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 .
Response to Arguments
Claim Objections. Applicant asserts that the claims have been amended to recite ‘input matrices M” and “respective estimated closest correlation matrices X0”, in claims 1 and 11, and replaced “a plurality of” with “the” in claims 7 and 17 to overcome the claim objections. Examiner agrees that this overcomes certain claim objections.
However, other claims previously objected to have not been amended to overcome the claim objections. See remaining claim objections below.
35 USC 112(b).
Applicant asserts that the claims have been amended to overcome “estimated” input correlation coefficients as being a term of degree lacking a standard for ascertaining the degree. Examiner agrees; this rejection is withdrawn.
Applicant further asserts claims 1, 6, 7, 11, 16, and 20 have been amended to overcome the rejections with respect to “estimated input correlation coefficients” replacing with “input coefficients”. Examiner agrees; this rejection is withdrawn.
However, other claims previously rejected under 35 USC 112(b) have not been amended to overcome the respective rejections. See remaining rejections under 35 USC 112(b) below.
35 USC 101.
Applicant asserts the claims as amended recite an improvement in the function of the computing system itself in the training data preprocessing and machine learning model training steps (Remarks p. 11-12).
Examiner respectfully disagrees. Any purported improvement in the functioning of the computing system is a direct result of the mathematical concepts themselves, such as the computing respective estimated closest correlation matrices X0 and the associated mathematical characteristics of this calculation. No improvement of the computing systems itself, which merely is used as a tool to execute the mathematical concepts is claimed. Furthermore, beyond the execution of mathematical concepts used to train a generic machine learning model, no improvement to the machine learning model itself is claimed. “It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology” (MPEP 2106.05(a)(II)).
Claim Objections
Claims 5, and 11-19 are objected to under 37 C.F.R. 1.71(a) which requires “full, clear, concise, and exact terms” as to enable any person skilled in the art or science to which the invention or discovery appertains, or with which it is most nearly connected, to make and use the same. The following should be corrected.
Claim 5 line 2, claim 11 lines 5-6, and claim 15 line 1 recite “the plurality of input matrices M”. This limitation lacks antecedent basis. Antecedent basis is present for “the input matrices M”. Claims 12-19 inherit the same deficiency as claim 11 by reason of dependence.
Claim 7 lines 2-3 recite “the input coefficients”. This limitation lacks antecedent basis. Antecedent basis is present for “the plurality of input coefficients”.
Claim 11 recites “a respective plurality of estimated closest correlation matrices X0” in line 5 and “each estimated closest correlation matrix X0” in line 7 and “the estimated closest correlation matrix X0” in claim 14 line 5. Perhaps Applicant may want to use different reference characters to denote the plurality of estimated closest correlation matrices and each individual estimated closest correlation matrix for better clarity. Claims 12-19 inherit the same deficiency as claim 11 by reason of dependence.
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 1-20 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 9 recites “the plurality of marginal distributions” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. A plurality of marginal distributions is introduced in claim 8, however, claim 9 does not depend on claim 8. For purposes of examination, claim 9 is interpreted to depend on claim 8.
Claim 10 recites “the plurality of marginal distributions” in lines 1-2. There is insufficient antecedent basis for this limitation in the claim. A plurality of marginal distributions is introduced in claim 8, however, claim 10 does not depend on claim 8. For purposes of examination, claim 10 is interpreted to depend on claim 8.
Claim 19 recites “the plurality of marginal distributions” in line 1. There is insufficient antecedent basis for this limitation in the claim. A plurality of marginal distributions is introduced in claim 18, however, claim 19 does not depend on claim 18. For purposes of examination, claim 19 is interpreted to depend on claim 18.
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, 4-11, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1, claims 1, and 4-10 and 20 recite a system and, therefore, is a machine. Claims 11, and 14-19 recite a series of steps and, therefore, is a process.
Under Step 2A prong 1, claim 1 recites
A computing system comprising:
one or more processors configured to:
receive input matrices M, wherein each input matrix M includes a plurality of input coefficients;
compute respective estimated closest correlation matrices X0 for the input matrices M at a semidefinite program solver, wherein each estimated closest correlation matrix X0 is a positive definite matrix;
the one or more processors are configured to estimate each estimated closest correlation matrix X0 to be the positive definite matrix closest to the input matrix M from which that estimated closest correlation matrix X0 is computed, according to a least squares distance measure; and
at the semidefinite program solver, the one or more processors are configured to compute the respective estimated closest correlation matrix X0 for each of the input matrices M at least in part by estimating a candidate solution matrix X that minimizes tr(MX);
generate a training data set including at least the plurality of estimated closest correlation matrices X0;
train a machine learning model using the training data set; and
compute a plurality of runtime output distributions at least in part by executing the trained machine learning model.
The above limitations of computing respective estimated closest correlation matrices X0 for the plurality of input matrices M amounts to processing mathematical relationships/calculations and falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim is directed to recite an abstract idea.
Under step 2A prong 2, the claim recites the following additional elements: a semidefinite program solver; one or more processors configured to: receive a plurality of input matrices M, wherein each input matrix M includes a plurality of estimated input correlation coefficients; generate a training data set including at least the plurality of estimated closest correlation matrices X0; and train a machine learning model using the training data set. However, the additional elements of “one or more processors”, “a semidefinite program solver” and “runtime compute” are recited at a high-level of generality (i.e., as a generic computer component for executing a series of operations; as a generic solver for solving) such that they amount to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “generate a training data set including at least the plurality of estimated closest correlation matrices X0”, “train a machine learning model using the training data set” and “executing the trained machine learning model” are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the result of the abstract idea as training data for a machine learning model without reciting specific details about how the training data are generated and how the machine learning model is trained such that they do not provide any meaningful limits on the claim). The additional elements of “receive input matrices M, wherein each input matrix M includes a plurality of input coefficients” is merely adding insignificant extra-solution activity, i.e. mere data gathering. The additional elements do not, individually or in combination, integrate the exception into a practical application. Accordingly, the claim is not integrated into a practical application.
Under step 2B, claim 1 does not include additional elements that, individually or in combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more processors”, “a semidefinite program solver” and “runtime compute” are recited at a high-level of generality (i.e., as a generic computer component for executing a series of operations; as a generic solver for solving) such that they amount to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “generate a training data set including at least the plurality of estimated closest correlation matrices X0”, “train a machine learning model using the training data set” and “executing the trained machine learning model” are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the result of the abstract idea as training data for a machine learning model without reciting specific details about how the training data are generated and how the machine learning model is trained such that they do not provide any meaningful limits on the claim). The additional elements of “receive input matrices M, wherein each input matrix M includes a plurality of input coefficients” is merely adding insignificant extra-solution activity, i.e. mere data gathering. See MPEP 2106.05(d)(II) which states that the courts have recognized computer functions such as “Receiving or transmitting data over a network” and “Storing and retrieving information in memory” as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. The claim does not recite additional elements that alone or in combination amount to an inventive concept. Accordingly, the claim does not amount to significantly more than the abstract idea.
Under step 2A prong 1, claims 4-10 recite the same abstract idea as claim 1 by reason of dependence. Claim 4 recites further details of the abstract idea of estimating the closest correlation matrix X0 by “compute the estimated closest correlation matrix X0 under a constraint that tr(AiX) = 1 - λ, where Ai is a square matrix in which each element is equal to 0 except a 1 located along a main diagonal of Ai in an ith row.” Claim 6 recites further abstract idea of “compute the input coefficients based at least in part on a copula.” Claim 7 recites further abstract idea of “compute the input coefficients from empirical correlation data” which falls within the “Mathematical Concepts” grouping of abstract ideas. In particular claims 6-7 do not include additional elements that would require further analysis under step 2A prong 2 and step 2B. Accordingly, the claims are directed to recite an abstract idea.
Under step 2A prong 2, claim 4 recites the following additional elements: receive a smallest eigenvalue λ of the estimated closest correlation matrix X0. Claim 5 recites the following additional elements: receive the plurality of input matrices M via user input at a graphical user interface (GUI). Claim 8 recites the following additional elements: wherein the training data set further includes a plurality of marginal distributions. Claim 9 recites the following additional elements: wherein the plurality of marginal distributions are financial risk distributions. Claim 10 recites the following additional elements: wherein the plurality of marginal distributions are energy source availability distributions. However, the additional elements of “graphical user interface (GUI)” in claim 5 is recited at a high-level of generality (i.e., as a generic computer component for receiving an input from a user) such that it amounts to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “wherein the training data set further includes a plurality of marginal distributions” in claim 8; “wherein the plurality of marginal distributions are financial risk distributions” in claim 9; and “wherein the plurality of marginal distributions are energy source availability distributions” in claim 10 are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the training data to a particular type of data including marginal distributions of financial risk distributions and energy source availability distributions). The additional elements of “receive a smallest eigenvalue λ of the estimated closest correlation matrix X0” in claim 4; and “receive the plurality of input matrices M via user input” in claim 5 are merely adding insignificant extra-solution activities, i.e. mere data gathering. The additional elements do not, individually or in combination, integrate the exception into a practical application. Accordingly, the claims are not integrated into a practical application.
Under the step 2B, claims 4-5 and 8-10 do not include additional elements that, individually and in combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “graphical user interface (GUI)” in claim 5 is recited at a high-level of generality (i.e., as a generic computer component for receiving an input from a user) such that it amounts to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “wherein the training data set further includes a plurality of marginal distributions” in claim 8; “wherein the plurality of marginal distributions are financial risk distributions” in claim 9; and “wherein the plurality of marginal distributions are energy source availability distributions” in claim 10 are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the training data to a particular type of data including marginal distributions of financial risk distributions and energy source availability distributions). The additional elements of “receive a smallest eigenvalue λ of the estimated closest correlation matrix X0” in claim 4; and “receive the plurality of input matrices M via user input” in claim 5 are merely adding insignificant extra-solution activities, i.e. mere data gathering. See MPEP 2106.05(d)(II) which states that the courts have recognized computer functions such as “Receiving or transmitting data over a network” and “Storing and retrieving information in memory” as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. The claims do not recite additional elements that alone or in combination amount to an inventive concept. Accordingly, the claims do not amount to significantly more than the abstract idea.
Regarding claims 11, and 14-19, they are directed to a method practiced by the computing system of claims 1, and 4-8 and 9/10 respectively. All steps performed by the method of claims 11, and 14-19 would be practiced by the computing system of claims 1-8 and 9/10. respectively. Claims 1, and 4--8 and 9/10 analysis applies equally to claims 11, and 14-19 respectively.
Under Step 2A prong 1, claim 20 recites
A computing system comprising:
one or more processors configured to:
at a graphical user interface (GUI), receive:
a user input specifying an input matrix M that includes a plurality of input correlation coefficients; and
a smallest eigenvalue λ for an estimated closest correlation matrix X0;
compute the estimated closest correlation matrix X0 for the input matrix M at a semidefinite program solver, wherein the estimated closest correlation matrix X0 is a positive definite matrix and has the smallest eigenvalue λ;
output a graphical representation of the estimated closest correlation matrix X0 for display at the GUI;
generate a training data set including the estimated closest correlation matrix X0;
train a machine learning model using the training data set; and
compute a plurality of runtime distributions at least in part by executing the trained machine learning model.
The above limitations of computing an estimated closest correlation matrix X0 for an input matrix M amounts to processing mathematical relationships/calculations and falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim is directed to recite an abstract idea.
Under step 2A prong 2, the claim recites the following additional elements: a semidefinite program solver; one or more processors configured to: at a graphical user interface (GUI), receive: a user input specifying an input matrix M that includes a plurality of estimated input correlation coefficients; and a smallest eigenvalue λ for an estimated closest correlation matrix X0; and output a graphical representation of the estimated closest correlation matrix X0 for display at the GUI. However, the additional elements of “one or more processors”, “graphical user interface (GUI)”, “a semidefinite program solver”, and “runtime compute” are recited at a high-level of generality (i.e., as a generic computer component for executing a series of operations; as a generic computer input/output component for receiving inputs and displaying the result of the mathematical calculations; and as a generic solver for solving) such that they amount to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “generate a training data set including at least the estimated closest correlation matrices X0”, “train a machine learning model using the training data set” and “executing the trained machine learning model” are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the result of the abstract idea as training data for a machine learning model without reciting specific details about how the training data are generated and how the machine learning model is trained such that they do not provide any meaningful limits on the claim).The additional elements of “receive: a user input specifying an input matrix M that includes a plurality of input coefficients; and a smallest eigenvalue λ for an estimated closest correlation matrix X0” and “output a graphical representation of the estimated closest correlation matrix X0 for display” are merely adding insignificant extra-solution activities, i.e. mere data gathering and data outputting steps. The additional elements do not, individually or in combination, integrate the exception into a practical application. Accordingly, the claim is not integrated into a practical application.
Under step 2B, claim 20 does not include additional elements that, individually or in combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more processors”, “graphical user interface (GUI)”, “a semidefinite program solver”, and “runtime compute” are recited at a high-level of generality (i.e., as a generic computer component for executing a series of operations; as a generic computer input/output component for receiving inputs and displaying the result of the mathematical calculations; and as a generic solver for solving) such that they amount to no more than mere instructions using a generic computer component or merely as tools to implement the abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f)(2) for more information. The additional elements of “generate a training data set including at least the estimated closest correlation matrices X0”, “train a machine learning model using the training data set” and “executing the trained machine learning model” are merely generally linking the use of a judicial exception to a particular technological environment or field of use (i.e., by limiting the result of the abstract idea as training data for a machine learning model without reciting specific details about how the training data are generated and how the machine learning model is trained such that they do not provide any meaningful limits on the claim). The additional elements of “receive: a user input specifying an input matrix M that includes a plurality of input coefficients; and a smallest eigenvalue λ for an estimated closest correlation matrix X0” and “output a graphical representation of the estimated closest correlation matrix X0 for display” are merely adding insignificant extra-solution activities, i.e. mere data gathering and data outputting steps. See MPEP 2106.05(d)(II) which states that the courts have recognized computer functions such as “Receiving or transmitting data over a network” and “Storing and retrieving information in memory” as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. The claim does not recite additional elements that alone or in combination amount to an inventive concept. Accordingly, the claim does not amount to significantly more than the abstract idea.
Allowable Subject Matter
For the reasons set forth in the office action dated 11/21/25, claims 1, 4-11, and 14-20 would be allowable if rewritten to overcome the 35 U.S.C. 112(b), and 35 U.S.C. 101 rejections, and claim objections discussed above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY E LAROCQUE whose telephone number is (469)295-9289. The examiner can normally be reached 10:00am - 1200pm, 2:00pm - 8pm ET M-F.
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/EMILY E LAROCQUE/Primary Examiner, Art Unit 2182
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