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 as being directed to patent-ineligible subject matter.
With respect to independent claims, claim 1 is directed to an order fulfillment control system comprising functional components (controller, memory, inference module, training module) configured to issue SKU multiplicity values, record operational data, analyze live data, and retrain a control model using machine learning based on operational priorities.
Step 1 — Statutory Category
Claim 1 is nominally directed to a machine, a statutory category.
Step 2A, Prong One — Judicial Exception
Claim 1 is directed to an abstract idea, specifically:
Managing inventory and fulfillment operations, and
Collecting, analyzing, and using data to make operational recommendations, including training and updating decision logic.
These concepts fall within:
Certain methods of organizing human activity (inventory management, warehouse logistics), and
Mental processes / mathematical concepts, including analyzing data, issuing recommendations, optimizing quantities, and retraining models.
Courts have consistently held that inventory optimization, logistics control, and data-driven decision making constitute abstract ideas (see Alice, Electric Power Group, SAP America, In re Killian).
Step 2A, Prong Two — No Practical Application
The claim does not integrate the abstract idea into a practical application because:
The controller, memory, inference module, and training module are recited at a high level of generality;
The claim merely uses generic computing components to automate known warehouse decision-making practices;
No improvement to computer technology itself is recited (e.g., no new data structure, processing technique, or hardware interaction).
The claimed machine learning is invoked functionally, without specifying how the learning improves computer performance rather than business outcomes.
Accordingly, the claim remains directed to an abstract idea.
Step 2B — No Inventive Concept
Claim 1 does not include an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter because:
The recited components (controller, memory, data storage, inference module) are generic computer components;
Machine learning is recited as a black-box result, not a specific technical implementation;
Using priorities, rewards, penalties, and live data represents routine and conventional optimization techniques.
The claim merely implements the abstract idea on a generic computer system, which is insufficient under Alice.
Thus, claim 1 is rejected under 35 U.S.C. § 101.
With respect to claim 10, it recites a method for controlling product storage and order fulfillment, including issuing SKU multiplicity values, recording operational data, analyzing live data, and retraining a control using machine learning.
The method steps correspond directly to the abstract concepts discussed for claim 1 and are likewise directed to:
Inventory management, and
Data analysis and optimization for logistics control.
The method merely automates mental processes and business practices using generic computing steps. No additional elements integrate the abstract idea into a practical application, and no inventive concept is present.
Thus claim 10 is rejected under 35 U.S.C. § 101.
Claim 18 is directed to a non-transitory computer-readable medium storing instructions that cause a processor to perform inventory control, issue SKU multiplicity values, and retrain an AI model.
The claim is directed to the same abstract idea as claims 1 and 10 and merely recites instructions to execute the abstract idea on a computer.
Storing instructions on a computer-readable medium does not confer eligibility where the underlying process is abstract (Alice, In re Marco Guldenaar).
Thus claim 18 is rejected under 35 U.S.C. § 101.
Dependent Claims
Claims 2–9 add limitations such as:
Simulation-based training with rewards and penalties,
Use of historical, hypothetical, or synthetic data,
Storage systems for totes,
Operational priorities and thresholds,
Human or robotic decanters,
Retraining based on time intervals or metrics.
These additional limitations:
Merely refine the abstract idea of inventory optimization,
Recite data types, constraints, or sources without technological improvement,
Reflect routine operational considerations in warehouse management.
None of these claims introduce a technological improvement to computer functionality or a non-conventional implementation.
Thus claims 2–9 are rejected under 35 U.S.C. § 101.
Claims 11–17 further specify:
Simulation-based retraining,
Reward and penalty mechanisms,
Historical data usage,
Storage system configurations,
Optimization priorities,
Human or robotic decanting.
These claims merely add insignificant extra-solution activity or field-of-use limitations to the abstract method of claim 10.
They do not add an inventive concept or integrate the abstract idea into a practical application.
Thus claims 11–17 are rejected under 35 U.S.C. § 101.
Claims 19–20 further recite updating and retraining the SKU multiplicity control using priorities and multiple multiplicity values.
These limitations merely specify how the abstract instructions are organized, without adding any non-generic computing functionality.
Thus claims 19–20 are rejected under 35 U.S.C. § 101.
Considering all of the factors, claims 1–20 are rejected under 35 U.S.C. § 101 as being directed to an abstract idea without significantly more.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lert, JR et al. (US2018/0150793A1, hereinafter LERT) in view of Malecha et al. (US2019/0347606 A1, hereinafter MALECHA).
With respect to claim 1, LERT discloses an automated warehouse fulfillment system in which a controller manages storage and fulfillment operations, including decanting products into totes and sub-totes based on SKU demand and velocity characteristics ([0008], [0012], [0031]).
LERT further teaches maintaining inventory state data for products stored in totes and using such data to control warehouse operations ([0020], [0026], [0041]).LERT additionally discloses robotic and human agents for decanting products into selected tote quantities for storage and fulfillment ([0034], [0036]).
However, LERT does not explicitly disclose retraining an SKU multiplicity control using machine learning based on live warehouse state data and optimization priorities.
MALECHA discloses a computerized inventory management system that receives real-time inventory data, stores current state data, and determines optimal inventory distribution actions using computational analysis ([0017], [0024], [0032]).
MALECHA further teaches dynamically updating inventory decision logic based on historical and live data inputs to optimize operational objectives such as balance, throughput, and efficiency ([0038], [0045]).
MALECHA also discloses that such optimization may be implemented using machine learning or adaptive models retrained using operational data ([0049], [0052]).
It would have been obvious to one of ordinary skill in the art to modify the fulfillment system of LERT to incorporate the adaptive optimization and retraining techniques of MALECHA in order to improve inventory efficiency, responsiveness, and accuracy of SKU-based tote allocation. Both references are in the same field of warehouse inventory and fulfillment management, and MALECHA merely applies well-known computational optimization techniques to LERT’s known fulfillment system. Such a combination represents a predictable use of prior-art elements according to known methods.
With respect to claim 2, LERT discloses simulated warehouse workflows and automated evaluation of decanting operations ([0039], [0042]), and
MALECHA discloses applying performance metrics, rewards, and penalties to guide optimization logic ([0046], [0050]).
With respect to claim 3, LERT discloses recording operational data from warehouse tasks ([0020]), and
MALECHA discloses generating and using simulated inventory data for optimization ([0035]).Thus, claim 3 is unpatentable over LERT in view of MALECHA.
With respect to claims 4–5 LERT teaches SKU velocity based on historical demand ([0031]).MALECHA teaches predictive modeling based on historical and hypothetical inventory scenarios ([0040])..
With respect to claim 6, LERT explicitly discloses warehouse storage systems that store totes for subsequent fulfillment ([0026], [0033]).
With respect to claim 7, MALECHA discloses optimizing inventory while maintaining thresholds and balancing competing operational objectives ([0038], [0052]).Thus, claim 7 is unpatentable over LERT in view of MALECHA.
With respect to claim 8, MALECHA discloses recalculating and updating inventory logic when metrics exceed operational windows ([0045], [0049]).
With respect to claim 9 LERT explicitly discloses both human and robotic agents performing decanting operations ([0034], [0036]).
With respect to claim 10–17 recite method steps corresponding directly to the system limitations of claims 1–9. LERT and MALECHA disclose performing warehouse fulfillment control, issuing SKU-based tote quantities, recording operational data, issuing recommendations based on live data, and retraining optimization logic ([0012], [0031], [0032], [0049]).
With respect to claim 18–20 recite non-transitory computer-readable media storing instructions to perform the claimed operations. LERT and MALECHA disclose computer-implemented inventory and fulfillment control systems with stored executable instructions ([0020], [0049]).
Thus, claims 18–20 are unpatentable under §103 as they merely recite computer-readable implementations of the unpatentable methods.
Conclusion
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/ROKIB MASUD/Primary Examiner, Art Unit 3627