DETAILED ACTION
This Non-Final action is responsive to the application filed 12/31/2024 and IDS filed 12/31/2024 & 3/21/2025.
In the application Claims 1-6 are pending. Claim 1 is the independent claim.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/31/2024 & 3/21/2025 has been entered, and considered by the examiner.
Priority
5. Acknowledgement is made to applicant’s claim for foreign priority to 10-2024-0066318 (KR), filed 5/22/2024 & 10-2024-0201583 (KR), filed 12/31/2024.
Drawings
6. The Drawings filed on 3/20/2025 have been approved.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to “An electronic computing device implemented method…” (process). Therefore, the claims are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]). In addition, claims 5-6 recite similar subject matter has in claim 1 and are rejected under the same rationale.
Claim 1. An electronic computing device implemented method for calculating an optimal route among a plurality of routes including nodes, comprising:
a first step of selecting first and second optimization targets to be applied to optimal route calculation according to Large Neighborhood Search (LNS), the first and second optimization target being different from each other [mathematical concept & mental process];
a second step of distributing the optimal route calculation of the first optimization target to a first thread module, and the optimal route calculation of the second optimization target to a second thread module [MPEP 2106.05(f) Mere Instructions to Apply an Exception];
a third step of calculating a cost matrix between nodes [mathematical concept] or
receiving the cost matrix from a database when calculating an optimal route according to the LNS by applying the first and second optimization targets [MPEP 2106.05(g) Insignificant Extra-Solution Activity, data gathering, pre-solution activity];
a fourth step of randomly arranging nodes of a starting point, an arrival point and stopovers, and executing the optimal route calculation according to the LNS based on the cost matrix between the arranged nodes [mathematical concept & mental process];
a fifth step of selecting an optimal route that satisfies a predetermined condition among different optimal routes according to the first optimization target and the second optimization target calculated in the fourth step, as a final optimal route [mathematical concept & mental process];
wherein the fourth step is a step of executing the optimal route calculation according to the LNS, by updating the cost matrix between two nodes connected by destruction and repair of the LNS into a cost matrix corresponding to the optimization target [mathematical concept].
The Examiner submits that the foregoing bolded limitation(s) above: constitute “mathematical concept” and a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind.
The claim describes selection of an optimization criteria which is a mental process that involves conceptual decision-making. LNS is a mathematical algorithm. Performing a calculation of a cost matrix between nodes is a mathematical concept. In addition, random arrangement with route calculation using LNS are algorithmic optimization (mathematical operations) that can be performed via pen/paper. Updating matrices via LNS destroy-repair fall under mathematical manipulation of data structures. Performing comparison and selection of optimal solutions is a mental process that involves performing mathematical comparison.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations”, while the bolded portions continue to represent the “abstract idea”.):
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “receiving the cost matrix from a database…”. The Examiner submits that these limitations are insignificant extra-solution activities that amount to pre-solution activity and data gathering via data collection of a cost matrix.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative claims does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “distributing the optimal route calculation”, amounts to nothing more than mere instructions to apply the exception using a generic computer component. The claim describes generic instructions to apply the abstract idea of optimization using computing components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) in addition to -Collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group), Collecting data, recognizing certain data within the collected data set and storing the recognized data in memory (Content Extraction).
Dependent claims 2-4, -do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claim are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. The claims describe optimization targets, coast matrix, calculation of costs between nodes which are mathematical calculations that are used to optimize routes. In addition, data inputs regarding fuel consumption, emissions etc. used in optimization calculations describe additional data used in the mathematical optimization step. Furthermore, describing plurality of thread modules for performing LNS operations is mathematical optimization executed in parallel computing threads. Thus, these claims therefore fall under mathematical concepts. Therefore, the claims are not patent eligible under the same rationale as provided for in the rejection of the Independent claims. Therefore, the claims are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
9. Claims 1 and 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Grigorios D. Konstantakopoulos herein Grigorios (NPL- A multi-objective Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Time Windows, September 26, 2020, MDPI, pgs. 1-17) in view of Ropke Stefan herein Ropke (NPL-PALNS- A software framework for parallel large neighborhood search, 2009, Technical University of Denmark, pgs. 1-11 (pdf)).
Regarding Independent claim 1, Grigorios discloses An electronic computing device implemented method for calculating an optimal route among a plurality of routes including nodes, comprising:
a first step of selecting first and second optimization targets to be applied to optimal route calculation according to Large Neighborhood Search (LNS), the first and second optimization target being different from each other (see pg. 1, abstract & pgs. 3-5 & 9, discloses python implementation for the calculation of optimal routes between nodes in a vehicle routing problem with time window (VRPTW). Further disclosing selection of two distinct LNS optimization targets via number of vehicles (NV) and total distance traveled (TD) minimization objectives);
a third step of calculating a cost matrix between nodes or receiving the cost matrix from a database when calculating an optimal route according to the LNS by applying the first and second optimization targets (see pg. 3, section 2, discloses every arc (i,j) which is a path from node i to j and characterized by a distance as d_ij & t_ij stands for the travel time from node i to node j);
a fourth step of randomly arranging nodes of a starting point, an arrival point and stopovers, and executing the optimal route calculation according to the LNS based on the cost matrix between the arranged nodes (see pgs.3 & 6 section 3.3.1, discloses random arrangement of nodes with destroy/repair via LNS);
a fifth step of selecting an optimal route that satisfies a predetermined condition among different optimal routes according to the first optimization target and the second optimization target calculated in the fourth step, as a final optimal route (see pgs. 8-9 section 3.3.3, discloses Pareto optimal solutions for selecting an optimal route that satisfies conditions among different routes based on the optimization targets);
wherein the fourth step is a step of executing the optimal route calculation according to the LNS, by updating the cost matrix between two nodes connected by destruction and repair of the LNS into a cost matrix corresponding to the optimization target (see pgs. 7-8, section 3.3.2, discloses a specific number of customers that are removed from the existing solution resulting in an empty list of size being created and filled with customers who are randomly selected and having a random order for inserting customers is produced for increasing the search space of the algorithm). Grigorios discloses a multi-objective LNS algorithm for route optimization having two distinct objectives. He fails to teach any parallel thread architecture including any updating of a cost matrix between nodes during destroy/repair cycles of the LNS.
Ropke discloses:
a second step of distributing the optimal route calculation of the first optimization target to a first thread module, and the optimal route calculation of the second optimization target to a second thread module (see pg. 5 section 4, wherein the proposed parallelization one current solution and global best solution is shared among worker threads wherein each thread obtains a copy of the current solution and performs destroy repair operations on its local copy. Further performing in parallel select destroy and repair methods d and r); Ropke further discloses in sections 2 pg. 2 and section 6 pg. 7, a traveling cost c_ij is given between each pair of vertices (i,j). Further he states that for each arc (i,j) in the graph on which the CVRP is defined, a number f_ij is stored (cost matrix), which represents the cost of the best solution that used arc (i,j) observed during the search. He further describes destroy method that selects k requests at random and removes them from the solution with free requests ordered in random and inserted one by one based on ordering in section 5. Both Ropke and Grigorios share identical LNS destroy/repair frameworks and operate in the same vehicle routing domain. At the time of the invention, it would have been obvious for one of ordinary skill in the art before the effective filing date of the application to have implemented parallelization of destroy and repair operations in LNS. Grigorios acknowledges that its MOLNS algorithm is constrained by computational time and solution quality degrading for large instances, Ropke directly addresses this identical issue via thread parallelization thus improving both computational time and solution quality.
Regarding Dependent claims 5-6, recite similar subject matter has in claim 1 and are rejected under the same rationale.
It is noted that any citation [[s]] to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. [[See, MPEP 2123]]
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANGLESH M PATEL whose telephone number is (571)272-5937. The examiner can normally be reached on M-F from 11 am to 7 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop, can be reached at telephone number 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manglesh M Patel/
Primary Examiner, Art Unit 3665
3/7/2026