Detailed Action
Status of Claims
Claims 1 – 9 were previously pending and subject to a final office action mailed 05/28/2026. Claims 1 – 2, 4 – 5, & 7 – 8 were amended in a reply filed 09/14/2026. Claims 1 – 9 are currently pending and subject to the non-final office action below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed after final rejection on 09/14/2026 has been entered.
Response to Arguments
The previous rejections under 35 USC 112(b) have been overcome by the amended claims filed 09/14/2026.
Applicant’s arguments filed 09/14/2026 with respect to the previous rejection of the claims under 35 USC 101 have been considered but are not persuasive.
Applicant initially argues, on pg. 16, that the claims are not directed to a judicial exception.
Examiner respectfully disagrees because the claims recite limitations which are processes that, under the broadest reasonable interpretation, cover performance of the limitations in a business relation or commercial interaction. That is, the functions in the context of the claims encompass optimizing delivery vehicle routing. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a commercial interaction, or while managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas e.g., “commercial or legal interactions (including marketing or sales activities or behaviors; business relations, and following rules or instructions).” Additionally, the functionality performing calculations and implementing mathematical models, as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in a mathematical calculation (including mathematical relationships, formulas, or equations). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. See the below 101 rejeciton for an in-depth analysis of which specific limitations recite a judicial exception. Accordingly, the claims recite abstract ideas that falls within both the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings of abstract ideas. Examiner additionally submits that the functionality of converting a message into a human-readable format - even if claimed - would not amount to an improvement in the way a computing device functions, and would be a part of the recited judicial exception.
Applicant next argues, on pp. 16 – 17, that “the claimed subject matter integrates a judicial exception into a practical application” because “the customized K-means clustering is not generic clustering, but a specialized data partitioning mechanism that constrains problem size, ensures even distribution of nodes, reduces dimensionality of optimization model prior to solving.”
Examiner respectfully disagrees, and submits that the customized K-means clustering is a part of the recited judicial exception itself. Furthermore, the purported advantages are directed to advantages to the solving of a mathematical problem rather than to an improvement to computer functionality or any other technology.
Applicant next argues, on pp. 18 – 19, that the claimed judicial exception is integrated into a practical application because “The amended claim limitation recites a specific sequence of rule-based steps for assigning vehicles to clusters, including sorting vehicles by capacity and type, sorting locations by demand, iteratively comparing capacity-demand differences, assigning vehicles based on minimum difference, and terminating assignment based on satisfaction of capacity constraints. These steps impose concrete limitations on how the optimization is performed and define a particular way of structuring and executing the computation.”
Examiner respectfully disagrees, because the limitations directed to the optimization of vehicle assignment are a part of the recited judicial exception, and therefore are not additional elements that would provide integration into a practical application. There is no evidence that the way the optimization is performed amounts to an improvement to computer functionality or any other technology.
Applicant next argues, on pp. 19 – 20, that “the claimed subject matter integrates a judicial exception into a practical application” because the claimed “formulation embeds constraint logic directly within the optimization model, improving how the computer performs the computation by reducing infeasible solution space, enhancing convergence, and enabling efficient execution of a combinatorial optimization problem. Accordingly, the claims provide a specific improvement in the functioning of a computer system.”
Examiner respectfully disagrees that modifying a mathematical algorithm by embedding constraint logic into that mathematical algorithm amounts to an improvement to technology. Rather, it is an improvement to the recited judicial exception itself (i.e., an improved mathematical algorithm). Moreover, the Federal Circuit has indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123USPQ2d 1100, 1108-09 (Fed. Cir. 2017). (See MPEP § 2106.04(a)(I))
Applicant next argues, on pp. 20 – 21, that the claimed invention “makes the recited QPUs capable of returning a solution they otherwise could not obtain in reasonable time for a large-scale instance. That is a specific improvement in the functioning of the quantum computer under § 2106.05(a), not an improvement to the mathematics.”
Examiner respectfully disagrees that providing a less complex problem for a computer to solve amounts to an improvement in the way the computer functions. In other words, the computer performs in the same way, merely while performing an easier, less time-consuming task.
Applicant next argues, on pg. 21, that “Reducing, by more than an order of magnitude, the number of variables that must be physically loaded onto and evolved by the QPUs is a direct improvement to how the quantum machine operates it lowers the qubit/variable burden on the hardware and is precisely the type of concrete technical benefit § 2106.05(a) contemplates.”
Examiner respectfully disagrees that the claims provide an improvement in functionality of a computer. In particular, reducing a computational burden while using a computing device to implement an abstract idea does not amount to an improvement in the way a computing device functions. That is, reducing the computational demand of a computer does not change the way it functions. Rather, the computer simply processes less information or performs fewer tasks.
Applicant next argues, on pp. 21 – 22, that “Eliminating a conversion step and guaranteeing per-execution feasibility is an improvement in the operation of the solver on the quantum hardware it [sic] changes what the machine reliably produces and is therefore a technological improvement, not a refinement of an abstract calculation.”
Examiner respectfully submits that improving the output of a mathematical calculation is an improvement to the recited abstract idea itself rather than an improvement to computer functionality or any other technology.
Applicant next argues, on pg. 22, that the quantum elements cannot be classified as “apply it” or “generally linking.”
Examiner respectfully disagrees. That a mathematical calculation is formulated to be performed on a generically recited quantum computing device still renders the quantum elements as both “apply it” and “generally linking” elements. In a hypothetical example, in the future all computing devices may be quantum computers. Algorithms implemented on these computing elements would not change the generically recited quantum computing devices to be anything other than both “apply it” and “generally linking” elements.
Applicant next argues, on pp. 22 – 23, that “the claimed subject achieves significantly more” because the claims “address the problem of high volume of commodities exceeding vehicle capacity at a given pickup location and need of separating incompatible commodities present at same pickup location across vehicles.”
Examiner respectfully notes that the problem of “the problem of high volume of commodities exceeding vehicle capacity at a given pickup location and need of separating incompatible commodities present at same pickup location across vehicle” is an economic (i.e., business) problem rather than a technological problem. Thus, the claims are not directed to an improvement to computer functionality or any other technology.
Applicant next argues, on pg. 23, that “The claim does not merely receive logistics data and optimize it; rather, it imposes a bounded-cluster representation that constrains node cardinality, controls cluster count through a ceiling-function rule, and organizes heterogeneous produce into a specific data structure for downstream assignment and optimization. That is the kind of specific limitation that confines the claim to a particular useful application, rather than leaving it at the level of generalized route planning.”
Examiner respectfully submits that constraining node cardinality, controlling cluster count through a ceiling-function rule, and organizing heterogeneous produce into a specific data structure for downstream assignment and optimization amount to mere data manipulation and mathematical calculations. Therefore, this functionality is a part of the recited judicial exception itself.
Applicant next argues, on pp. 23 – 24, that “the amended claim limitation achieves significantly more in terms of vehicle assignment steps” because the claims recite “concrete operational rules that govern how vehicle-cluster assignment is actually performed.”
Examiner respectfully submits that limitations directed to performing vehicle assignment are a part of the recited judicial exception which falls withing the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
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Regarding Applicant’s remarks associated with preemption on pg. 24, Examiner respectfully notes that, while preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1150, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379, 115 USPQ2d 1152, 1158 (Fed. Cir. 2015). As outlined above and below, the claims, after performing the two-part framework of Alice Corp., the claims are not directed to patent eligible subject matter under 35 U.S.C. 101. As such, applicant’s arguments on the basis of preemption are found unpersuasive.
Applicant next argues, on pg. 24, that “When considered with the clustering and vehicle-assignment steps, the δ limitations contribute to a specific technical arrangement for solving the routing problem, which is the type of ordered combination that Step 2B recognizes as potentially amounting to significantly more.”
Examiner respectfully disagrees that a particular mathematical computation amounts to significantly more because this functionality is a part of the recited judicial exception which falls withing the “Mathematical Concepts” grouping of abstract ideas.
Applicant next argues, on pg. 25, that “the claims are not directed to an abstract idea because they recite a specific technological solution that improves computer functionality and therefore satisfy Step 2A, and alternatively Step 2B, of the eligibility analysis” because “The method and system disclosed herein provides both optimal route planning and optimal vehicle capacity utilization, with heterogeneous commodities, along with the separation of incompatible commodities in different vehicles, using a smaller number of variables and constraint equations, resulting in less use of computational resources.”
Examiner respectfully disagrees that the claims integrate the concept into a practical application by providing an improvement in functionality of a computer. In particular, the additional elements, as outlined in the below 101 rejection, are recited at a high-level of generality, such that, when viewed as whole/ordered combination, amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)) as well as merely generally linking the recited judicial exception to a particular technological environment of quantum computing (see MPEP § 2106.05(h)). Examiner additionally submits that “consuming less computation resources” while using a computing device to implement an abstract idea does not amount to an improvement in the way a computing device functions. In other words, reducing the computational demand of a computer does not change the way it functions; rather, the computer simply processes less information or performs fewer tasks, or tasks at a decreased rate. However, the tasks the computer performs is nonetheless being used as a tool to optimize delivery vehicle routing, which is a judicial exception as explained below.
Examiner further notes that using “less computational resources” [SIC] does not amount to an improvement in the functionality of a computing device or any other technology as outlined above. Furthermore, as stated by the Court in Enfish, “the first step in the Alice inquiry in this case asks whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or, instead, on a process that qualifies as an “abstract idea” for which computers are invoked merely as a tool. As noted infra, in Bilski and Alice and virtually all of the computer-related § 101 cases we have issued in light of those Supreme Court decisions, it was clear that the claims were of the latter type—requiring that the analysis proceed to the second step of the Alice inquiry, which asks if nevertheless there is some inventive concept in the application of the abstract idea.” See Alice, 134 S. Ct. at 2355, 2357–59. In this case, however, the plain focus of the claims is not to an improvement to computer functionality itself, but “on economic or other tasks (i.e., using fewer vehicles) for which a computer is used in its ordinary capacity.” The instant claims are directed to a method of organizing human activity as well as a mathematical concept, which invokes generic computer components as a mere tool for implementation, rather to an improvement thereof. Therefore, the claims are not directed to an improvement in the functionality of a computing device or other technology.
Applicant next argues, on pg. 25, that “the amended claims recite rule-based processing, stepwise transformation, and structured algorithm. As in McRO, the claims recite a specific implementation that improves a technological process, rather than merely claiming an abstract result.”
Examiner respectfully disagrees. In particular, the claim considered by the McRO court claimed a method for automatically animating lip synchronization and facial expressions. McRO, 837 F.3d at 1303. The McRO court concluded that the claims did not recite an abstract idea because the computer animation improved the technology through the use of rules, rather than artists, to set morph weights and transitions between phonemes. Id. at 1308. Thus, the claimed invention in McRO allowed for computer performance of animation steps that previously had to be performed by human animators. Id. at 1309. The claims in McRO used “limited rules in a process specifically designed to achieve an improved technological result” over “existing, manual 3-D animation techniques.” Id. at 1316 (emphasis added). Unlike the claims of McRO, the instant claims are not directed to rules for lip sync and facial expression animation or an improvement in computer technology. Instead, the present claims recite abstract ideas which, as identified in the 101 rejection, falls into the “Certain Methods of Organizing Human Activities” and “Mathematical Concepts” groupings – not a technological improvement. Examiner further submits that performing a mathematical calculation to optimize vehicle assignments is entirely unrelated to the fact pattern and subject matter of McRO.
Applicant next argues, on pg. 25, that the claims are similar to “Example 37 in which mathematical operations achieve improved process control, the claimed subject matter uses sorting and matching rules to structure optimization before solving.”
Examiner respectfully disagrees, as Claim 1 of Example 37 recites a specific manner of automatically displaying icons in a GUI based on usage which provides a specific improvement over prior systems, and thus providing an improvement to user interface technology for electronic devices. In particular, Claim 1 of Example 37 includes additional elements that, as a whole, integrates the recited mental process into a practical application by reciting a specific manner of automatically displaying icons comprising the additional elements of receiving, via a GUI, a user selection to organize each icon based on the amount of use of each icon, a processor for performing the determining step, and automatically moving the most used icons to a position on the GUI closest to the start icon of the computer system based on the determined amount of use. Therefore, Claim 1 of Example 37 is directed to an improved user interface, as opposed to a judicial exception. This is in contrast with Applicant’s instant claims, which are directed to the judicial exception of optimizing vehicle assignments, as opposed to an improved user interface. Therefore, the instant claims share no similarities with Example 37.
Applicant next argues, on pg. 25, that the claims are similar to “Example 40 in which specific data processing technique improves system efficiency, the claimed subject matter transforms vehicles to ordered capacity list, locations to demand-ranked nodes, thereby enabling efficient matching.”
Examiner respectfully disagrees, as Example 40 provides a specific improvement over prior systems (improved network monitoring) by limiting collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition (when the collected network delay, packet loss, or jitter is greater than the predefined threshold). In contrast to Example 40, Applicant’s instant claims provide "efficient matching," which is not an improvement to computer functionality itself, but “on economic or other tasks for which a computer is used in its ordinary capacity.”
Applicant next argues, on pg. 25, that the claims are similar to “Example 42 in which optimization integrated with specific application constraints, the claimed subject matter integrates incompatibility constraints, vehicle capacity constraints, cluster-level demand constraints.”
Examiner respectfully disagrees, as the combination of elements of claim 1 of Example 42 integrate the abstract idea into a practical application because the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user, and therefore provide an integration of the judicial exception into a practical application. In contrast to claim 1 of Example 42, Applicant’s instant claims do not recite a combination of additional elements that integrate the abstract idea into a practical application.
Applicant next argues, on pg. 26, that “The additional elements-individually and as an ordered combination-provide a technical improvement, similar to the inventive concept recognized in BASCOM.”
Examiner respectfully disagrees, because the claims are more similar to the claims of Electric Power Group: 'Collecting information, analyzing it, and displaying certain results of the collection and analysis,’ as opposed to BASCOM, which are directed to an entirely different invention of filtering content. Furthermore, in Bascom, the Courts concluded that the claim limitations taken as an “ordered combination” under step two are an inventive concept, sufficient for patent eligibility under 35 USC 101. Because of the ordered combination elements, the claims in BASCOM were considered to improve the functionality of the computer, and thus amounted to significantly more under step two of the Alice analysis. Contrary to BASCOM, the instant claimed invention, when implemented, does not improve the functionality of the computer nor does it improve a technology/technical field. There is no technical evidence/technical support in the Applicant's Specification of technical improvements/solution to a technical problem.
Applicant next argues, on pg. 26, that “the amended claims recite additional elements that amount to significantly more” because “The specification (paragraph [0152]) confirms the technical result: "less use of computational resources" and the ability to "solve a large scale of the problem within a reasonable time".
Examiner respectfully disagrees, and submits that using fewer computational resources while using a computing device to implement an abstract idea does not amount to an improvement in the way a computing device functions. In other words, reducing the computational demand of a computer does not change the way it functions; rather, the computer simply processes less information or performs fewer tasks, or tasks at a decreased rate. However, the tasks the computer performs are nonetheless being used as a tool to optimize delivery vehicle routing, which is a judicial exception as explained below. Moreover, the Federal Circuit has indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123USPQ2d 1100, 1108-09 (Fed. Cir. 2017). (See MPEP § 2106.04(a)(I))
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 – 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1 – 3 are directed to a process (i.e., a method). Claims 4 – 6 are directed to a system (i.e., a machine). Claims 7 – 9 are directed to a product. Therefore, claims 1 – 9 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claims 1, 4, & 7 substantially recite: “a… method for optimal route planning and vehicle capacity utilization with management of incompatible commodities in logistics, the method comprising: splitting… each pickup location among a plurality of pickup locations into a plurality of nodes, wherein each pickup location comprises commodities with a combination of compatible commodities and incompatible commodities to be picked and transported to a collection center, and wherein the splitting is determined based on (i) number of the incompatible commodities at the pickup location, (ii) an amount of commodity comprising a weight amount and a volume amount at each location, and (iii) a median capacity in terms of a weight capacity and a volume capacity of a plurality of vehicles registered for transport to the collection center; clustering… the plurality of nodes into a plurality of clusters based on geological distance among the nodes, wherein the clustering is performed if a number of the plurality of nodes exceed a predefined node count (C), wherein the predefined node count (C) is derived from a maximum number of variables (V max)…, such that a total number of variables of the optimization model, given by M(N2+1), where M is a total number of the plurality of vehicles and N is a total number of the plurality of nodes does not exceed V max, and wherein the predefined node count is computed as C = √ ((V max / M) - 1) and wherein number of clusters is restricted by ratio of the number of the plurality of nodes to the predefined node count, and maximum nodes in a cluster are limited by the predefined node count, wherein the clustering includes a customized K-means clustering and each cluster includes heterogeneous types of farm produce, and a maximum number of nodes allowed in each cluster is C, wherein for even distribution of all nodes, divide a total number of nodes (N1) by the maximum number of nodes allowed in each cluster (C),and a ceiling function to obtain a nearest integer larger than N1/C; assigning… a set of vehicles from among the plurality of vehicles to each cluster using an iterative process until demand of the amount of each of the plurality of clusters is equal to or less than a capacity of one or more vehicles assigned to each cluster, the iterative process is applied on (i) the plurality of vehicles arranged in descending order of the capacity comprising the weight capacity and the volume capacity, (ii) the plurality of nodes of each of the plurality of clusters arranged in descending order of the amount of the one or more commodities at each node among the plurality of nodes a market demand of the one or more commodities available at each of node, and (iii) a difference between the capacity of the vehicle and amount of the one or more commodities at each of the set of nodes in each cluster, wherein steps of assigning the set of vehicles include (1) sorting the vehicles in descending order by capacity and group them on the basis of vehicle types and store a count of each vehicle type, (2) sorting locations in each cluster in decreasing order by commodity demands, (3) iterate over sorted vehicles and ii locations of each cluster, compare difference of vehicle capacity with that location of each cluster, where, i=1 to C, (4) assigning the vehicle to the cluster with minimum difference, (5) if total demand of that cluster <=assign vehicle capacities, then the clustering terminates, else repeat steps 3 to 5, wherein the assigning of the set of vehicles to each cluster is performed prior to creating the optimization model, and wherein restricting the optimization model of each cluster to only the set of vehicles assigned to that cluster, rather than to the plurality of vehicles, reduces a number of variables and constraint equations of the optimization model that is solved; creating… an optimization model defined by an object value (O) for each cluster based on a plurality of input parameters comprising a total number of nodes, a total number of the set of vehicles assigned to a cluster, distance among the set of nodes within the cluster, penalty value for clubbing a pair of commodities in a single vehicle, the weight amount demand of a node and the volume amount demand of a node for the commodity to be picked up from the node, the weight capacity and the volume capacity of the vehicle, and total time steps allotted to complete the pickup of the plurality of commodities, wherein the object value is defined by summation of a plurality of decision variables (a, 3, y, S, il, Q), wherein S represents a penalty term updated by the penalty value based on whether the compatible commodities or the incompatible commodities are paired in a single vehicle, wherein the penalty value is to '1' for compatible commodities that are allowed for clubbing and set to 100 times the largest input parameter, wherein…, to minimize the object value maintains value of S equal to '0' enabling only compatible commodities to be clubbed in the single vehicle; upon creation of the optimization model for each cluster… finding an optimal route with optimal capacity utilization for each vehicle among the assigned set of vehicles within each cluster by solving the optimization model under a plurality of constraints to minimize the object value… wherein the optimization model, having the reduced number of variables bounded by the predefined node count, is refined before being passed… without a Quadratic Unconstrained Binary Optimization (QUBO) conversion, such that a feasible solution is obtained in each execution… while the total number of variables of the optimization model… remains within the maximum number of variables V max, wherein the optimal route specifies number of nodes to be visited and associated time steps for each vehicle for logistics planning… wherein the optimal route is identified with optimal capacity utilization for each vehicle among the assigned set of vehicles within each cluster by solving the optimization model under a plurality of constraints…; and communicating an output of optimization variables to a farmer and a driver of the vehicle including location visited by the vehicle, type of commodity, and commodity amount loaded in each vehicle, and a total distance covered by the vehicle” (exemplary claim 1).
The limitations stated above are processes that, under the broadest reasonable interpretation, cover performance of the limitations in a business relation or commercial interaction. That is, the functions in the context of the claims encompass optimizing delivery vehicle routing. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a commercial interaction, or while managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas e.g., “commercial or legal interactions (including marketing or sales activities or behaviors; business relations, and following rules or instructions).”
Additionally, the functionality of “wherein the predefined node count (C) is derived from a maximum number of variables (V max)…, such that a total number of variables of the optimization model, given by M(N2+1), where M is a total number of the plurality of vehicles and N is a total number of the plurality of nodes does not exceed V max, and wherein the predefined node count is computed as C = √ ((V max / M) - 1) and wherein number of clusters is restricted by ratio of the number of the plurality of nodes to the predefined node count, and maximum nodes in a cluster are limited by the predefined node count, wherein the clustering includes a customized K-means clustering and each cluster includes heterogeneous types of farm produce, and a maximum number of nodes allowed in each cluster is C, wherein for even distribution of all nodes, divide a total number of nodes (N1) by the maximum number of nodes allowed in each cluster (C),and a ceiling function to obtain a nearest integer larger than N1/C; {…} creating… an optimization model defined by an object value (O) for each cluster based on a plurality of input parameters comprising a total number of nodes, a total number of the set of vehicles assigned to a cluster, distance among the set of nodes within the cluster, penalty value for clubbing a pair of commodities in a single vehicle, the weight amount demand of a node and the volume amount demand of a node for the commodity to be picked up from the node, the weight capacity and the volume capacity of the vehicle, and total time steps allotted to complete the pickup of the plurality of commodities, wherein the object value is defined by summation of a plurality of decision variables (a, 3, y, S, il, Q), wherein S represents a penalty term updated by the penalty value based on whether the compatible commodities or the incompatible commodities are paired in a single vehicle, wherein the penalty value is to '1' for compatible commodities that are allowed for clubbing and set to 100 times the largest input parameter, wherein…, to minimize the object value maintains value of S equal to '0' enabling only compatible commodities to be clubbed in the single vehicle; upon creation of the optimization model for each cluster… finding an optimal route with optimal capacity utilization for each vehicle among the assigned set of vehicles within each cluster by solving the optimization model under a plurality of constraints to minimize the object value… wherein the optimization model, having the reduced number of variables bounded by the predefined node count, is refined before being passed… without a Quadratic Unconstrained Binary Optimization (QUBO) conversion, such that a feasible solution is obtained in each execution … while the total number of variables of the optimization model… remains within the maximum number of variables V max, wherein the optimal route specifies number of nodes to be visited and associated time steps for each vehicle for logistics planning… wherein the optimal route is identified with optimal capacity utilization for each vehicle among the assigned set of vehicles within each cluster by solving the optimization model under a plurality of constraints,” as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in a mathematical calculation (including mathematical relationships, formulas, or equations). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claims recite abstract ideas that falls within both the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings of abstract ideas.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Independent claims 1, 4, & 7, as a whole, amount to merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), generally linking the recited judicial exception to a particular technological environment (see MPEP § 2106.05(h)), as well as adding insignificant extra-solution activity to the judicial exception.
Claim 1 recites the additional computer-related elements of “processor,” “by one or more hardware processors of a classical computing system,” “by the one or more hardware processors,” “Quantum Processing Units (QPUs) of a quantum computing system are configured for,” “using a Quantum hybrid solver executed by OPUs of the quantum computing system,” “that a Quantum hybrid solver executed by Quantum Processing Units (QPUs) is capable of solving in a reasonable time,” “by the QPUs,” “Quantum hybrid solver,” “to the Quantum hybrid solver,” “operated on by the QPUs,” “wherein the quantum computing system includes a control system, a signal delivery system, the plurality of QPUs and a quantum memory, wherein the quantum computing system operates using gate-based models for quantum computing, and Qubits are initialized in an initial state, and a quantum logic circuit comprised of a series of quantum logic gates are applied to transform the Qubits and extract measurements representing an output of the quantum computation,” “wherein the plurality of QPUs, and the quantum memory is maintained in a controlled cryogenic environment by a shielding equipment and components in the QPUs operate in a cryogenic temperature regime,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” “wherein the quantum memory performs preprocessing, signal conditioning, or other operations to the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system,” and “incur limited use of computational resources.”
Claim 4 recites the additional computer-related elements of “a classical computing system a memory storing instructions,” “one or more Input/Output (I/O) interfaces,” “one or more hardware processors coupled to the memory via the one or more I/O interfaces,” “a quantum computing system coupled to the classical computing system comprising Quantum Processing Units (QPUs),” “wherein the one or more hardware processors are configured by the instructions,” “wherein the QPUs, executing a Quantum hybrid solver is configured to,” “that a Quantum hybrid solver executed by Quantum Processing Units (QPUs) is capable of solving in a reasonable time,” “by the QPUs,” “Quantum hybrid solver,” “to the Quantum hybrid solver,” “operated on by the QPUs,” “wherein the quantum computing system includes a control system, a signal delivery system, the plurality of QPUs and a quantum memory, wherein the quantum computing system operates using gate-based models for quantum computing, and Qubits are initialized in an initial state, and a quantum logic circuit comprised of a series of quantum logic gates are applied to transform the Qubits and extract measurements representing an output of the quantum computation,” “wherein the plurality of QPUs, and the quantum memory is maintained in a controlled cryogenic environment by a shielding equipment and components in the QPUs operate in a cryogenic temperature regime,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” “wherein the quantum memory performs preprocessing, signal conditioning, or other operations to the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system,” and “incur limited use of computational resources.”
Claim 7 recites the additional computer-related elements of “one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors of a classical computing system,” “a Quantum Processing Units (QPUs) of a quantum computing system are configured for,” “using a Quantum hybrid solver executed by OPUs of the quantum computing system,” “that a Quantum hybrid solver executed by Quantum Processing Units (QPUs) is capable of solving in a reasonable time,” “by the QPUs,” “Quantum hybrid solver,” “to the Quantum hybrid solver,” “operated on by the QPUs,” “wherein the quantum computing system includes a control system, a signal delivery system, the plurality of QPUs and a quantum memory, wherein the quantum computing system operates using gate-based models for quantum computing, and Qubits are initialized in an initial state, and a quantum logic circuit comprised of a series of quantum logic gates are applied to transform the Qubits and extract measurements representing an output of the quantum computation,” “wherein the plurality of QPUs, and the quantum memory is maintained in a controlled cryogenic environment by a shielding equipment and components in the QPUs operate in a cryogenic temperature regime,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” “wherein the quantum memory performs preprocessing, signal conditioning, or other operations to the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system,” and “incur limited use of computational resources.”
The additional elements of “processor,” “by one or more hardware processors of a classical computing system,” “by the one or more hardware processors,” “using a Quantum hybrid solver executed by a Quantum Processing Units (QPUs) of a quantum computing system,” “a classical computing system a memory storing instructions,” “one or more Input/Output (I/O) interfaces,” “one or more hardware processors coupled to the memory via the one or more I/O interfaces,” “a quantum computing system coupled to the classical computing system comprising Quantum Processing Units (QPUs),” “wherein the one or more hardware processors are configured by the instructions,” “one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors of a classical computing system,” “a Quantum Processing Units (QPUs) of a quantum computing system are configured for,” “using a Quantum hybrid solver executed by OPUs of the quantum computing system,” “wherein the quantum computing system includes a control system, a signal delivery system, the plurality of QPUs and a quantum memory, wherein the quantum computing system operates using gate-based models for quantum computing, and Qubits are initialized in an initial state, and a quantum logic circuit comprised of a series of quantum logic gates are applied to transform the Qubits and extract measurements representing an output of the quantum computation,” “that a Quantum hybrid solver executed by Quantum Processing Units (QPUs) is capable of solving in a reasonable time,” “by the QPUs,” “Quantum hybrid solver,” “to the Quantum hybrid solver,” “operated on by the QPUs,” “wherein the plurality of QPUs, and the quantum memory is maintained in a controlled cryogenic environment by a shielding equipment and components in the QPUs operate in a cryogenic temperature regime,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” “wherein the quantum memory performs preprocessing, signal conditioning, or other operations to the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system,” and “incur limited use of computational resources” are recited at a high-level of generality, such that, when viewed as whole/ordered combination, amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)) as well as merely generally linking the recited judicial exception to a particular technological environment of quantum computing (see MPEP § 2106.05(h)).
The additional elements of “being passed to the Quantum hybrid solver,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” and “the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system” are recited at a high-level of generality, and when viewed as whole/ordered combination, amounts to insignificant extra-solution activity, such as mere data gathering (See MPEP 2106.05(g)).
Accordingly, these additional elements, when viewed as a whole/ordered combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), generally linking the recited judicial exception to a particular field or technological environment, as well as adding insignificant extra-solution activity to the judicial exception to the judicial exception (See MPEP2106.05(g)), and do not provide integration of the recited abstract ideas into a practical application. The same analysis applies here in Step 2B, i.e., merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)), and generally linking the recited judicial exception to a particular technological environment or field of use (See MPEP 2106.05(I)(A) & MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
The extrasolution activity of “being passed to the Quantum hybrid solver,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” and “the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system” is similar to functionality found by the courts to be well-understood, routine, and conventional activities (See MPEP § 2106.05(d)(II), noting “Receiving or transmitting data over a network, e.g., using the Internet to gather data”), and thus does not amount to significantly more.
Therefore, the additional elements of “processor,” “by one or more hardware processors of a classical computing system,” “by the one or more hardware processors,” “using a Quantum hybrid solver executed by a Quantum Processing Units (QPUs) of a quantum computing system,” “a classical computing system a memory storing instructions,” “one or more Input/Output (I/O) interfaces,” “one or more hardware processors coupled to the memory via the one or more I/O interfaces,” “a quantum computing system coupled to the classical computing system comprising Quantum Processing Units (QPUs),” “wherein the one or more hardware processors are configured by the instructions,” “one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors of a classical computing system,” “a Quantum Processing Units (QPUs) of a quantum computing system are configured for,” “using a Quantum hybrid solver executed by OPUs of the quantum computing system,” “that a Quantum hybrid solver executed by Quantum Processing Units (QPUs) is capable of solving in a reasonable time,” “by the QPUs,” “Quantum hybrid solver,” “to the Quantum hybrid solver,” “operated on by the QPUs,” “wherein the quantum computing system includes a control system, a signal delivery system, the plurality of QPUs and a quantum memory, wherein the quantum computing system operates using gate-based models for quantum computing, and Qubits are initialized in an initial state, and a quantum logic circuit comprised of a series of quantum logic gates are applied to transform the Qubits and extract measurements representing an output of the quantum computation,” “wherein the plurality of QPUs, and the quantum memory is maintained in a controlled cryogenic environment by a shielding equipment and components in the QPUs operate in a cryogenic temperature regime,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” “wherein the quantum memory performs preprocessing, signal conditioning, or other operations to the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system,” “incur limited use of computational resources,” “being passed to the Quantum hybrid solver,” “wherein the quantum memory receives control signals from the control system and delivers the control signals to the QPUs,” and “the control signals before delivering them to the QPUs, the quantum memory includes connectors or other hardware elements that transfer signals between the QPUs and the control system” fail to integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. There is no indication that the combination of elements, taken both individually and as an ordered combination, improves the functioning of a computer or improves any other technology. Thus, the claims are not patent eligible.
Furthermore, dependent claims 2 – 3, 5 – 6, & 8 – 9 are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims, when considered both individually and as an ordered combination, do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea with generic computer components that conduct generic computer functions within a certain field of use, and thus are ineligible.
Conclusion
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/BRYAN J KIRK/Examiner, Art Unit 3628
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