DETAILED ACTION
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 responsive to pending claims 1-20 filed 6/6/2023.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application GR20230100069 filed in HELLENIC REPUBLIC on 1/30/2023.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The 35 U.S.C. 101 subject matter eligibility analysis first asks whether the claim is directed to one of the four statutory categories (Step 1). It next asks whether the claim is directed to an abstract idea (Step 2A), via Prong 1, whether an abstract idea (e.g., mathematical concept, mental process, certain methods of organizing human activity) is recited, and Prong 2, whether it is integrated into a practical application. It finally asks whether the claim as a whole includes additional elements that amount to significantly more than the judicial exception (Step 2B). See MPEP 2106.
STEP 1: The claims falls within one of the four statutory categories:
As the claims recite methods, hardware apparatuses, and computer media that are not signals per se, the claims fall into statutory categories.
STEP 2A PRONG 1: The claims recite a judicial exception:
The claims recite a technique of reconciling hierarchical time series forecasts by holding certain nodes fixed relative to a base estimate and reconciling the remaining nodes. As such, it is directed to a mathematical process, hat of data reconciliation or constrained optimization, and a mental process, that of generating base forecast data and merging solution data. In particular:
For claim 1: A method of generating forecasts from time series data, the method comprising:
receiving a set of time series data organized according to a data structure having a plurality of nodes (This defines the hierarchical data structure for reconciliation / constrained optimization);
generating a plurality of base forecasts, including a base forecast for each node (The generating a plurality of base forecasts for the optimization problem may be performed in the mind);
selecting a sub-set of the plurality of nodes as fixed nodes (data is selected for the mathematical optimization technique);
performing a reconciliation process to generate reconciled forecasts, wherein the reconciliation process includes reconciling only the base forecasts of non-fixed nodes (constrained optimization is performed on the data forecasts to reconcile the data, the constraint condition including not altering certain nodes);
merging the base forecasts of the fixed nodes and the reconciled forecasts of the non-fixed nodes to generate an overall forecast (the is combining the fixed and reconciled nodes into an overall solution, this merging or data rearranging being a mental process).
For claim 2: The method of claim 1, wherein the time series data is organized as at least one of a hierarchical time series and a grouped time series (This merely limits the type of mathematical structures for input).
For claim 3: The method of claim 1, wherein selecting the sub-set includes at least one of:
randomly selecting nodes from the nodes of the data structure, excluding bottom layer nodes and leaf nodes (The random selection of nodes to hold fixed may be performed mentally);
selecting nodes based on knowledge relating to stability of a domain of each node (Selecting based on judgments of stability is a mental process); and
selecting nodes based on a statistical analysis of time series data in each node (selecting based on observations of statistical characteristics is a mental process).
For claim 4: The method of claim 3, wherein selecting nodes based on the statistical analysis includes selecting the nodes based on a statistical stability of each node (making determinations on the stability of a node is a mental process akin to observation, judgement).
For claim 5: The method of claim 1, wherein the reconciliation process is based on a constrained optimization problem based on a Lagrange function having a Lagrange multiplier, and includes selecting a computationally stable formulation or a computationally efficient formulation for solving the optimization problem (Performing constrained optimization reconciliation on a Lagrange form is a mathematical concept).
For claim 6: The method of claim 1, wherein the reconciliation process includes splitting the nodes into a plurality of sets of nodes, and performing reconciliation separately for each set of nodes (Dividing a group of noes for separate reconciliation processes a mental process).
For claim 7: The method of claim 5, wherein the computationally stable formulation includes solving the optimization problem by solving for a vector of forecasts of the non-fixed nodes concatenated with the Lagrange multiplier (Solving a constrained optimization problem via concatenation with the Lagrangian is a mathematical concept).
For claim 8: The method of claim 5, wherein the computationally efficient formulation includes solving the optimization problem by solving for the Lagrange multiplier, and subsequently solving for a vector of forecasts of the non-fixed node forecasts (Solving a constrained optimization problem via sequential determination of Lagrangian multiplier followed by vector forecasts is a mathematical process).
The remaining limitations recite analogous apparatuses and computer media and hence are likewise rejected.
STEP 2A PRONG 2: The claims do not integrate the exception into a practical application:
The additional elements are:
For claim 9: An apparatus for generating forecasts from time series data, comprising one or more computer processors that comprise: a processing unit including a processor. However, this is mere instructions to implement an abstract idea on a computer and hence does not constitute an integration into a practical application.
For claim 17: A computer program product comprising a storage medium readable by one or more processing circuits, the storage medium storing instructions executable by the one or more processing circuits to perform a method. However, this is mere instructions to implement an abstract idea on a computer and hence does not constitute an integration into a practical application.
STEP 2B: The claim as a whole do not include additional elements that amount to significantly more than the abstract idea:
For claim 9: An apparatus for generating forecasts from time series data, comprising one or more computer processors that comprise: a processing unit including a processor. The use of a computer with processor and memory medium to implement optimization techniques is well-understood, routine and conventional (WURC) and hence does not constitute significantly more (2b).
For claim 17: A computer program product comprising a storage medium readable by one or more processing circuits, the storage medium storing instructions executable by the one or more processing circuits to perform a method. The use of a computer with processor and memory medium to implement optimization techniques is well-understood, routine and conventional (WURC) and hence does not constitute significantly more (2b).
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.
Claim(s) 1-6, 8-14, 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang ("Optimal reconciliation with immutable forecasts", published 11/24/2022), with Wickramasuriya ("Optimal non-negative forecast reconciliation", published 2020) incorporate by reference.
For claim 1, Zhang discloses: a method of generating forecasts from time series data, the method (§3: “Forecast reconciliation with immutability constraints” gives an overview of the technique of generating reconciled forecasts given time series data) comprising:
receiving a set of time series data organized according to a data structure having a plurality of nodes (§3 ¶1: hierarchical time series data is received having a plurality of immutable and mutable nodes, see also fig.1 showing possible hierarchies);
generating a plurality of base forecasts, including a base forecast for each node (ibid: y-hat is generated as base forecasts);
selecting a sub-set of the plurality of nodes as fixed nodes (ibid: u_t is selected as immutable);
performing a reconciliation process to generate reconciled forecasts, wherein the reconciliation process includes reconciling only the base forecasts of non-fixed nodes (§3 eq.3 gives general form of a reconciled forecast, with the immutable nodes u held constant);
merging the base forecasts of the fixed nodes and the reconciled forecasts of the non-fixed nodes to generate an overall forecast (ibid: y-tilde is generated via the reconciliation process as the merging of the fixed and non-fixed nodes, see eq.5).
For claim 2, Zhang discloses the method of claim 1, as described above. Zhang further discloses: wherein the time series data is organized as at least one of a hierarchical time series and a grouped time series (p.651 col.1 last ¶ contemplates application to grouped time series, see also §6 for application to grouped data).
For claim 3, Zhang discloses the method of claim 1, as described above. Zhang further discloses: wherein selecting the sub-set includes at least one of:
randomly selecting nodes from the nodes of the data structure, excluding bottom layer nodes and leaf nodes;
selecting nodes based on knowledge relating to stability of a domain of each node (p.651 col.1 ¶3: sufficiently long history reflects stability of predictions); and
selecting nodes based on a statistical analysis of time series data in each node (§5 ¶2: intermittent time series or promotional peaked series constitutes a statistical feature analyzed from the data).
For claim 4, Zhang discloses the method of claim 3, as described above. Zhang further discloses: wherein selecting nodes based on the statistical analysis includes selecting the nodes based on a statistical stability of each node (ibid: intermittency and promotional peaks constitutes a statistical stability measure).
For claim 5, Zhang discloses the method of claim 3, as described above. Zhang further discloses, in p.653 c.1 ¶2, using various algorithms disclosed by Wickramasuriya 2020 such as BPV, PCG, scaled gradient projection, hence, Zhang discloses: wherein the reconciliation process is based on a constrained optimization problem based on a Lagrange function having a Lagrange multiplier (Wickramasuriya §2.3 goes discloses a Lagrange formulation of the constrained optimization problem for non-negative reconciliation, hence, the reconciliation being based on the Lagrangian function), and includes selecting a computationally stable formulation or a computationally efficient formulation for solving the optimization problem (Wickramasuriya §3.1-3 goes over several numerical algorithms for solving said problem, with BPV (§3.1) being computationally unstable, i.e., causing a cycle, see p.1171 col.1 ¶2, and the remaining 3.2-3 not having the issue and using a computationally efficient algorithms such as by finding a piecewise-linear path to the solution, see §3.2 ¶1 ).
For claim 6, Zhang discloses the method of claim 1, as described above. Zhang further discloses: wherein the reconciliation process includes splitting the nodes into a plurality of sets of nodes, and performing reconciliation separately for each set of nodes (§3 ¶1 discloses separating nodes into an immutable, mutable set, and non-basis set, the reconciliation being performed separately on each set such that the immutable set is unchanged, the mutable set is optimized, and the basis set is adjusted accordingly ).
For claim 8, Zhang discloses the method of claim 5, as described above. Zhang further discloses: wherein the computationally efficient formulation includes solving the optimization problem by solving for the Lagrange multiplier, and subsequently solving for a vector of forecasts of the non-fixed node forecasts (Wickramasuriya p.1173 alg.3.3 (scaled gradient projection) algorithm includes iteratively solving for the Lagrangian multiplier lambda at step 8-10 and subsequently solving for base b corresponding to iterative base reconciliation forecasts corresponding to non-fixed nodes).
Claims 9-14, 16-19 recite apparatuses and computer media corresponding to the above methods and are hence likewise rejected.
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.
Claim(s) 7, 15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang ("Optimal reconciliation with immutable forecasts", published 11/24/2022), with Wickramasuriya ("Optimal non-negative forecast reconciliation", published 2020) incorporate by reference, in view of Won ("Newton's Method for Constrained Optimization", published 12/8/2021).
For claim 7, Zhang discloses the method of claim 5, as described above. Zhang does not disclose: wherein the computationally stable formulation includes solving the optimization problem by solving for a vector of forecasts of the non-fixed nodes concatenated with the Lagrange multiplier.
Won discloses: wherein the computationally stable formulation includes solving the optimization problem by solving for a vector concatenated with the Lagrange multiplier (p.4-5 “Feasible start Newton”, “Infeasible Start Newton” shows solving for the concatenation of the vector x concatenated with the Lagrangian estimator v / w via sequential updates, hence, combination with Zhang yielding a computationally stable (e.g., no cycling issues) formulation for solving the constrained hierarchical distillation problem).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zhang by incorporating the numerical method of Won. Both concern the art of numerical solving of constrained optimization problems, and the incorporation would have been, according to Won, to use a fundamental and theoretically grounded numerical convergence method to solve constrained optimization problems (p.2: “Course Objectives”).
Claim 15 recites an apparatus corresponding to the above method and is hence likewise rejected.
For claim 20, Zhang discloses the method of claim 19, as described above. Zhang further discloses: wherein the computationally efficient formulation includes solving the optimization problem by solving for the Lagrange multiplier, and subsequently solving for a vector of forecasts of the non-fixed node forecasts (Wickramasuriya p.1173 alg.3.3 (scaled gradient projection) algorithm includes iteratively solving for the Lagrangian multiplier lambda at step 8-10 and subsequently solving for base b corresponding to iterative base reconciliation forecasts corresponding to non-fixed nodes).
Zhang does not disclose: wherein the computationally stable formulation includes solving the optimization problem by solving for a vector of forecasts of the non-fixed nodes concatenated with the Lagrange multiplier.
Won discloses: wherein the computationally stable formulation includes solving the optimization problem by solving for a vector concatenated with the Lagrange multiplier (p.4-5 “Feasible start Newton”, “Infeasible Start Newton” shows solving for the concatenation of the vector x concatenated with the Lagrangian estimator v / w via sequential updates, hence, combination with Zhang yielding a computationally stable (e.g, no cycling issues) formulation for solving the constrained hierarchical distillation problem).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Zhang by incorporating the numerical method of Won. Both concern the art of numerical solving of constrained optimization problems, and the incorporation would have been, according to Won, to use a fundamental and theoretically grounded numerical convergence method to solve constrained optimization problems (p.2: “Course Objectives”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Trovero (US 20120089609 A1) discloses hierarchical time series forecasting with constraints, see fig.6.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143