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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Under the Alice/Mayo test, as set forth in Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014) and as interpreted in the USPTO Subject Matter Eligibility Guidance, the claims are analyzed as follows.
Step 1:
Claims 1-10 are directed toward method (process) and claims 11-16 are directed towards system (apparatus) and claims 17-20 are directed towards product. Thus, all claims fall within one of the four statutory categories as required by Step 1.
Step 2A – Prong One (Identification of the Judicial Exception):
Independent claims 1, 11, and 17 recite limitations that fall within the certain methods of organizing human activity and mathematical concepts groupings of abstract ideas.
Specifically, the claims recite steps including: accessing cargo block data , assigning cargo blocks to containers based on constraints, generating optimization solutions, determining packing arrangements.
These limitations collectively describe logistics planning and resource allocation, which is a fundamental economic practice and a method of organizing human activity.
Additionally, the claims recite: optimization problems, objective functions, constraint-based calculations, knapsack algorithms, linear programming.
Such features correspond to mathematical concepts, including mathematical relationships, formulas, and optimization calculations.
Courts have consistently held that mathematical optimization and resource allocation techniques constitute abstract ideas (see Alice Corp. v. CLS Bank, 573 U.S. 208; SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018)).
Therefore, claims 1–20 recite abstract ideas.
Step 2A – Prong Two (Integration into a Practical Application):
The claims do not integrate the abstract idea into a practical application.
The additional elements recited in the claims include: a computer system, processors, storage media, software instructions, invoking a quantum annealer, executing optimization algorithms.
These elements merely implement the abstract idea on generic computer components performing their well-understood, routine, and conventional functions, such as: receiving data, performing calculations, storing information, generating output.
The claims do not improve the functioning of a computer itself, or effect an improvement to another technology or technical field.
Instead, the claims merely use generic computing devices as tools to perform an abstract logistical planning process.
Therefore, the claims do not integrate the abstract idea into a practical application.
Step 2B – Inventive Concept
Because the claims recite a judicial exception and do not integrate it into a practical application, it must be determined whether the claims include an inventive concept.
The additional elements recited in the claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
The additional elements include: generic processors, computer storage media, data access operations, optimization solvers, GPU or HPC environments, quantum annealer invocation.
These are well-understood, routine, and conventional computer components used for their ordinary purposes.
Merely implementing an abstract idea using generic computing elements does not provide an inventive concept (see Alice Corp., 573 U.S. 208).
Furthermore, the claimed steps of:
• assigning cargo blocks to containers
• determining packing arrangements
• solving optimization problems
represent conventional data processing and mathematical operations, which do not transform the abstract idea into patent-eligible subject matter.
Accordingly, the claims do not recite significantly more than the abstract idea.
Independent Claim Analysis
Claim 1
Claim 1 recites a computer-implemented method including: accessing cargo data, assigning cargo blocks to containers, solving optimization problems, determining packing arrangements.
These steps correspond to mathematical optimization and logistics planning, which are abstract ideas.
The claim further recites invoking a quantum annealer and performing the method on a computer. However, these elements merely represent generic computer implementation and do not meaningfully limit the abstract idea.
Thus, claim 1 is directed to an abstract idea without significantly more.
Claim 11 recites a computer system including a processor and storage media configured to perform the same steps as claim 1.
System claims that merely recite generic computing hardware performing an abstract idea are also ineligible (see Alice Corp., 573 U.S. 208).
Accordingly, claim 11 is also directed to an abstract idea without significantly more.
Claim 17 recites a computer storage medium storing instructions that cause computing devices to perform the same method steps.
Because the instructions implement the same abstract idea using generic computing devices, claim 17 is also directed to an abstract idea without significantly more.
Dependent Claims 2–10, 12–16, and 18–20 further limit the independent claims by reciting additional features such as: knapsack algorithms, objective functions, center of gravity calculations, constraint density, linear programming, GPU/HPC processing.
These limitations merely describe additional mathematical concepts or conventional computing techniques applied to the abstract idea.
Such limitations do not provide an inventive concept because they represent well-known optimization techniques and conventional computer operations.
Therefore, the dependent claims also fail to amount to significantly more than the abstract idea.
Claims 1–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas relating to mathematical optimization and logistics planning, and the claims do not include additional elements sufficient to amount to significantly more than the abstract idea itself.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1–20 are rejected under 35 U.S.C. 103 as being unpatentable over Kuck et al. (US 2023/0245036, hereinafter Kuck) in view of Wintz et al. (US 2021/0150473) and further in view of GAULTIERI et al. (US 2021/0395026, hereinafter Gaultieri).
With respect to claims 1, 11 and 17, Kuck disclose a computer-implemented method for determining a plan for loading objects into transport containers (see for example abstract, paragraph [0046] and FIG. 18A):
accessing cargo block data of one or more cargo blocks (see for example paragraphs [0030] and [0048] and FIG. 18A step 1802);
cargo blocks to be arranged into one or more containers for transport by a vehicle having a payload area (see for example paragraph [0035] and [0036]);
assigning each cargo block … to a container based on constraints (see for example paragraphs [0035], [0036] [0038] discloses determining a plan to partition objects across multiple transport containers based on predefined constraints such as weight distribution and container dimensions);
based at least in part on one or more constraints (see for example paragraph [0036] discloses constraints including container dimensions, robotic resource constraints, weight distribution constraints, and scoring functions evaluating density and stability);
solutions usable to determine a packing arrangement (see for example paragraphs [0045] and [0049] – [0051] discloses determining an arrangement of objects in a container based on optimization models).
Kuck does not explicitly disclose invoking a quantum annealer to generate one or more solutions to optimization problems, for subsections of the payload area and packing arrangement of cargo blocks within containers for each subsection of the payload area.
However, Wintz teaches the feature of invoking a quantum annealer to generate one or more solutions to optimization problems (see for example paragraphs [0035], [0048], and FIG. 4), and
Gaultieri teaches the feature of for subsections of the payload area and packing arrangement of cargo blocks within containers for each subsection of the payload area (see for example figure 4 and paragraphs [0058] –[0062] discloses determining container packing solutions specifying positions and orientations of items within a container space and also dividing a container loading problem into spatial regions and evaluating packing configurations for subsets of the container space).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to combine the teachings of Kuck, Wintz, and Gualtieri, since Kuck discloses a framework for determining container loading plans and arrangements based on constraints and optimization models, Wintz teaches generating candidate container loading solutions using optimization solvers to improve efficiency of container loading operations and Gualtieri teaches determining spatial packing configurations of items within containers using computational packing algorithms. A person of ordinary skill in the art would have been motivated to combine these teachings because container loading is a known combinatorial optimization problem involving numerous constraints such as space, weight distribution, and item orientation.
Combining these teachings would have predictably resulted in a system capable of generating optimized packing arrangements for cargo blocks within container subsections, as claimed. Such a combination would have been obvious to improve solution quality and computational efficiency when solving NP-hard container packing problems, which commonly require multiple optimization techniques.
With respect to Claims 2, 12 and 18 Gualtieri further teaches iterative assignment using a knapsack algorithm (see for example paragraphs [0058]–[0061] discloses heuristic and combinatorial optimization approaches for container packing problems, which are known implementations of knapsack-type algorithms).
With respect to Claims 3, 13 and 19 Kuck further teaches objective function minimizing torque around center of gravity (see paragraphs [0036] and [0045] teaches weight distribution constraints ensuring stable transport of the container load).
Weight distribution inherently requires balancing loads relative to the center of gravity.
With respect to Claims 4 and 5 Wintz further teaches accessing a generic modeling language file and converting to code for quantum annealer or HPC environment (see paragraphs [0045]–[0051] teaches generating optimization models and executing them using computing systems).
Thus conversion of optimization models to executable code for different computing environments would have been obvious.
With respect to Claims 6, 16 and 20 Gualtieri further teaches determining subsections based on constraint density (see paragraph [0042] teaches dividing the container packing problem into spatial segments or subsets of the container space to reduce computational complexity).
With respect to Claim 7 Kuck further teaches maximizing loaded weight subject to vehicle weight threshold (see paragraph [0036] teaches weight distribution and loading constraints to ensure safe container transport).
With respect to Claim 8 Kuck further teaches size constraints of cargo blocks (see paragraphs [0036] and [0048] discloses object dimensions used when determining loading arrangements).
With respect to Claims 9 and 14 Wintz further teaches aggregating solutions to generate final packing arrangement (see paragraphs [0055]–[0057] discloses evaluating candidate solutions and selecting the optimal loading configuration).
With respect to Claims 10 and 15 Kuck disclose
when determining to generate the one or more solutions to the one or more optimization problems using the HPC environment, converting the LP file to HPC code for a graphics processing unit (“GPU”) (see for example paragraphs [0027]–[0034] describing determining loading plans and constraints, and paragraphs [0100]–[0108] describing partitioning and arrangement determination; see also FIG. 1 illustrating the container loading system and FIG. 18A illustrating the loading optimization process).
However, Kuck do not explicitly disclose the step of defining the one or more optimization problems using a linear programming (LP) file; determining whether to generate the one or more solutions to the one or more optimization problems using a high-performance computing (“HPC”) environment and converting the LP file to GPU code.
Wintz disclose defining optimization problems using linear programming formulations and generating solver code from the LP formulation (see for example paragraphs [0036]–[0042] describing defining optimization models using linear programming, and paragraphs [0064]–[0071] describing converting optimization models into solver-executable code; see also FIG. 3 illustrating an optimization framework), and
Wintz further disclose executing optimization solutions within high-performance computing environments including GPU-based solvers (see for example paragraphs [0085]–[0090] describing GPU-accelerated computation and parallel solver execution),
additionally, Gualtieri disclose executing optimization algorithms for container loading using GPU or parallel computing resources to compute packing solutions (see for example paragraphs [0054]–[0061] describing container packing optimization using computational resources, and paragraphs [0072]–[0078] describing GPU-based computation of packing solutions; see also FIG. 4 illustrating the packing optimization architecture), and
Gualtieri further disclose aggregating computed packing solutions generated by computational solvers (see for example paragraphs [0080]–[0085] describing aggregation of solutions generated by optimization solvers).
Conclusion
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/ROKIB MASUD/Primary Examiner, Art Unit 3627
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