the model. The training of the model will be further analyzed in Step 2B below, as being well-understood, routine, and conventional. (See § MPEP 2106.05(f)).
program; program code; These limitations recite additional elements that merely
recite instructions to implement an abstract idea on a generic computer, or merely uses a generic
computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
by the processor; This limitation recites additional elements that merely
recite instructions to implement an abstract idea on a generic computer, or merely uses a generic
computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Claim 1 additionally recites, A computer-implemented method for generating optimization solutions, the computer-implemented method comprising; Claim 8 additionally recites, A computer system for generating optimization solutions, the computer system comprising: a processor; a memory; and program instructions stored on a storage device, the program instructions executable by the processor to perform one or more operations, the operations comprising; Claim 15 additionally recites, A computer program product for generating optimization solutions, the computer program product comprising program instructions stored on a storage device, the program instructions executable by a processor to cause the processors to perform operations to; These limitations recite additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claims do not include additional elements that are sufficient to amount to significantly more
than the judicial exception. As discussed above with respect to the integration of the abstract ideas into
a practical application, all of the additional elements merely recite generic computer and computer
components, and merely applying the abstract idea, which is well-understood, routine, and conventional
(see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine,
and conventional.). Respectively, thus do not amount to significantly more than the judicial exception.
The claims are not patent eligible.
Regarding Claims 2, 9, 16:
Step 2A Prong 1 Analysis:
See corresponding analysis regarding Claims 1, 8, 15.
Step 2A Prong 2 Analysis:
updating, [] [] with one or more additional constraints and/or updated objectives; and; This claim recites the additional element “updating” which is merely an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exception into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
updating the loss function and retraining the model; This claim recites the additional elements “updating” and “retraining” which are mere insignificant extra-solution activities such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exception into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
by the processor; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
the program code; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”, and insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which is well-understood, routine, and conventional (see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.). Respectively, thus do not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 3, 10, 17:
Step 2A Prong 1 Analysis:
labeling, [] a second set of random solution, based on the updated program code; This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. For example, one of ordinary skill in the art may observe the updated program code to then label a second set of random solution, with the assistance of pen and paper. Therefore, this limitation recites a mental process. (See MPEP § 2106.04(a)(2), subsection III).
Step 2A Prong 2 Analysis:
tuning, [] the sequence generation model based on the labeled second set of random solutions; This claim recites the additional element “tuning” which is merely an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exceptions into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
by the processor; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”, and insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which is well-understood, routine, and conventional (see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.). Respectively, thus do not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 4, 11, 18:
Step 2A Prong 1 Analysis:
wherein the generated optimized solution for the optimization issue is a set of decision variable values; This limitation recites mathematical concepts. The "set of decision variable values" is merely describing a mathematical relationship based on the “optimized solution”. Therefore, this limitation recites a mathematical concept (See MPEP 2106.04(a)(2), subsection I).
wherein the decision variable values provide an optimized objective value; This limitation recites mathematical concepts. The "optimized objective value" is merely describing a mathematical relationship based on the “decision variable values”. Therefore, this limitation recites a mathematical concept (See MPEP 2106.04(a)(2), subsection I).
Step 2A Prong 2 Analysis:
when applied to the objective function within a domain space for the optimization issue; This limitation recites additional elements that indicate a field of use or technological environment in which to apply a judicial exception. The claim merely recites “within a domain space for the optimization issue” as a field of use/technological environment to perform the abstract idea(s) directed to mathematical concepts. (See MPEP 2106.05(h)).
and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation [] in a supervised manner; This claim recites the additional element “to tune” which is merely an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exception into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
model; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”, an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, and merely indicate a field of use or technological environment in which to apply a judicial exception, which is well-understood, routine, and conventional (see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.). Respectively, thus do not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 5, 12, 19:
Step 2A Prong 1 Analysis:
See corresponding analysis regarding Claims 1, 8, 15.
Step 2A Prong 2 Analysis:
wherein the optimization solution verification [] is based on python programming language code; This limitation recites additional elements that indicate a field of use or technological environment in which to apply a judicial exception. The claim merely recites “based on python programming” as a field of use/technological environment to perform the abstract idea(s) directed to mathematical concepts and mental processes. (See MPEP 2106.05(h)).
program; This limitation recites additional elements that merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea. (See § MPEP 2106.05(f)).
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all the additional elements amount to no more than generic computing components applying the abstract idea, and merely indicate a field of use or technological environment in which to apply a judicial exception, which is well-understood, routine, and conventional (See MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.). Thus, do not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 6, 13, 20:
Step 2A Prong 1 Analysis:
See corresponding analysis regarding Claims 1, 8, 15.
Step 2A Prong 2 Analysis:
wherein the optimization solution verification program is a spreadsheet based program; This limitation recites additional elements that indicate a field of use or technological environment in which to apply a judicial exception. The claim merely recites “spreadsheet based program” as a field of use/technological environment to perform the abstract idea(s) directed to mathematical concepts and mental processes. (See MPEP 2106.05(h)).
configured to receive one or more decision variables of the optimization issue as input; This claim recites the additional element “configured to receive” which is merely an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, which does not integrate the judicial exception into a practical application (see MPEP § 2106.05(g)), and will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a
practical application? No, even when viewed in combination, these additional elements do not integrate
the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claims will be analyzed further below in Step 2B as being well-understood, routine, and conventional.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are an insignificant extra-solution activity such as gathering, displaying, updating, transmitting, and storing data, and merely indicate a field of use or technological environment in which to apply a judicial exception, which is well-understood, routine, and conventional (see MPEP § 2106.05(d)(II) for court decisions recognizing that this activity is well-understood, routine, and conventional.). Respectively, thus do not amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claims 7, 14:
Step 2A Prong 1 Analysis:
See corresponding analysis in Claims 1, 8.
Step 2A Prong 2 Analysis:
wherein the sequence generation module is a transformer based deep learning network; This limitation recites additional elements that indicate a field of use or technological environment in which to apply a judicial exception. The claim merely recites “transformer based” as a field of use/technological environment to perform the abstract idea(s) directed to mathematical concepts and mental processes. (See MPEP 2106.05(h)).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements merely indicate a field of use or technological environment in which to apply a judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Wasserkrug et al. (U.S. Publication No. 20230237222 A1, hereinafter Wasserkrug).
The applied reference has a common joint inventor (Wasserkrug) and common assignee (International Business Machines Corporation) with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding Claim 1:
Wasserkrug discloses, “A computer-implemented method for generating optimization solutions, the computer-implemented method comprising” (In paragraph [0039], “The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”.).
“generating an optimization solution verification program, wherein the optimization solution verification program determines if an input satisfies one or more constraints associated with an optimization issue” (Paragraph [0005] teaches the optimization solution verification program, “generating a solution to the optimization problem using the automatically generated constraint and/or objective definitions and executing a computer-readable instruction based on the solution … Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint definitions to determine, for each solution, whether the values satisfy the corresponding constraints”.);
“and responsive to the input satisfying the one or more constraints” (Paragraph [0005], “the labeled constraint data may comprise a value of the objective function(s) computed based on the input values of each solution variable”.);
“calculate a value for the input associated with an objective function based on the one or more constraints” (In paragraph [0007], “Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint and/or objective definitions to determine, for each value, whether the value satisfies its corresponding constraints and/or objectives”.);
“converting automatically, by the processor, a program code of the optimization solution verification program into a loss function incorporating the objective function calculation and constraint satisfaction” (In paragraph [0016], “converting the originally defined constraints in any programming language into a format that is capable of being processed by mathematical optimization engines. This formal representation of the constraints can typically be solved by optimization engines”. In paragraph [0022], “The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s) (e.g., maximizes or minimizes the value of each such function). The computed value of an objective function may be referred to herein as the cost or reward depending on whether the value is to be minimized or maximized.” In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model … The optimization engine generates a solution that maximizes a reward or minimizes a cost”.);
(Examiner’s Note #1: The definition of a “loss function” is not provided in the specification of the examined case. Therefore, the BRI (Broadest Reasonable Interpretation) of the “loss function” is interpreted by the examiner as a type of objective function, where minimization is the training objective for a machine learning model. Note that a “loss function” is also referred to as “cost function”.1 Although a “loss function” is not explicitly stated in the reference, the definition of loss function is described. Subsequently, the reference specifies that a loss function may be used in the invention.);
(Examiner’s Note #2: The claim limitations including a processor is covered in the reference by Wasserkrug, “the computing device 100 can include fewer or additional components not illustrated in FIG. 1 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Furthermore, any of the functionalities of the data generation component 128, constraints and/or objectives learning algorithms 132, optimization engine 136, and active learning module 138 are partially, or entirely, implemented in hardware and/or in the processor 102. For example, the functionality may be implemented with … logic implemented in the processor 102” (paragraph [0027]).
“generating, by the processor, a plurality of random inputs to the optimization issue;” (In paragraph [0024], “To generate this data 128, possible solutions to the optimization problem are generated. Each possible solution includes a selected value for the each of the decision variables. The selected values for each solution may be generated randomly … The solution is also provided as input to each constraint”.);
“training, by the processor, a sequence generation model with the loss function and the plurality of random inputs” (In paragraph [0025], “As with the formal constraint model, the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Examiner’s Note #3: The BRI (Broadest Reasonable Interpretation) of “sequence generation model” is interpreted by the examiner in light of the examined cases’ specification in paragraph [0082], “decision generation model (i.e., a sequence generation model) in response to updated constraints and conditions in an optimization issue”.);
“and generating, by the processor, an optimized solution for the optimization issue based on the trained sequence generation model” (In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model generated at block 206 … The optimization engine generates a solution that maximizes a reward or minimizes a cost. As mentioned above in relation to FIG. 1, the solution can cause one or more computer readable instructions to be executed by a processor”. Note previously in paragraph [0032], the “At block 206 … The formal model … using a machine learning algorithm that are trained”.).
Regarding Claim 2:
Wasserkrug discloses, “The computer-implemented method of claim 1, further comprising; updating, by the processor, the program code with one or more additional constraints and/or updated objectives; and updating the loss function and retraining the model” (In paragraph [0026], “the active learning module 138 may be configured to select additional input data to be input to the data generation component 128. The active learning module 138 can select the input data in terms of values to all or some of the decision variables, based on the results of previous iterations of the constraints and/or objectives learning algorithms 132”. Where the context given in paragraph [0025], “The constraint and/or objectives learning algorithms 132 generate a formal constraints and objectives model 134 … the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. And the model In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.).
Regarding Claim 3:
Wasserkrug discloses, “The computer-implemented method of claim 2, further comprising: labeling, by the processor, a second set of random solution, based on the updated program code; and” (In paragraph [0030], “The resulting data is a set of labeled data that includes the solution (i.e., the variable values)”. In paragraph [0031], “the variable values may be selected randomly … For example, the active learning module can generate a sample solution, input the solution to the programmatic specification and generate a single instance of labeled constraint data. The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. Where the selecting of the next solution may be random, as shown in paragraph [0024], “The selected values for each solution may be generated randomly … The result is a set of labeled constraints and objectives data”.);
“tuning, by the processor, the sequence generation model based on the labeled second set of random solutions” (In paragraph [0031), “The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Examiner’s Note #4: Note the reference’s connection between the active learning module and formal model, through the optimization model; In paragraph [0024], “The data generation component 128 the constraints 126 and the objectives are used to generate the optimization model learning data 128 … The result is a set of labeled constraints and objectives data 130”, in paragraph [0025], “The constraint and/or objectives learning algorithms 132 generate a formal constraints and objectives model 134 using the labeled constraints and objectives data 130”, and in paragraph [0026], “the active learning module 138 may be configured to select additional input data to be input to the data generation component 128. The active learning module 138 can select the input data in terms of values to all or some of the decision variables, based on the results of previous iterations of the constraints and/or objectives learning algorithms 132”.).
Regarding Claim 4:
Wasserkrug discloses, “The computer-implemented method of claim 1, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0016], “optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0022], “The objective function(s) 124 may be specified by a developer codifying the domain knowledge of the business user and can include several decision variables. The objective function(s) is/are specified as a function in any suitable programming language based on the business objects defined, and must return a real number as a result. The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s)”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraph [0016], “learning a formal model of an optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made … The coded constraint specifications are then used in conjunction with a data generation procedure to generate a high quality labeled data set. This data set can then be used to learn a formal mathematical model of the constraints and objectives that is formatted in a precise manner that is tailored for the specific optimization engine to be used … the present techniques may also be coupled with active learning wherein the programmatic specification can be used to provide feedback”.).
Regarding Claim 5:
Wasserkrug discloses, “The computer-implemented method of claim 1, wherein the optimization solution verification program is based on python programming language code.” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others”.).
Regarding Claim 8:
Wasserkrug discloses, “A computer system for generating optimization solutions, the computer system comprising: a processor; a memory; and program instructions stored on a storage device, the program instructions executable by the processor to perform one or more operations, the operations comprising” (In paragraph [0039], “The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”. In paragraph [0002], “a system for learning all or part of a mathematical optimization model can include a memory device to store human-specified constraint and/or objective definitions that have been programmed in a general-purpose programming language by a human user. The system may include a processor”.);
“generate an optimization solution verification program, wherein the optimization solution verification program determines if an input satisfies one or more constraints associated with an optimization issue” (Paragraph [0005] teaches the optimization solution verification program, “generating a solution to the optimization problem using the automatically generated constraint and/or objective definitions and executing a computer-readable instruction based on the solution … Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint definitions to determine, for each solution, whether the values satisfy the corresponding constraints”.);
“and responsive to the input satisfying the one or more constraints” (Paragraph [0005], “the labeled constraint data may comprise a value of the objective function(s) computed based on the input values of each solution variable”.);
“calculate a value for the input associated with an objective function based on the one or more constraints” (In paragraph [0007], “Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint and/or objective definitions to determine, for each value, whether the value satisfies its corresponding constraints and/or objectives”.);
“convert automatically, by the processor, a program code of the optimization solution verification program into a loss function incorporating the objective function calculation and constraint satisfaction” (In paragraph [0016], “converting the originally defined constraints in any programming language into a format that is capable of being processed by mathematical optimization engines. This formal representation of the constraints can typically be solved by optimization engines”. In paragraph [0022], “The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s) (e.g., maximizes or minimizes the value of each such function). The computed value of an objective function may be referred to herein as the cost or reward depending on whether the value is to be minimized or maximized.” In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model … The optimization engine generates a solution that maximizes a reward or minimizes a cost”.);
(Refer to Examiner’s Note #1 for the mapping of a “loss function” to the reference.);
(Refer to Examiner’s Note #2 for additional hardware mapping such as the processor, memory.);
“generate a plurality of random inputs to the optimization issue;” (In paragraph [0024], “To generate this data 128, possible solutions to the optimization problem are generated. Each possible solution includes a selected value for the each of the decision variables. The selected values for each solution may be generated randomly … The solution is also provided as input to each constraint”.);
“train a sequence generation model with the loss function and the plurality of random inputs” (In paragraph [0025], “As with the formal constraint model, the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Refer to Examiner’s Note #3 for The BRI of “sequence generation model”.);
“and generate an optimized solution for the optimization issue based on the trained sequence generation model” (In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model generated at block 206 … The optimization engine generates a solution that maximizes a reward or minimizes a cost. As mentioned above in relation to FIG. 1, the solution can cause one or more computer readable instructions to be executed by a processor”. Note previously in paragraph [0032], the “At block 206 … The formal model … using a machine learning algorithm that are trained”.).
Regarding Claim 9:
Wasserkrug discloses, “The computer system of claim 8, further comprising program instructions to: update the program code with one or more additional constraints and/or updated objectives; and update the loss function and retraining the model” (In paragraph [0026], “the active learning module 138 may be configured to select additional input data to be input to the data generation component 128. The active learning module 138 can select the input data in terms of values to all or some of the decision variables, based on the results of previous iterations of the constraints and/or objectives learning algorithms 132”. Where the context given in paragraph [0025], “The constraint and/or objectives learning algorithms 132 generate a formal constraints and objectives model 134 … the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. And the model In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.).
Regarding Claim 10:
Wasserkrug discloses, “The computer system of claim 9, further comprising program instructions to: label a second set of random solution, based on the updated program code; and” (In paragraph [0030], “The resulting data is a set of labeled data that includes the solution (i.e., the variable values)”. In paragraph [0031], “the variable values may be selected randomly … For example, the active learning module can generate a sample solution, input the solution to the programmatic specification and generate a single instance of labeled constraint data. The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. Where the selecting of the next solution may be random, as shown in paragraph [0024], “The selected values for each solution may be generated randomly … The result is a set of labeled constraints and objectives data”.);
“tune the sequence generation model based on the labeled second set of random solutions” (In paragraph [0031), “The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Refer to Examiner’s Note #4 for the reference’s connection between the active learning module and formal model, through the optimization model.).
Regarding Claim 11:
Wasserkrug discloses, “The computer system of claim 8, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0016], “optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0022], “The objective function(s) 124 may be specified by a developer codifying the domain knowledge of the business user and can include several decision variables. The objective function(s) is/are specified as a function in any suitable programming language based on the business objects defined, and must return a real number as a result. The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s)”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraph [0016], “learning a formal model of an optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made … The coded constraint specifications are then used in conjunction with a data generation procedure to generate a high quality labeled data set. This data set can then be used to learn a formal mathematical model of the constraints and objectives that is formatted in a precise manner that is tailored for the specific optimization engine to be used … the present techniques may also be coupled with active learning wherein the programmatic specification can be used to provide feedback”.).
Regarding Claim 12:
Wasserkrug discloses, “The computer system of claim 8, wherein the optimization solution verification program is based on python programming language code” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others”.).
Regarding Claim 15:
Wasserkrug discloses, “A computer program product for generating optimization solutions, the computer program product comprising program instructions stored on a storage device, the program instructions executable by a processor to cause the processors to perform operations to” (In paragraph [0039], “The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”. In paragraph [0007], “An exemplary computer program product may comprise program instructions executable by the processor to cause the processor to generate a formal objective model”.);
“generate an optimization solution verification program, wherein the optimization solution verification program determines if an input satisfies one or more constraints associated with an optimization issue” (Paragraph [0005] teaches the optimization solution verification program, “generating a solution to the optimization problem using the automatically generated constraint and/or objective definitions and executing a computer-readable instruction based on the solution … Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint definitions to determine, for each solution, whether the values satisfy the corresponding constraints”.);
“and responsive to the input satisfying the one or more constraints” (Paragraph [0005], “the labeled constraint data may comprise a value of the objective function(s) computed based on the input values of each solution variable”.);
“calculate a value for the input associated with an objective function based on the one or more constraints” (In paragraph [0007], “Generating the labeled data may also comprise inputting the values to an objective function to compute a value of the objective function, and inputting the values to the human-generated constraint and/or objective definitions to determine, for each value, whether the value satisfies its corresponding constraints and/or objectives”.);
“convert automatically, by the processor, a program code of the optimization solution verification program into a loss function incorporating the objective function calculation and constraint satisfaction” (In paragraph [0016], “converting the originally defined constraints in any programming language into a format that is capable of being processed by mathematical optimization engines. This formal representation of the constraints can typically be solved by optimization engines”. In paragraph [0022], “The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s) (e.g., maximizes or minimizes the value of each such function). The computed value of an objective function may be referred to herein as the cost or reward depending on whether the value is to be minimized or maximized.” In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model … The optimization engine generates a solution that maximizes a reward or minimizes a cost”.);
(Refer to Examiner’s Note #1 for the mapping of a “loss function” to the reference.);
(Refer to Examiner’s Note #2 for additional hardware mapping such as the processor, memory.).
“generate a plurality of random inputs to the optimization issue;” (In paragraph [0024], “To generate this data 128, possible solutions to the optimization problem are generated. Each possible solution includes a selected value for the each of the decision variables. The selected values for each solution may be generated randomly … The solution is also provided as input to each constraint”.);
“train a sequence generation model with the loss function and the plurality of random inputs” (In paragraph [0025], “As with the formal constraint model, the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Refer to Examiner’s Note #3 for The BRI of “sequence generation model”.);
“and generate an optimized solution for the optimization issue based on the trained sequence generation model” (In paragraph [0033], “the optimization problem is solved using an optimization engine and the formal constraint model generated at block 206 … The optimization engine generates a solution that maximizes a reward or minimizes a cost. As mentioned above in relation to FIG. 1, the solution can cause one or more computer readable instructions to be executed by a processor”. Note previously in paragraph [0032], the “At block 206 … The formal model … using a machine learning algorithm that are trained”.).
Regarding Claim 16:
Wasserkrug discloses, “The computer program product of claim 15, further comprising program instructions to: update the program code with one or more additional constraints and/or updated objectives; and update the loss function and retraining the model” (In paragraph [0026], “the active learning module 138 may be configured to select additional input data to be input to the data generation component 128. The active learning module 138 can select the input data in terms of values to all or some of the decision variables, based on the results of previous iterations of the constraints and/or objectives learning algorithms 132”. Where the context given in paragraph [0025], “The constraint and/or objectives learning algorithms 132 generate a formal constraints and objectives model 134 … the formal objective model describes the objective function that had been learned from the constraints and objectives data 130 and is formatted in way that is tailored to a specific optimization engine”. And the model In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.).
Regarding Claim 17:
Wasserkrug discloses, “The computer program product of claim 16, further comprising program instructions to: label a second set of random solution, based on the updated program code; and” (In paragraph [0030], “The resulting data is a set of labeled data that includes the solution (i.e., the variable values)”. In paragraph [0031], “the variable values may be selected randomly … For example, the active learning module can generate a sample solution, input the solution to the programmatic specification and generate a single instance of labeled constraint data. The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. Where the selecting of the next solution may be random, as shown in paragraph [0024], “The selected values for each solution may be generated randomly … The result is a set of labeled constraints and objectives data”.);
“tune the sequence generation model based on the labeled second set of random solutions” (In paragraph [0031), “The active learning module may be programmed to select the next solution based on the data, constraints and objectives obtained from the previous iteration or iterations”. In paragraph [0032], “The formal model of the constraints and objectives can be generated automatically using a machine learning algorithm that are trained using the labeled constraint data”.);
(Refer to Examiner’s Note #4 for the reference’s connection between the active learning module and formal model, through the optimization model.).
Regarding Claim 18:
Wasserkrug discloses, “The computer program product of claim 15, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0016], “optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0022], “The objective function(s) 124 may be specified by a developer codifying the domain knowledge of the business user and can include several decision variables. The objective function(s) is/are specified as a function in any suitable programming language based on the business objects defined, and must return a real number as a result. The goal of the optimization process is to determine a solution that optimizes the value of the objective function(s)”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraph [0016], “learning a formal model of an optimization model that includes constraints and objectives defined over a set of decision variables, which specify the possible decisions that can be made … The coded constraint specifications are then used in conjunction with a data generation procedure to generate a high quality labeled data set. This data set can then be used to learn a formal mathematical model of the constraints and objectives that is formatted in a precise manner that is tailored for the specific optimization engine to be used … the present techniques may also be coupled with active learning wherein the programmatic specification can be used to provide feedback”.).
Regarding Claim 19:
Wasserkrug discloses, “The computer program product of claim 15, wherein the optimization solution verification program is based on python programming language code” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others”.).
Claims 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention as being anticipated by Harris et al. (U.S. Publication No. 20210027182-A1, hereinafter Harris).
Regarding Claim 1:
Harris discloses, “A computer-implemented method for generating optimization solutions, the computer-implemented method comprising” (In paragraph [0013], “The method can be performed by one or more server computers”.);
“generating an optimization solution verification program, wherein the optimization solution verification program determines if an input satisfies one or more constraints associated with an optimization issue and responsive to the input satisfying the one or more constraints” (In paragraph [0046], “The series of artificial intelligence algorithms can modify the training data prior to building the model … These algorithms can be driven and controlled by a modeling behavior tree that initializes and runs each of the algorithms. The modeling behavior tree that drives the model building process can be tuned by an optimization behavior tree based on an evaluation of the performance of the model. Thus, the machine learning model building process is “automated” because the modeling behavior tree is used to monitor new training data and drive the model building process … This automatic self-correction enables the model to maintain its accuracy should characteristics of the training data shift overtime”.);
“calculate a value for the input associated with an objective function based on the one or more constraints” (In paragraph [0072], “The overall path that is taken by the agents when finding a solution can be determined based on the error structure (e.g., gradient) of the information space in relation to the target goal … The agents may then determine a path and may determine the cost of the path and compare it to a predetermined cost requirement. The agents may continuously calculate the cost of their determined paths until their chosen path has met the predetermined cost requirement”.);
“converting automatically, by the processor, a program code of the optimization solution verification program into a loss function incorporating the objective function calculation and constraint satisfaction” (In paragraph [0015], “a supervised machine learning algorithm can be used to build a model 130 based on a training sample selected from among the data records … The building (e.g., training) of the model can involve an iterative process of updating the model in order to minimize a loss function that quantifies the difference between the model's prediction and the target output values”. Further, in paragraph [0068], “the server computer can determine one or more inferred edge connections between the nodes of the topological graph using an optimization algorithm. The one or more inferred edge connections can reduce a cost function based on the results associated with the new set of previous requests and stored results associated with the stored set of historical requests”.);
“generating, by the processor, a plurality of random inputs to the optimization issue” (In paragraphs [0055]-[0056], “The first graph 301 of FIG. 3 illustrates the training data expressed as a topological graph. In some embodiments, the topological graph can be created based on a training sample of the new and historical requests stored in the data storage 210. The sample can be selected from the stored requests randomly”.);
“training, by the processor, a sequence generation model with the loss function and the plurality of random inputs” (In paragraph [0053], “The training data for training the model can be based on both the new set of previous requests and the stored set of historical requests to ensure that the model is up to date with trending parameters and characteristics of the requests”. In paragraph [0071], “Optimal paths (e.g., inferred edge connections) may be determined based on a cost function or goal function, such as a signal-to-noise criteria … signal-to-noise criteria may be, for example, a ratio of the number of fraudulent authentication requests to non-fraudulent authentication requests for given inputs in a detected path. The cost function can be based on the training data and their corresponding results. A gradient may describe whether the cost function was successfully decreased for each of the respective paths determined by each of the agents … the goal being to approach a global optimal path (i.e. shortest or least costly path within the information space to reach the specified goal). In this manner, new features may be added”. In paragraph [0072], “A random search may be performed by the agents, with each of the agents initialized within a given domain of the information space”. Note in the support used in paragraph [0071] above, that the feature is used to train the model as stated in paragraph [0033], “A ‘feature’ may refer to a specific set of data to be used in training a machine learning model … An input feature may be data that is compiled and expressed in a form that may be accepted and used to train an artificial intelligence model as useful information for making predictions”, as well as requests as stated in paragraph [0050], “The new and historical requests may be stored to be used as training data for models builds”.);
“and generating, by the processor, an optimized solution for the optimization issue based on the trained sequence generation model” (In paragraph [0046], “The modeling behavior tree that drives the model building process can be tuned by an optimization behavior tree based on an evaluation of the performance of the model”. Further, in paragraph [0037]-[0038], “one or more agents may be used to calculate a solution to an optimization problem. A plurality of agents that work together to solve a given problem, such as in the case of ant colony optimization algorithm, may be referred to as a ‘colony.’ The term ‘epoch’ may refer to a period of time, e.g., in training a machine learning model. During training of learners in a learning algorithm, each epoch may pass after a defined set of steps have been completed. For example, in ant colony optimization, each epoch may pass after all computational agents have found solutions and have calculated the cost of their solutions”.).
Regarding Claim 2:
Harris discloses, “The computer-implemented method of claim 1, further comprising; updating, by the processor, the program code with one or more additional constraints and/or updated objectives; and updating the loss function and retraining the model” (In paragraph [0094], “the server computer can update the modeling behavior tree, to obtain an optimized modeling behavior tree, based on the evaluated performance of the predictive model. As discussed above, the modeling behavior tree sets parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm”. Further in paragraph [0015], “The building (e.g., training) of the model can involve an iterative process of updating the model in order to minimize a loss function … the machine learning algorithm “learns” how to make better predictions over successive iterations”. In paragraph [0072], “The agents may then determine a path and may determine the cost of the path and compare it to a predetermined cost requirement. The agents may continuously calculate the cost of their determined paths until their chosen path has met the predetermined cost requirement. The agents may then begin to converge to a solution and may communicate the error of the chosen solution in relation to the target goal. The agents may update a global feedback level, indicating the error gradient, for a path”, where as stated in paragraph [0071], “The cost function can be based on the training data and their corresponding results. A gradient may describe whether the cost function was successfully decreased for each of the respective paths determined by each of the agents … In this manner, new features may be added to the graph, in the form of newly inferred connections between input nodes and output nodes”.).
Regarding Claim 3:
Harris discloses, “The computer-implemented method of claim 2, further comprising: labeling, by the processor, a second set of random solution, based on the updated program code;” (In paragraph [0053], “In this example, the results associated with the new set of previous requests may be a scoring-value determine by the model for the corresponding request. The results associated with the new set of previous requests may also include a label”. In paragraph [0056], “The sample can be selected from the stored requests randomly”.);
“and tuning, by the processor, the sequence generation model based on the labeled second set of random solutions” (In paragraph [0096], “By tuning the modeling behavior tree 280, the parameters and settings for the various algorithms can be updated to suit the different incoming data, thereby improving model performance in later builds”.).
Regarding Claim 4:
Harris discloses, “The computer-implemented method of claim 1, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0102], “After the model has been built and the set of decision rules has been generated, they may be used to perform decision making in an operational setting”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0072], “The overall path that is taken by the agents when finding a solution can be determined based on the error structure (e.g., gradient) of the information space in relation to the target goal. A random search may be performed by the agents, with each of the agents initialized within a given domain of the information space”. In paragraph [0103], “The server computer can load the predictive model into a system memory ... the request may be received in a message from a client device sent over a network. In some embodiments, the server computer can extract or reformat the request to suit the model. Then, the server computer can apply the new request to the predictive model to obtain a request score. The server computer can then determine a decision based on the request score using the set of binary decision rules. The server computer can generate a response indicating the decision”. Where in paragraph [0003] defines the decision as a value, “The binary decision rules can set a threshold value for a continuous score determined by the predictive model”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraphs [0112]-[0113], “If the percentage difference between the new and old models is greater than a predetermined threshold value, indicating that there is new information (YES at 511), then the model building process continues to generate decision rules corresponding to the new model, at 512, as discussed above. Then the modeling behavior tree used to drive the model building process can be tuned using the Evolutionary Learner AI”.).
Regarding Claim 5:
Harris discloses, “The computer-implemented method of claim 1, wherein the optimization solution verification program is based on python programming language code” (In paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language such as, for example, Java, C, C++, C #, Objective-C, Swift, or scripting language such as Perl or Python using, for example, conventional or object-oriented techniques.”.).
Regarding Claim 8:
Harris discloses, “A computer system for generating optimization solutions, the computer system comprising: a processor; a memory; and program instructions stored on a storage device, the program instructions executable by the processor to perform one or more operations, the operations comprising” (In paragraph [0003], “The computer system can include a system memory, one or more processors, and a computer readable storage medium. The computer readable storage medium of the computer system can store instructions that, when executed by the one or more processors, cause the one or more processors to perform certain functions for building machine learning models”.);
Regarding Claim 9:
Harris discloses, “The computer system of claim 8, further comprising program instructions to: update the program code with one or more additional constraints and/or updated objectives; and update the loss function and retraining the model” (In paragraph [0094], “the server computer can update the modeling behavior tree, to obtain an optimized modeling behavior tree, based on the evaluated performance of the predictive model. As discussed above, the modeling behavior tree sets parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm”. Further in paragraph [0015], “The building (e.g., training) of the model can involve an iterative process of updating the model in order to minimize a loss function … the machine learning algorithm “learns” how to make better predictions over successive iterations”. In paragraph [0072], “The agents may then determine a path and may determine the cost of the path and compare it to a predetermined cost requirement. The agents may continuously calculate the cost of their determined paths until their chosen path has met the predetermined cost requirement. The agents may then begin to converge to a solution and may communicate the error of the chosen solution in relation to the target goal. The agents may update a global feedback level, indicating the error gradient, for a path”, where as stated in paragraph [0071], “The cost function can be based on the training data and their corresponding results. A gradient may describe whether the cost function was successfully decreased for each of the respective paths determined by each of the agents … In this manner, new features may be added to the graph, in the form of newly inferred connections between input nodes and output nodes”.).
Regarding Claim 10:
Harris discloses, “The computer system of claim 9, further comprising program instructions to: label a second set of random solution, based on the updated program code;” (In paragraph [0053], “In this example, the results associated with the new set of previous requests may be a scoring-value determine by the model for the corresponding request. The results associated with the new set of previous requests may also include a label”. In paragraph [0056], “The sample can be selected from the stored requests randomly”.);
“and tune the sequence generation model based on the labeled second set of random solutions” (In paragraph [0096], “By tuning the modeling behavior tree 280, the parameters and settings for the various algorithms can be updated to suit the different incoming data, thereby improving model performance in later builds”.).
Regarding Claim 11:
Harris discloses, “The computer system of claim 8, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0102], “After the model has been built and the set of decision rules has been generated, they may be used to perform decision making in an operational setting”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0072], “The overall path that is taken by the agents when finding a solution can be determined based on the error structure (e.g., gradient) of the information space in relation to the target goal. A random search may be performed by the agents, with each of the agents initialized within a given domain of the information space”. In paragraph [0103], “The server computer can load the predictive model into a system memory ... the request may be received in a message from a client device sent over a network. In some embodiments, the server computer can extract or reformat the request to suit the model. Then, the server computer can apply the new request to the predictive model to obtain a request score. The server computer can then determine a decision based on the request score using the set of binary decision rules. The server computer can generate a response indicating the decision”. Where in paragraph [0003] defines the decision as a value, “The binary decision rules can set a threshold value for a continuous score determined by the predictive model”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraphs [0112]-[0113], “If the percentage difference between the new and old models is greater than a predetermined threshold value, indicating that there is new information (YES at 511), then the model building process continues to generate decision rules corresponding to the new model, at 512, as discussed above. Then the modeling behavior tree used to drive the model building process can be tuned using the Evolutionary Learner AI”.).
Regarding Claim 12:
Harris discloses, “The computer system of claim 8, wherein the optimization solution verification program is based on python programming language code.” (In paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language such as, for example, Java, C, C++, C #, Objective-C, Swift, or scripting language such as Perl or Python using, for example, conventional or object-oriented techniques.”.).
Regarding Claim 15:
Harris discloses, “A computer program product for generating optimization solutions, the computer program product comprising program instructions stored on a storage device, the program instructions executable by a processor to cause the processors to perform operations to” (In paragraph [0156], “As such, a computer readable medium according to an embodiment of the present invention may be created using a data signal encoded with such programs. Computer readable media encoded with the program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer readable medium may reside on or within a single computer product (e.g. a hard drive, a CD, or an entire computer system)”. And in paragraph [0003], “The computer readable storage medium of the computer system can store instructions that, when executed by the one or more processors, cause the one or more processors to perform certain functions for building machine learning models”.);
Regarding Claim 16:
Harris discloses, “The computer program product of claim 15, further comprising program instructions to: update the program code with one or more additional constraints and/or updated objectives; and update the loss function and retraining the model” (In paragraph [0094], “the server computer can update the modeling behavior tree, to obtain an optimized modeling behavior tree, based on the evaluated performance of the predictive model. As discussed above, the modeling behavior tree sets parameters for initializing the community detection algorithm, the optimization algorithm, and the supervised machine learning algorithm”. Further in paragraph [0015], “The building (e.g., training) of the model can involve an iterative process of updating the model in order to minimize a loss function … the machine learning algorithm “learns” how to make better predictions over successive iterations”. In paragraph [0072], “The agents may then determine a path and may determine the cost of the path and compare it to a predetermined cost requirement. The agents may continuously calculate the cost of their determined paths until their chosen path has met the predetermined cost requirement. The agents may then begin to converge to a solution and may communicate the error of the chosen solution in relation to the target goal. The agents may update a global feedback level, indicating the error gradient, for a path”, where as stated in paragraph [0071], “The cost function can be based on the training data and their corresponding results. A gradient may describe whether the cost function was successfully decreased for each of the respective paths determined by each of the agents … In this manner, new features may be added to the graph, in the form of newly inferred connections between input nodes and output nodes”.).
Regarding Claim 17:
Harris discloses, “The computer program product of claim 16, further comprising program instructions to: label a second set of random solution, based on the updated program code;” (In paragraph [0053], “In this example, the results associated with the new set of previous requests may be a scoring-value determine by the model for the corresponding request. The results associated with the new set of previous requests may also include a label”. In paragraph [0056], “The sample can be selected from the stored requests randomly”.);
“and tune the sequence generation model based on the labeled second set of random solutions” (In paragraph [0096], “By tuning the modeling behavior tree 280, the parameters and settings for the various algorithms can be updated to suit the different incoming data, thereby improving model performance in later builds”.).
Regarding Claim 18:
Harris discloses, “The computer program product of claim 15, wherein the generated optimized solution for the optimization issue is a set of decision variable values” (In paragraph [0102], “After the model has been built and the set of decision rules has been generated, they may be used to perform decision making in an operational setting”.);
“wherein the decision variable values provide an optimized objective value when applied to the objective function within a domain space for the optimization issue” (In paragraph [0072], “The overall path that is taken by the agents when finding a solution can be determined based on the error structure (e.g., gradient) of the information space in relation to the target goal. A random search may be performed by the agents, with each of the agents initialized within a given domain of the information space”. In paragraph [0103], “The server computer can load the predictive model into a system memory ... the request may be received in a message from a client device sent over a network. In some embodiments, the server computer can extract or reformat the request to suit the model. Then, the server computer can apply the new request to the predictive model to obtain a request score. The server computer can then determine a decision based on the request score using the set of binary decision rules. The server computer can generate a response indicating the decision”. Where in paragraph [0003] defines the decision as a value, “The binary decision rules can set a threshold value for a continuous score determined by the predictive model”.);
“and in which these inputs and solutions generated by the optimized value and satisfying the constraints are used to tune the sequence generation model in a supervised manner” (In paragraphs [0112]-[0113], “If the percentage difference between the new and old models is greater than a predetermined threshold value, indicating that there is new information (YES at 511), then the model building process continues to generate decision rules corresponding to the new model, at 512, as discussed above. Then the modeling behavior tree used to drive the model building process can be tuned using the Evolutionary Learner AI”.).
Regarding Claim 19:
Harris discloses, “The computer program product of claim 15, wherein the optimization solution verification program is based on python programming language code.” (In paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language such as, for example, Java, C, C++, C #, Objective-C, Swift, or scripting language such as Perl or Python using, for example, conventional or object-oriented techniques.”.).
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.
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 (Wasserkrug; International Business Machines Corporation) 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 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al. (U.S. Publication No. 20210027182-A1, hereinafter Harris,) in view of Fine et al. (U.S. Publication No. 20070010975 A1, hereinafter Fine).
Regarding Claim 6:
Harris discloses, “The computer-implemented method of claim 1, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0142], “The method can also include steps for receiving, by the server computer, a new request in real time, applying the new request to the predictive model to obtain a request score, and determining a decision based on the request score using the set of binary decision rules”. Although Harris does not explicitly disclose a spreadsheet based program, Harris more broadly discloses any suitable computer language in paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language”.);
Harris does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Harris by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem (Fine, Abstract).
Regarding Claim 13:
Harris discloses, “The computer system of claim 8, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0142], “The method can also include steps for receiving, by the server computer, a new request in real time, applying the new request to the predictive model to obtain a request score, and determining a decision based on the request score using the set of binary decision rules”. Although Harris does not explicitly disclose a spreadsheet based program, Harris more broadly discloses any suitable computer language in paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language”.);
Harris does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Harris by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem (Fine, Abstract).
Regarding Claim 20:
Harris discloses, “The computer program product of claim 15, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0142], “The method can also include steps for receiving, by the server computer, a new request in real time, applying the new request to the predictive model to obtain a request score, and determining a decision based on the request score using the set of binary decision rules”. Although Harris does not explicitly disclose a spreadsheet based program, Harris more broadly discloses any suitable computer language in paragraph [0154], “Any of the software components or functions described in this application may be implemented as software code to be executed by a processor, or more than one processor, using any suitable computer language”.);
Harris does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Harris by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem (Fine, Abstract).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug in view of Fine.
Regarding Claim 6:
Wasserkrug discloses, “The computer-implemented method of claim 1, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others … Each constraint describes limitations on or more decision variables that have been defined by a user with domain knowledge of the particular business problem”. Although Wasserkrug does not explicitly disclose a spreadsheet program, Wasserkrug more broadly discloses any general programming language may be used.);
Wasserkrug does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Wasserkrug by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem.
Regarding Claim 13:
Wasserkrug discloses, “The computer system of claim 8, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others … Each constraint describes limitations on or more decision variables that have been defined by a user with domain knowledge of the particular business problem”. Although Wasserkrug does not explicitly disclose a spreadsheet program, Wasserkrug more broadly discloses any general programming language may be used.);
Wasserkrug does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Wasserkrug by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem.
Regarding Claim 20:
Wasserkrug discloses, “The computer program product of claim 15, wherein the optimization solution verification program is a [] configured to receive one or more decision variables of the optimization issue as input” (In paragraph [0029], “At block 202, programmatically specified constraints and objectives are received. The constraints may be specified using any general programming language such as C, C+, C++, Python, Java, and others … Each constraint describes limitations on or more decision variables that have been defined by a user with domain knowledge of the particular business problem”. Although Wasserkrug does not explicitly disclose a spreadsheet program, Wasserkrug more broadly discloses any general programming language may be used.);
Wasserkrug does not disclose however Fine discloses, “spreadsheet based program” (In paragraph [0084], “Next, at step 42 a model of the design-under-test is prepared by known methods, suitable for automated processing by a test generator. This can be done, for example, by creating spreadsheet files, which can then be parsed and processed by the test generator”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Wasserkrug by adopting the teaching of Fine to combine a spreadsheet based program into the solution verification program; motivated by the same goal to obtain optimization of a decision making process, based on specified constraints of a given problem.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Harris in view of Chandraker et al. (U.S. Publication No. 20170124433 A1, hereinafter Chandraker).
Regarding Claim 7:
Harris discloses, “The computer-implemented method of claim 1, wherein the sequence generation module is a []” (In paragraph [0015], “For example, the model 130 can be built using linear regression, nearest neighbor, gradient boosting, or neural network algorithms”.);
Harris does not disclose however Chandraker discloses, “transformer based deep learning network” (In paragraph [0007], “A system for training a deep learning network is presented. The system includes a memory and a processor in communication with the memory, wherein the processor is configured to receive a first image and a second image, mine exemplar thin-plate spline (TPS) to determine transformations for generating point correspondences between the first and second images, use artificial point correspondences to train the deep neural network, learn and use the TPS transformation output through a spatial transformer, and apply heuristics for selecting an acceptable set of images to match for accurate reconstruction”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Harris by adopting the teaching Chandraker to combine a transformer based deep learning network into the model; motivated by the common goal to obtain optimization of solution building, based on constraints of a given issue (Chandraker, paragraph [0025]).
Regarding Claim 14:
Harris discloses, “The computer system of claim 8, wherein the sequence generation module is a []” (In paragraph [0015], “For example, the model 130 can be built using linear regression, nearest neighbor, gradient boosting, or neural network algorithms”.);
Harris does not disclose however Chandraker discloses, “transformer based deep learning network” (In paragraph [0007], “A system for training a deep learning network is presented. The system includes a memory and a processor in communication with the memory, wherein the processor is configured to receive a first image and a second image, mine exemplar thin-plate spline (TPS) to determine transformations for generating point correspondences between the first and second images, use artificial point correspondences to train the deep neural network, learn and use the TPS transformation output through a spatial transformer, and apply heuristics for selecting an acceptable set of images to match for accurate reconstruction”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Harris by adopting the teaching Chandraker to combine a transformer based deep learning network into the model; motivated by the common goal to obtain optimization of solution building, based on constraints of a given issue (Chandraker, paragraph [0025]).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wasserkrug in view of Chandraker.
Regarding Claim 7:
Wasserkrug discloses, “The computer-implemented method of claim 1, wherein the sequence generation module is a []” (In paragraph [0025], “The algorithm for learning the formal objective model may be any suitable machine learning algorithm, including linear regression algorithms, Support Vector Machines (SVMs), deep neural networks, and others”.).
Chandraker discloses, “transformer based deep learning network” (In paragraph [0007], “A system for training a deep learning network is presented. The system includes a memory and a processor in communication with the memory, wherein the processor is configured to receive a first image and a second image, mine exemplar thin-plate spline (TPS) to determine transformations for generating point correspondences between the first and second images, use artificial point correspondences to train the deep neural network, learn and use the TPS transformation output through a spatial transformer, and apply heuristics for selecting an acceptable set of images to match for accurate reconstruction”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Wasserkrug by adopting the teaching Chandraker to combine a transformer based deep learning network into the model; motivated by the common goal to obtain optimization of solution building, based on constraints of a given issue (Chandraker, paragraph [0025]).
Regarding Claim 14:
Wasserkrug discloses, “The computer system of claim 8, wherein the sequence generation module is a []” (In paragraph [0025], “The algorithm for learning the formal objective model may be any suitable machine learning algorithm, including linear regression algorithms, Support Vector Machines (SVMs), deep neural networks, and others”.).
Chandraker discloses, “transformer based deep learning network” (In paragraph [0007], “A system for training a deep learning network is presented. The system includes a memory and a processor in communication with the memory, wherein the processor is configured to receive a first image and a second image, mine exemplar thin-plate spline (TPS) to determine transformations for generating point correspondences between the first and second images, use artificial point correspondences to train the deep neural network, learn and use the TPS transformation output through a spatial transformer, and apply heuristics for selecting an acceptable set of images to match for accurate reconstruction”.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Wasserkrug by adopting the teaching Chandraker to combine a transformer based deep learning network into the model; motivated by the common goal to obtain optimization of solution building, based on constraints of a given issue (Chandraker, paragraph [0025]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beza D Nigatu whose telephone number is (571)272-9643. The examiner can normally be reached Monday - Friday, 7:30am-3:30pm .
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The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The BRI in the “Examiner’s Note #1” cites the definition of a “loss function” using the source by Niklas Lang, “What is a loss function?” (https://databasecamp.de/en/ml/loss-function). The source was publicly available prior to the effective filing date of the examined case.
/S. Sough/SPE, Art Unit 2192
1 Lang, “What is a loss function?” (https://databasecamp.de/en/ml/loss-function). See conclusion for further details.
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