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 .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “This is not a generic computer function but rather a specific technological implementation that "provides a detailed, three-dimensional representation and view of the warehouse.",” Examiner respectfully asserts that the use of a digital twin to generate “labeled coordinates” of the warehouse is not sufficient to prove integration into a practical application or anything significantly more. The generation of coordinates, but for the “digital twin,” is nothing more than an abstract process defining the step of mapping the warehouse. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Regarding Applicant’s assertion of “This limitation specifies the use of "a pathfinding algorithm, such as the A (A star) pathfinding algorithm" to "calculate the most efficient paths for moving items within the warehouse, considering various factors like distances, coordinates, and time consumed." Specification, paragraph [0060]. The Specification explains that "[e]mbodiments of the present invention may measure the cost of traveling ('travel cost') from one slot to another based on the distance between those slots" and that "[e]xamples of types of costs that the cost computation engine 106 may calculate and use as proxies for distance include: * Picking Costs: The costs associated with the process of retrieving ('picking') items from their storage locations in the warehouse to fulfill orders.",” Examiner respectfully disagrees. Examiner respectfully asserts that the “pathfinding algorithm” of the claim is an abstract limitation for consideration under Step 2A, Prong 1. Therefore, the use of an algorithm to generate an “efficient path,” as drafted, is not sufficient to prove integration into a practical application because this purported improvement is rooted in the abstract limitations for consideration under Step 2A, Prong 1 and not the additional elements for consideration under Step 2A, Prong 2 or Step 2B. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “The amended claims demonstrate integration into a practical application by improving the technical field of warehouse management systems through a specific technological solution. As explained in the Specification, "[a]dditionally, the digital twin of the warehouse represents a significant improvement to computer technology" as "[t]his virtual model may be dynamically updated in real-time to mirror the physical state of the warehouse, incorporating changes in inventory, layout modifications, and operational adjustments." Specification, paragraph [0095]. The digital twin "enables simulations and optimizations that transform the planning and operational strategies within the warehouse, effectively turning data into actionable insights and operational enhancements" and "[t]his transformation of abstract data into a detailed, interactive model that can predict outcomes and influence real-world operations is a clear example of a feature that changes an entity into a different state or thing.",” Examiner respectfully disagrees with Applicant’s assertions. The present claims do not comprise any particular improvements to the functioning of the digital twin or to any other technology or technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Regarding Applicant’s assertion of “Under the guidance provided in the Manual of Patent Examining Procedure § 2106, limitations that may indicate integration into a practical application include "[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field" and "[a]pplying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment." The amended claims satisfy both criteria by providing specific technological improvements to warehouse management systems through the integration of three-dimensional spatial modeling, pathfinding algorithms, and bilevel optimization techniques.,” Examiner respectfully disagrees. Examiner respectfully asserts that the “pathfinding algorithm” and “bilevel optimization” of the claim is an abstract limitation for consideration under Step 2A, Prong 1. Therefore, the use of an algorithm to generate an “efficient path” or “bilevel optimization,” as drafted, is not sufficient to prove integration into a practical application because this purported improvement is rooted in the abstract limitations for consideration under Step 2A, Prong 1 and not the additional elements for consideration under Step 2A, Prong 2 or Step 2B. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology” Furthermore, with respect to the three-dimensional spatial modeling of the claim, the present claims do not comprise any particular improvements to the functioning of the digital twin or to any other technology or technical field. The generation of coordinates, but for the “digital twin,” is nothing more than an abstract process defining the step of mapping the warehouse. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Regarding Applicant’s assertion of “The Kim Memo makes clear that establishing the BRI first is a fundamental requirement of the eligibility analysis, stating that examiners should "establish the BRI of the claim before conducting the eligibility analysis" and that "this step should not be skipped." The Specification provides detailed explanations of these claim terms, including that the digital twin "provides a detailed, three-dimensional representation and view of the warehouse" (Specification, paragraph [0047]) and involves "3D modeling technology to engage in a rack design phase" (Specification, paragraph [0048]). Without establishing the BRI of these terms in light of the Specification, the Examiner's eligibility analysis lacks the proper foundation. This failure to establish the BRI is a fatal defect in the 35 U.S.C. § 101 rejections of claims 1-20, and the rejections should be withdrawn.,” Examiner respectfully disagrees. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 103 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “While Jahani teaches dynamic slotting optimization and Nicotera teaches digital twin technology for warehouse management, neither reference teaches or suggests the specific integration of generating a digital twin of a facility as recited by claim 1 and then using that digital twin in combination with sales history data, demand forecast data, and supply chain plan data to generate a prediction of target allocation, compute costs, and generate a slotting strategy.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Mendes and Liu references to cure the deficiencies of the prior art combination of the record.
Accordingly, the present claims are rejected under 35 USC 103.
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-16 are directed to a method and claims 17-20 are directed to a system. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1 and 17 recite generating a slotting strategy, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors, as well as business relations. Claim 1 recites limitations, similarly recited in claim 17, including “generating a prediction of a target allocation of items in the facility based on sales history data, demand forecast data, and supply chain plan data; generating product affinities based on the sales history data; generating a simulated order book; computing costs associated with the simulated order book based on the simulated order book, the product affinities the prediction of the target allocation of items in the facility, and the digital twin, wherein computing costs comprises using a pathfinding algorithm to calculate paths for moving items within the facility, and generating a slotting strategy based on the costs associated with the simulated order book, wherein generating the slotting strategy comprises performing bilevel optimization where upper-level decisions determine optimal inventory levels to minimize restocking costs and lower-level decisions determine SKU arrangement to minimize order fulfillment costs.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of the preamble, covers an abstract idea but for the recitation of generic computer components. That is, other than the preamble, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 5-10 and 12-13 further narrow the abstract idea identified in the independent claims.
Dependent claims 2-4, 11, 14-16, and 18-20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1 and 17 do not integrate the judicial exception into a practical application. Independent claim 1 is directed to a method that recites “a method, performed by at least one computer processor executing computer program instructions stored on at least one non-transitory computer-readable medium, the method comprising,” which is in the preamble of the claim. Independent claim 17 recites “a system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by at least one computer processor to perform a method,” which is recited in the preamble of the claim. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claims 1 and 17 further recite the additional elements of “generating a digital twin of a facility, wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types,” “generating a prediction of a target allocation of items in the facility based on the digital twin,” “and wherein the digital twin is used to calculate travel distances based on horizontal and vertical movements between specific slot coordinates,” and “…minimize order fulfillment costs based on the digital twin's spatial representation.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 5-10 and 12-13 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not prove integration into a practical application.
Dependent claims 2 and 18 introduces the additional element of “wherein generating a digital twin of the facility comprises: constructing a three-dimensional model of the facility using input from at least one three-dimensional scanning device.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claims 3 and 19 introduces the additional element of “wherein generating the digital twin of the facility further comprises: capturing real-time data from the facility using sensors, the real-time data representing at least one of changes in a layout inventory level, or operational status of the facility; and updating the digital twin in real-time based on changes detected in the facility based on the real-time data captured from the sensors.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claims 4 and 20 introduces the additional element of “wherein generating the digital twin of the facility further comprises: applying machine learning to refine the digital twin based on discrepancies between predicted and actual facility operations.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein computing costs associated with the simulated order book includes: calculating total fulfillment costs based on the simulated order book and the digital twin of the facility.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 14 introduces the additional element of “wherein optimizing the place of items within the facility comprises: simulating different slotting configurations using the digital twin to compare cost implications of each configuration.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 15 introduces the additional element of “further comprising: integrating real-time feedback from the facility’s operational data to dynamically adjust the slotting strategy in response to changes in the operational data.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 16 introduces the additional element of “further comprising: applying a machine learning model to predict the impact of the slotting strategy on future operational efficiency and cost savings.” The limitation of applying a machine learning model to predict an impact provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the dependent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1 and 17 do not comprise anything significantly more than the judicial exception. Independent claim 1 is directed to a method that recites “a method, performed by at least one computer processor executing computer program instructions stored on at least one non-transitory computer-readable medium, the method comprising,” which is in the preamble of the claim. Independent claim 17 recites “a system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by at least one computer processor to perform a method,” which is recited in the preamble of the claim. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claims 1 and 17 further recite the additional elements of “generating a digital twin of a facility, wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types,” “generating a prediction of a target allocation of items in the facility based on the digital twin,” “and wherein the digital twin is used to calculate travel distances based on horizontal and vertical movements between specific slot coordinates,” and “…minimize order fulfillment costs based on the digital twin's spatial representation.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 5-10 and 12-13 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not provide anything significantly more than the judicial exception.
Dependent claims 2 and 18 introduces the additional element of “wherein generating a digital twin of the facility comprises: constructing a three-dimensional model of the facility using input from at least one three-dimensional scanning device.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claims 3 and 19 introduces the additional element of “wherein generating the digital twin of the facility further comprises: capturing real-time data from the facility using sensors, the real-time data representing at least one of changes in a layout inventory level, or operational status of the facility; and updating the digital twin in real-time based on changes detected in the facility based on the real-time data captured from the sensors.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claims 4 and 20 introduces the additional element of “wherein generating the digital twin of the facility further comprises: applying machine learning to refine the digital twin based on discrepancies between predicted and actual facility operations.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein computing costs associated with the simulated order book includes: calculating total fulfillment costs based on the simulated order book and the digital twin of the facility.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 14 introduces the additional element of “wherein optimizing the place of items within the facility comprises: simulating different slotting configurations using the digital twin to compare cost implications of each configuration.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 15 introduces the additional element of “further comprising: integrating real-time feedback from the facility’s operational data to dynamically adjust the slotting strategy in response to changes in the operational data.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 16 introduces the additional element of “further comprising: applying a machine learning model to predict the impact of the slotting strategy on future operational efficiency and cost savings.” The limitation of applying a machine learning model to predict an impact provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the dependent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected 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 (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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 5-8, and 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jahani et al. (US 20180253680 A1) in view of Mendes (“Slotting Strategy in a Pharmaceutical Wholesaler Warehouse,” 2021) in view of Liu (“Order Picking Method for Multi-entity Cooperation in Picking Warehouses,” August 5, 2023.).
Regarding claim 1, Jahani teaches a method, performed by at least one computer processor executing computer program instructions stored on at least one non-transitory computer-readable medium, the method comprising:
generating a prediction of a target allocation of items in the facility based on the digital twin, sales history data, demand forecast data, and supply chain plan data ([0088] teaches dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analysis, wherein [0179-0180] teach the steps of the proposed algorithm for profiling and slotting optimization of the forward area include importing data including SKU data, order data, rack data, and facility data, wherein the order information includes historical demand data, wherein [0313] teaches the order data for 50 days of history is simulated and a dynamic slotting strategy that uses the daily demand quantity forecasting for 30 days ahead, wherein [0260] teaches running the dynamic model utilizing forecasted demand data and pick data, wherein the updated forecast data includes forecast data for a period of time, wherein the planning can involve calculating the total costs during the relevant time window, which is the planning horizon, wherein [0202] teaches the time window for analysis includes considering the seasonality of the products over the planning horizon when determining storage locations; see also: [0190]);
generating product affinities based on the sales history data ([0083] teaches historical time-based demand data provides valuable information for models, which have demand as an input, wherein [0212] teaches the PSO algorithm is proposed for both profiling and slotting optimization, wherein it determines the best size of a different pick mode along with SKU assignment and slot allocation, wherein the window analysis can be utilized to determine how to share the storage area with the seasonal fast movers with different active periods, wherein [0202] teaches the time window for analysis includes considering the seasonality of the products over the planning horizon when determining storage locations, wherein seasonal products are fast movers in their active period and inactive during the rest of the planning horizon, wherein [0467-0468] teach the demand forecasting of products with seasonal demand while further considering the relevance and availability of historical data; see also: [0099, 0110, 0215, 0219, 0426, 0467]);
generating a simulated order book ([0313] teaches the order data for 50 days of history is simulated and a dynamic slotting strategy that uses the daily demand quantity forecasting for 30 days ahead, as well as in [0424] teaches order transaction data is simulated and presented with a portion of SKUs with normal demand pattern, as well as in [0135-0136] teach inputting the SKU, order, rack, and facility data in order to output the profiling and slotting optimization of the multi-mode forward area; see also: [0261, 0343]);
computing costs associated with the simulated order book based on the simulated order book, the product affinities, the prediction of the target allocation of items in the facility (Fig. 12 and [0260] teach calculating the total cost during the planning horizon for the picking and replenishment of SKUs assigned to the various areas, wherein [0121] teaches the picking and replenishment costs of the SKUs selected for the forward area can be reduced in multi-mode forward areas, wherein while the number of cases stored in some pick modes can be more flexible, others have a fixed storage capacity, wherein the number of slots given to the SKUs in the carton flow rack are optimized by the allocation problem, wherein [0122] teaches selecting the best types of racks and their effective size along with the best assignment of SKUs to the pick mode within the forward area, wherein [0260] teaches running the dynamic model utilizing forecasted demand data and pick data, wherein the updated forecast data includes forecast data for a period of time, wherein the planning can involve calculating the total costs during the relevant time window, which is the planning horizon, wherein [0202] teaches the time window for analysis includes considering the seasonality of the products over the planning horizon when determining storage locations, wherein seasonal products are fast movers in their active period and inactive during the rest of the planning horizon; see also: [0110, 0215, 0348, 0467]); and
and generating a slotting strategy based on the costs associated with the simulated order book ([0089] teaches generating optimal solutions of the dynamic slotting model and developing a threshold policy that generates an optimal solution in terms of both the total cost of picking and replenishment and the forward area assignment and allocation, wherein [0097-0098] teach the optimal slot allocation enhances warehouse productivity and reduces operational costs, wherein the optimal size of the forward area reduces the total cost of picking and replenishments, as well as in [0465-0466] teaches dynamic slotting of the forwarding area is a warehousing approach where the set of SKUs and slots allocated to them are changed to continuously have an updated layout that will improve the picking and replenishment costs, wherein the dynamic slotting optimization is performed with discrete slot allocation with various replenishment strategies, wherein the system can find the optimal SKU assignment and slot allocation, wherein [0212] teaches the PSO algorithm is proposed for both profiling and slotting optimization, wherein it determines the best size of a different pick mode along with SKU assignment and slot allocation, wherein the window analysis can be utilized to determine how to share the storage area with the seasonal fast movers with different active periods, as well as in Fig. 12 and [0260] teach calculating the total cost during the planning horizon for the picking and replenishment of SKUs assigned to the various areas, wherein [0110] teaches dynamic slotting by updating the forwarding layout area of a warehouse on a frequent basis by using the replenishment of empty slots with the correct SKUs without any moves; see also: [0467-0468]).
However, Jahani does not explicitly teach generating a digital twin of a facility, wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types; generating a prediction of a target allocation of items in the facility based on the digital twin; computing costs associated with the simulated order book based on the digital twin; wherein computing costs comprises using a pathfinding algorithm to calculate paths for moving items within the facility, and wherein the digital twin is used to calculate travel distances based on horizontal and vertical movements between specific slot coordinates; wherein generating the slotting strategy comprises performing bilevel optimization where upper-level decisions determine optimal inventory levels to minimize restocking costs and lower-level decisions determine SKU arrangement to minimize order fulfillment costs based on the digital twin's spatial representation.
From the same or similar field of endeavor, Mendes teaches generating a digital twin of a facility (Pg. iii teaches utilizing optislot, a warehouse optimization software, as a digital twin of the real warehouse allowing for simulation and testing of scenarios, wherein Pg. 3 teaches optislot is used as slotting optimization software; see also: Pg. 4),
generating a prediction of a target allocation of items in the facility based on the digital twin (Pgs. 23-24 teach allocating the best products to the best/prime slot requires a knowledge of how products usually behave including how demand varies over time, wherein the demand pattern is a relevant factor in deciding what products are moved to the A-Frame, wherein the SKUs are classified according to the ABC-XYZ method, wherein the articles are ranked according to demand variability, wherein Pg. 30 teaches the slot selected for a given product must be able to meet the demand for the SKUs located within it, as well as in Pg. 53 teaches analyzing the demand and turnover rate of all products in order to make slotting decisions; see also: Pgs. 44-46);
computing costs associated with the simulated order book based on the digital twin (Pg. 13 teaches the main objectives are minimizing time, cost and distance, wherein slotting optimization projects must reduce costs and increase efficiency, wherein Pg. 14 teaches simulating several scenarios regarding slotting in order to determine how product storage influences the operational costs, wherein Pg. 27 teaches utilizing optislot in order to prioritize the slots according to specified goals including slot average picking cost and slot name sequence, as well as prioritize the products according to their velocity and weight, wherein each SKU is assigned a priority order based on the highest priority slot available that satisfies all the specified constraints including location type, inventory requirements, and more, wherein the software identifies the best slot product combination by using greedy heuristics; see also: Pgs. 28-30);
wherein computing costs comprises using a pathfinding algorithm to calculate paths for moving items within the facility (Pg. 13 teaches the main objectives are minimizing time, cost and distance, wherein slotting optimization projects must reduce costs and increase efficiency, wherein Pg. 14 teaches simulating several scenarios regarding slotting in order to determine how product storage influences the operational costs, wherein Pg. 27 teaches utilizing optislot in order to prioritize the slots according to specified goals including slot average picking cost and slot name sequence, as well as prioritize the products according to their velocity and weight, wherein each SKU is assigned a priority order based on the highest priority slot available that satisfies all the specified constraints including location type, inventory requirements, and more, wherein the software identifies the best slot product combination by using greedy heuristics, wherein Pgs. 35-36 teach the optislot can evaluate scenarios based on metrics including lines per hour, total distance, travel time, time to pick, total cost, and number of replenishments; see also: Pgs. 28-30),
and wherein the digital twin is used to calculate travel distances based on horizontal and vertical movements between specific slot coordinates (Pg. 13 teaches the main objectives are minimizing time, cost and distance, wherein slotting optimization projects must reduce costs and increase efficiency, wherein Pg. 14 teaches simulating several scenarios regarding slotting in order to determine how product storage influences the operational costs, wherein Pg. 27 teaches utilizing optislot in order to prioritize the slots according to specified goals including slot average picking cost and slot name sequence, as well as prioritize the products according to their velocity and weight, wherein each SKU is assigned a priority order based on the highest priority slot available that satisfies all the specified constraints including location type, inventory requirements, and more, wherein the software identifies the best slot product combination by using greedy heuristics, wherein Pgs. 35-36 teach the optislot can evaluate scenarios based on metrics including lines per hour, total distance, travel time, time to pick, total cost, and number of replenishments, wherein Pg. 12 teaches evaluating the rack dimensions including height, depth, and length, as well as assignment of the products families to specific aisles, slotting products to locations inside of the aisle and meeting the product families, and determining the minimum locations possible to the less demanded products, as well as in Pgs. 18-19 teach evaluating the different zones, case-flow, height, depth, and capacity per level, wherein the shelving can be divided into groups, wherein each level can have 1-6 different SKUs per level, wherein Pg. 32 teaches the optislot utilizes the virtual warehouse identification information including dimensions related to width, height, and length, as well as unit movement, hits, current location, and more, as well as in Pg. 33 teaches the number of channels required for a SKU in a given frame can be calculated based on a formula, wherein the optislot can determine whether the slot has capacity to keep all existing stock, or if items are required to go to overstock locations; see also: Pgs. 8, 20, 28-30);
wherein generating the slotting strategy comprises performing bilevel optimization where upper-level decisions determine optimal inventory levels to minimize restocking costs and lower-level decisions determine SKU arrangement to minimize order fulfillment costs based on the digital twin's spatial representation (Pg. 3 teaches slotting products must be done in order according to the operation’s slotting objectives including minimizing picking costs, travel distance, number of replenishments, and more, as well as in Pg. 13 teaches the main objectives are minimizing time, cost and distance, wherein slotting optimization projects must reduce costs and increase efficiency, wherein Pg. 14 teaches simulating several scenarios regarding slotting in order to determine how product storage influences the operational costs, wherein Pg. 27 teaches utilizing optislot in order to prioritize the slots according to specified goals including slot average picking cost and slot name sequence, as well as prioritize the products according to their velocity and weight, wherein each SKU is assigned a priority order based on the highest priority slot available that satisfies all the specified constraints including location type, inventory requirements, and more, wherein the software identifies the best slot product combination by using greedy heuristics, wherein Pgs. 35-36 teach the optislot can evaluate scenarios based on metrics including lines per hour, total distance, travel time, time to pick, total cost, and number of replenishments, as well as in Pg. 53 teaches the warehouse and picking characteristics were utilized by the optislot software in order to optimize the current situation based on demand, turnover rate, slotting decisions, storage areas, wherein the strategy was designed to reduce the moves between the two main picking areas; see also: Pgs. 30-34).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Jahani to incorporate the teachings of Mendes to include generating a digital twin of a facility, generating a prediction of a target allocation of items in the facility based on the digital twin; computing costs associated with the simulated order book based on the digital twin; wherein computing costs comprises using a pathfinding algorithm to calculate paths for moving items within the facility, and wherein the digital twin is used to calculate travel distances based on horizontal and vertical movements between specific slot coordinates; wherein generating the slotting strategy comprises performing bilevel optimization where upper-level decisions determine optimal inventory levels to minimize restocking costs and lower-level decisions determine SKU arrangement to minimize order fulfillment costs based on the digital twin's spatial representation. One would have been motivated to do so in order to provide a digital twin of a warehouse that provides simulation and optimization of scenarios in order to efficiently discover one slotting strategy that matches with the warehouse reality (Mendes, Pg. iii). By incorporating the teachings of Mendes, one would have been able to reduce operational costs and maintain the service quality during slotting and order picking in a warehouse (Mendes, Pg. 3).
However, the combination of Jahani and Mendes does not explicitly teach wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types.
From the same or similar field of endeavor, Liu teaches wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types (Pg. 5 teaches providing an analytical approach with a simulation model that is a digital twin of the MCOP and provides a visualized realization of the processes in MCOP, wherein the simulation model is a cross validation tool of the mathematical models that enables a detailed analysis and performance evaluation under certain scenarios involving various product picking times and entity traveling speeds, wherein Pg. ii teaches the multi-entity cooperative order picking strategy utilizes pickers and transporters collaborate in a picker-to-parts warehouse to complete the order picking, as well as in Pgs. 31-32 teach providing a warehouse layout that includes positions represented by their X, Y, and Z values with the corresponding unit under a specific coordinate system, wherein Pgs. 23-24 teach the warehouse operations data structure provide3s orders, SKUs, line items, nodes, arcs, and slots, wherein the slots relates to storage that connects the representation of the node with the SKUs, wherein the nodes relate to representations of the warehouse layout, and wherein the SKUs relate to each stock keeping unit; see also: Pgs. 27-28).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani and Mendes to incorporate the teachings of Liu to include wherein the digital twin comprises a three-dimensional model with labeled coordinates representing a warehouse layout, slot locations, and slot types. One would have been motivated to do so in order to identify good strategies to apply to robots to improve the performance and efficiency of order picking by identifying the optimal operational decisions regarding workloads and routes (Liu, Pg. ii). By incorporating the teachings of Liu, one would have been able to utilize algorithms to improve the decisions by searching for alternative locations for placing items (Pg. 93).
Regarding claim 17, the claims recite limitations similarly recite in claim 1. Regarding claim 17, Jahani teaches a system comprising at least one non-transitory computer-readable medium having computer program instructions stored thereon, the computer program instructions being executable by at least one computer processor to perform a method ([0015] teaches a computer program product comprising a non-transitory computer readable medium having program code portions stored therein is provided, the program code portions being configured to direct the apparatus to perform operations, as well as in [0530] teaches a processing system that executes software stored on a computer-readable medium), the method comprising. Accordingly, claim 17 is rejected as being unpatentable over Jahani in view of Mendes in view of Liu.
Regarding claim 5, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches wherein generating the prediction of the target allocation of items comprises: optimizing placement of items to minimize average distance traveled for picking operations ([0138] teaches the PSO algorithm refers to generating one alternative for the forward area including alternatives that differ in the number of bays of each pick mode, wherein the average travel distance for picking or replenishment of the items depends on the size of the pick mode, wherein the average travel distance of the pick mode refers to the average horizontal distance that labor traverses to pick or replenish an item, which is the average aisle width, plus the average vertical distance, which is average aisle length, wherein the model disclosed accounts for the different picking and replenishment costs between the pick modes with different sizes within an alternative and also between the same pick mode of different alternatives, wherein the sequence of the pick modes within the forward area is known and is taken into consideration while calculating average travel distance, wherein the farther pick modes would then have higher average pick/replenishment travel distance, wherein [0139] teaches besides the travel distance analysis, SKU, case, and slot dimensions are taken into consideration to conduct the fitting test for the replenishment unit and slot, wherein the model suggests the best orientation of replenishment case, which enhances space utilization, wherein the space allocation is discrete, wherein [0203] teaches shrinking the forward area as required and shortening the travel distance for the rest of the fast movers, wherein [0212] teaches the PSO algorithm is proposed for profiling and slotting optimization, wherein it determines the best size of a different pick mode of the forward area, wherein the size of each pick mode can be smaller and the replenishment travel distances are shorter, wherein [0099-0100] teach minimizing linear travel, and wherein [0094] teaches the zones of the warehouse and information about the SKUs and cases including length, width, height, case pack, and order data can be utilized to search and extract smaller areas that need less travel distance; see also: [0095, 0098, 0314]),
considering constraints related to item size, weight, and storage requirements ([0138] teaches the PSO algorithm refers to generating one alternative for the forward area including alternatives that differ in the number of bays of each pick mode, wherein the average travel distance for picking or replenishment of the items depends on the size of the pick mode, wherein the average travel distance of the pick mode refers to the average horizontal distance that labor traverses to pick or replenish an item, which is the average aisle width, plus the average vertical distance, which is average aisle length, wherein the model disclosed accounts for the different picking and replenishment costs between the pick modes with different sizes within an alternative and also between the same pick mode of different alternatives, wherein the sequence of the pick modes within the forward area is known and is taken into consideration while calculating average travel distance, wherein the farther pick modes would then have higher average pick/replenishment travel distance, wherein [0139] teaches besides the travel distance analysis, SKU, case, and slot dimensions are taken into consideration to conduct the fitting test for the replenishment unit and slot, wherein the model suggests the best orientation of replenishment case, which enhances space utilization, wherein the space allocation is discrete, wherein [0203] teaches shrinking the forward area as required and shortening the travel distance for the rest of the fast movers, wherein [0212] teaches the PSO algorithm is proposed for profiling and slotting optimization, wherein it determines the best size of a different pick mode of the forward area, wherein the size of each pick mode can be smaller and the replenishment travel distances are shorter, wherein [0099-0100] teach minimizing linear travel, and wherein [0094] teaches the zones of the warehouse and information about the SKUs and cases including length, width, height, case pack, and order data can be utilized to search and extract smaller areas that need less travel distance; see also: [0095, 0098, 0314]).
Regarding claim 6, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches wherein generating the prediction of the target allocation of items in the facility comprises: analyzing patterns in sales history data, demand forecast data, and supply chain plan data to optimize item placement ([0088] teaches dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics, wherein the layout of the fast picking area is updated over time with replenishment of the appropriate SKUs, wherein the demand pattern and demand forecasting is explored with models, as well as in [0120] teaches forecasting the demand trend, as well as in [0201] teaches evaluating the historical order data and determining whether the order data has a downward or upward trend, as well as in [0219] teaches raw order data can be used to obtain real-time demand trends for use in evaluating slow movers in the forward area and moving them to the reserve area, wherein the slotting map can be used and the warehouse environment can be treated as dynamic, and thus the forward area layout is updating continuously, and wherein [0313] teaches an order generator can simulate the different demand trends, wherein order data for a history is simulated and a dynamic slotting strategy can be generated that uses the daily demand quantity forecasting for a future planning window; see also: [0319, 0423, 0426, 0468]).
Regarding claim 7, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 6 above.
However, Jahani does not explicitly teach wherein generating the prediction of the target allocation of items further comprises: simulating allocation scenarios using the digital twin to assess the impact of different allocation strategies on operational efficiency.
From the same or similar field of endeavor, Mendes further teaches wherein generating the prediction of the target allocation of items further comprises: simulating allocation scenarios using the digital twin to assess the impact of different allocation strategies on operational efficiency (Pg. 25 teaches assessing if products are being efficiently slotted based on a number of indicators including productivity, losses, and more, wherein Pg. 27 teaches managing the location of products within a facility in order to have an efficient order picking operation based on prioritization of slots and products according to specific, respective goals, wherein Pg. 39 teaches the optislot software can generate scenarios that evaluate efficiently picking performance goals and the number of replenishments and travel time, wherein Pg. 14 teaches simulating several scenarios regarding slotting in order to determine how product storage influences the operational costs, wherein Pg. 27 teaches utilizing optislot in order to prioritize the slots according to specified goals including slot average picking cost and slot name sequence, as well as prioritize the products according to their velocity and weight, wherein each SKU is assigned a priority order based on the highest priority slot available that satisfies all the specified constraints including location type, inventory requirements, and more, wherein the software identifies the best slot product combination by using greedy heuristics; see also: pgs. 13, 28-30, 53-54).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, and Liu to incorporate the further teachings of Mendes to include wherein generating the prediction of the target allocation of items further comprises: simulating allocation scenarios using the digital twin to assess the impact of different allocation strategies on operational efficiency. One would have been motivated to do so in order to provide a digital twin of a warehouse that provides simulation and optimization of scenarios in order to efficiently discover one slotting strategy that matches with the warehouse reality (Mendes, Pg. iii). By incorporating the teachings of Mendes, one would have been able to reduce operational costs and maintain the service quality during slotting and order picking in a warehouse (Mendes, Pg. 3).
Regarding claim 8, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 6 above.
Jahani further teaches wherein generating the prediction of the target allocation of items further comprises: performing cluster analysis to group items based on similar handling and storage characteristics ([0106-0107] teach optimizing the decisions about the forward area includes the selection and quantities of the SKUs in the forward area, wherein the size of the forward area is another critical decision, wherein the decisions about the forward and reserve areas are critical, wherein it is important to store fast moving products in the fast picking area of the forward area, wherein [0119] teaches utilizing an algorithm that optimizes both profiling and slotting simultaneously by utilizing different rack configurations, pick technology specifications, and replenishment policies of the pick mode, wherein the active period of the fast movers and optimal case orientation can be determined, as well as in [0086] teaches a dynamic approach can introduce the latest fast movers to the forward area as opportunity arises, wherein allocated space to items in the forward area can vary over time based on the flexibility that dynamic slotting provides, wherein [0203] teaches active SKUs, which are picked from the similar pick mode zone and whose active periods do not have any overlap, will share one location within a zone, wherein Christmas products may replace the Halloween products, when their active periods are over, wherein this method shrinks the forward area as required and shortens the travel distance for the rest of the fast movers; see also: [0083, 0097, 0108-0109, 0204, 0219, 0322]),
facilitating the creation of zones within the facility optimized for specific types of items ([0106-0107] teach optimizing the decisions about the forward area includes the selection and quantities of the SKUs in the forward area, wherein the size of the forward area is another critical decision, wherein the decisions about the forward and reserve areas are critical, wherein it is important to store fast moving products in the fast picking area of the forward area, wherein [0119] teaches utilizing an algorithm that optimizes both profiling and slotting simultaneously by utilizing different rack configurations, pick technology specifications, and replenishment policies of the pick mode, wherein the active period of the fast movers and optimal case orientation can be determined, as well as in [0086] teaches a dynamic approach can introduce the latest fast movers to the forward area as opportunity arises, wherein allocated space to items in the forward area can vary over time based on the flexibility that dynamic slotting provides, wherein [0203] teaches active SKUs, which are picked from the similar pick mode zone and whose active periods do not have any overlap, will share one location within a zone, wherein Christmas products may replace the Halloween products, when their active periods are over, wherein this method shrinks the forward area as required and shortens the travel distance for the rest of the fast movers; see also: [0083, 0097, 0108-0109, 0204, 0219, 0322]).
Regarding claim 10, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches wherein generating the simulated order book includes: using a simulator to generate predictions of customer orders based on historical sales data and current market trends ([0088] teaches dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics, wherein the layout of the fast picking area is updated over time with replenishment of the appropriate SKUs, wherein the demand pattern and demand forecasting is explored with models, as well as in [0313] teaches the order data for 50 days of history is simulated and a dynamic slotting strategy that uses the daily demand quantity forecasting for 30 days ahead, as well as in [0424] teaches order transaction data is simulated and presented with a portion of SKUs with normal demand pattern, as well as in [0135-0136] teach inputting the SKU, order, rack, and facility data in order to output the profiling and slotting optimization of the multi-mode forward area, as well as in [0201] teaches evaluating the historical order data and determining whether the order data has a downward or upward trend, as well as in [0219] teaches raw order data can be used to obtain real-time demand trends for use in evaluating slow movers in the forward area and moving them to the reserve area, wherein the slotting map can be used and the warehouse environment can be treated as dynamic, and thus the forward area layout is updating continuously, and wherein [0313] teaches an order generator can simulate the different demand trends, wherein order data for a history is simulated and a dynamic slotting strategy can be generated that uses the daily demand quantity forecasting for a future planning window; see also: [0120, 0261, 0319, 0423, 0426, 0468]).
Regarding claim 11, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
However, Jahani does not explicitly teach wherein computing costs associated with the simulated order book includes: calculating total fulfillment costs based on the simulated order book and the digital twin of the facility.
From the same or similar field of endeavor, Mendes further teaches wherein computing costs associated with the simulated order book includes: calculating total fulfillment costs based on the simulated order book and the digital twin of the facility (Pgs. 35-36 teach the optislot can evaluate scenarios based on metrics including lines per hour, total distance, travel time, time to pick, total cost, and number of replenishments, as well as in Pg. 41 teaches the scenarios are selected and evaluated based on total cost and number of replenishments, wherein Pg. iii teaches utilizing optislot, a warehouse optimization software, as a digital twin of the real warehouse allowing for simulation and testing of scenarios, wherein Pg. 3 teaches optislot is used as slotting optimization software; see also: Pg. 4).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, and Liu to incorporate the further teachings of Mendes to include wherein computing costs associated with the simulated order book includes: calculating total fulfillment costs based on the simulated order book and the digital twin of the facility. One would have been motivated to do so in order to provide a digital twin of a warehouse that provides simulation and optimization of scenarios in order to efficiently discover one slotting strategy that matches with the warehouse reality (Mendes, Pg. iii). By incorporating the teachings of Mendes, one would have been able to reduce operational costs and maintain the service quality during slotting and order picking in a warehouse (Mendes, Pg. 3).
Regarding claim 12, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches wherein generating the slotting strategy comprises: optimize the placement of items within the facility to minimize overall fulfillment costs ([0089] teaches the optimal solution of the dynamic slotting model is optimized in terms of both total cost of picking and replenishment and the forward area assignment and allocation, as well as in [0465-0466] teach dynamic slotting can improve picking and replenishment costs, wherein the system can find the optimal SKU assignment and slot allocation, wherein the model used to generate and optimize the SKU assignment and slot allocation can test if the SKU fits the location, and may further determine the best orientation for a case associated with the SKU, wherein this approach is advantageous in achieving an optimal space utilization in situations where relevant dimensions are imposed, wherein after finding the optimal SKU assignment and slot allocation, the system can calculate a savings obtained for each design, wherein the sum difference in costs associated with picking each assigned SKU can be calculated and the system can further calculate replenishment costs, as well as in [0520] teaches the algorithm can perform both profiling and slotting optimization that significantly reduces the replenishment activities and picking costs, while at the same time, maximizing the space utilization within a slot type in the forward area, wherein the algorithm evaluates the slot types in the fast picking areas and determines the best size of each pick mode, along with SKU assignment and slot allocation; see also: [0123, 0134-0137, 0223-0225]).
Regarding claim 13, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 12 above.
Jahani further teaches wherein optimizing the placement of items within the facility comprises: prioritizing item placement based on frequency of access and proximity to shipping areas to reduce travel and handling costs ([0138] teaches the PSO algorithm refers to generating one alternative for the forward area including alternatives that differ in the number of bays of each pick mode, wherein the average travel distance for picking or replenishment of the items depends on the size of the pick mode, wherein the average travel distance of the pick mode refers to the average horizontal distance that labor traverses to pick or replenish an item, which is the average aisle width, plus the average vertical distance, which is average aisle length, wherein the model disclosed accounts for the different picking and replenishment costs between the pick modes with different sizes within an alternative and also between the same pick mode of different alternatives, wherein the sequence of the pick modes within the forward area is known and is taken into consideration while calculating average travel distance, wherein the farther pick modes would then have higher average pick/replenishment travel distance, wherein, wherein [0089] teaches the optimal solution of the dynamic slotting model is optimized in terms of both total cost of picking and replenishment and the forward area assignment and allocation, wherein [0084] teaches the forward area includes items that have a higher picking cost and places them in the forward area in order to decrease picking cost, wherein the SKUs can be selected for the fast picking area and based on the allocated space, wherein [0106-0107] teach optimizing the decisions about the forward area includes the selection and quantities of the SKUs in the forward area, wherein the size of the forward area is another critical decision, wherein the decisions about the forward and reserve areas are critical, wherein it is important to store fast moving products in the fast picking area of the forward area, wherein [0119] teaches utilizing an algorithm that optimizes both profiling and slotting simultaneously by utilizing different rack configurations, pick technology specifications, and replenishment policies of the pick mode, wherein the active period of the fast movers and optimal case orientation can be determined, wherein [0090] teaches the items can be sent to other functional areas or directly to the shipping area, as well as in [0520] teaches the algorithm can perform both profiling and slotting optimization that significantly reduces the replenishment activities and picking costs, while at the same time, maximizing the space utilization within a slot type in the forward area, wherein the algorithm evaluates the slot types in the fast picking areas and determines the best size of each pick mode, along with SKU assignment and slot allocation; see also: [0086, 0123, 0134-0137, 0203, 0223-0225, 0465-0466]).
Regarding claim 14, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 12 above.
However, Jahani does not explicitly teach wherein optimizing the place of items within the facility comprises: simulating different slotting configurations using the digital twin to compare cost implications of each configuration.
From the same or similar field of endeavor, Mendes further teaches wherein optimizing the place of items within the facility comprises: simulating different slotting configurations using the digital twin to compare cost implications of each configuration (Pgs. 35-36 teach the optislot can evaluate scenarios based on metrics including lines per hour, total distance, travel time, time to pick, total cost, and number of replenishments, as well as in Pg. 41 teaches the scenarios are selected and evaluated based on total cost and number of replenishments, wherein Pg. iii teaches utilizing optislot, a warehouse optimization software, as a digital twin of the real warehouse allowing for simulation and testing of scenarios, wherein Pg. 3 teaches optislot is used as slotting optimization software, wherein Pg. 61 teaches comparing the total cost and other parameters between the simulated scenarios, as well as in Pg. 43 teaches generating a final comparison between scenarios including parameters related to total cost and more; see also: Pg. 4).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, and Liu to incorporate the further teachings of Mendes to include wherein optimizing the place of items within the facility comprises: simulating different slotting configurations using the digital twin to compare cost implications of each configuration. One would have been motivated to do so in order to provide a digital twin of a warehouse that provides simulation and optimization of scenarios in order to efficiently discover one slotting strategy that matches with the warehouse reality (Mendes, Pg. iii). By incorporating the teachings of Mendes, one would have been able to reduce operational costs and maintain the service quality during slotting and order picking in a warehouse (Mendes, Pg. 3).
Regarding claim 15, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches further comprising: integrating real-time feedback from the facility’s operational data to dynamically adjust the slotting strategy in response to changes in the operational data ([0088] teaches dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics, wherein the layout of the picking area can be updated over time with replenishment of appropriate SKUs that can periodically re-slot the forward area to reach a predefined target map, wherein the slotting methodology considers seasonality demand shifts, as well as in [0213] teaches changing the layout of the forward area by real-time replenishments of the correct SKUs in the naturally emptied slots by picks, as well as in [0217] teaches dynamic warehouse reshuffling can be performed by utilizing empty slots in a real-time process to replenish the correct items in the correct locations; see also: [0087, 0096, 0219, 0313]).
Regarding claim 16, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
Jahani further teaches further comprising: applying a machine learning model to predict the impact of the slotting strategy on future operational efficiency and cost savings ([0089] teaches the optimal solution of the dynamic slotting model is optimized in terms of both total cost of picking and replenishment and the forward area assignment and allocation, as well as in [0119-0120] teach proposing an algorithm for profiling and slotting optimization simultaneously including determining the best size of each pick mode within the forward area, as well as different rack configurations, wherein a neural network model is proposed for pattern recognition of different types of demand trends, wherein [0121] teaches the picking and replenishment costs of the SKUs selected for the forward area can be reduced in multi-mode forward areas, wherein while the number of cases stored in some pick modes can be more flexible, others have a fixed storage capacity, wherein the number of slots given to the SKUs in the carton flow rack are optimized by the allocation problem, wherein [0122] teaches selecting the best types of racks and their effective size along with the best assignment of SKUs to the pick mode within the forward area, wherein [0260] teaches running the dynamic model utilizing forecasted demand data and pick data, wherein the updated forecast data includes forecast data for a period of time, wherein the planning can involve calculating the total costs during the relevant time window, which is the planning horizon, wherein [0202] teaches the time window for analysis includes considering the seasonality of the products over the planning horizon when determining storage locations, wherein seasonal products are fast movers in their active period and inactive during the rest of the planning horizon, wherein [0088] teaches dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics, wherein the layout of the picking area can be updated over time with replenishment of appropriate SKUs that can periodically re-slot the forward area to reach a predefined target map, wherein the slotting methodology considers seasonality demand shifts, as well as in [0213] teaches changing the layout of the forward area by real-time replenishments of the correct SKUs in the naturally emptied slots by picks, as well as in [0217] teaches dynamic warehouse reshuffling can be performed by utilizing empty slots in a real-time process to replenish the correct items in the correct locations; see also: [0087, 0096, 0219, 0313]).
Claim(s) 2 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Jahani et al. (US 20180253680 A1) in view of Mendes (“Slotting Strategy in a Pharmaceutical Wholesaler Warehouse,” 2021) in view of Liu (“Order Picking Method for Multi-entity Cooperation in Picking Warehouses,” August 5, 2023.) in view of Krishnaswamy et al. (US 20220198565 A1).
Regarding claims 2 and 18, the combination of Jahani, Mendes, and Liu teaches all the limitations of claims 1 and 17 above.
However, Jahani does not explicitly teach wherein generating a digital twin of the facility comprises: constructing a three-dimensional model of the facility using input from at least one three-dimensional scanning device.
From the same or similar field of endeavor, Krishnaswamy teaches wherein generating a digital twin of the facility comprises: constructing a three-dimensional model of the facility using input from at least one three-dimensional scanning device ([0162] teaches a first lidar sensor unit installed near an area corresponding to an automated storage and retrieval system, wherein another Lidar may be installed in another area, wherein the sensor units of the lidar are capable of capturing a data stream, such as a 3D data stream, representative of a 3D scan of that area where the lidar sensor unit is located, wherein the data stream is used by the vision system to monitor the articles, machines, and workers present at the material handling system, as well as in [0168] teaches the replenishment area includes one or more lidar based sensors associated with the replenishment area to perform a 3D scan that captures activities, operations, devices, and workers in the replenishment area, wherein the vision systems employ lidar based sensors to record activities related to the operators, wherein [0122] teaches visualizing the portfolio of assets using a three-dimensional model of the asset, wherein [0044] teaches generating digital twins of assets in order to generate actionable insights, wherein [0050] teaches the models can be utilized for inventory management including slotting; see also: [0167, 0173, 0228]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, and Liu to incorporate the teachings of Krishnaswamy to include wherein generating a digital twin of the facility comprises: constructing a three-dimensional model of the facility using input from at least one three-dimensional scanning device. One would have been motivated to do so in order to utilize digital twins to translate status inputs into actionable insights (Krishnaswamy, [0044]). By incorporating the teachings of Krishnaswamy, one would have been able to monitor workers and articles present in various sections of the material handling system by capturing a 3D data stream (Krishnaswamy, [0162]).
Claim(s) 3-4 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jahani et al. (US 20180253680 A1) in view of Mendes (“Slotting Strategy in a Pharmaceutical Wholesaler Warehouse,” 2021) in view of Liu (“Order Picking Method for Multi-entity Cooperation in Picking Warehouses,” August 5, 2023.) in view of Krishnaswamy et al. (US 20220198565 A1) in view of Haley et al. (US 20240046204 A1).
Regarding claims 3 and 19, the combination of Jahani, Mendes, Liu, and Krishnaswamy teaches all the limitations of claims 2 and 18 above.
However, Jahani does not explicitly teach wherein generating the digital twin of the facility further comprises: capturing real-time data from the facility using sensors, the real-time data representing at least one of changes in a layout inventory level, or operational status of the facility; and updating the digital twin in real-time based on changes detected in the facility based on the real-time data captured from the sensors.
From the same or similar field of endeavor, Haley teaches wherein generating the digital twin of the facility further comprises: capturing real-time data from the facility using sensors, the real-time data representing at least one of changes in a layout inventory level, or operational status of the facility ([0047] teaches the machine learning solutions include a warehouse simulation or digital twin 106, wherein the high performance 3D simulator represents the warehouses, manages order generation and allocation, as well as automated vehicle control systems that navigate micro-agents throughout the simulated warehouse, wherein [0040] teaches the system controller can monitor the different areas and resources in the real-time and can utilize intelligent algorithms that have been tuned to the operational conditions of the warehouse/facility, wherein [0044] teaches the controller of a flexible order fulfillment system uses reinforcement learning to control different types of agents in the warehouse facility and optimizes various objectives of the warehouse facility, wherein the controller adapts varying operating conditions and makes ongoing control and communication, and coordinates decisions amongst various systems and resources engaged in supporting the order fulfillment objective, wherein artificial intelligence and machine learning techniques can be used to retrain or tune and update the agent algorithms to react and adapt quickly to changes and keep the facility running at peak performance, wherein the training system includes reinforcement learning, wherein data can be received including simulation data from a digital twin and live operational data from the fulfillment facility itself, as well as synthesized operational data from GANs, wherein [0046] teaches the training module retrieves operational data, synthetic data, and simulation data from storage to retrain AI algorithms, wherein the data can be used to retrain the AI algorithm, wherein the updated neural network weights are copied again to the controller and the process of monitoring loops again; see also: [0045, 0073]);
and updating the digital twin in real-time based on changes detected in the facility based on the real-time data captured from the sensors ([0047] teaches the machine learning solutions include a warehouse simulation or digital twin 106, wherein the high performance 3D simulator represents the warehouses, manages order generation and allocation, as well as automated vehicle control systems that navigate micro-agents throughout the simulated warehouse, wherein [0040] teaches the system controller can monitor the different areas and resources in the real-time and can utilize intelligent algorithms that have been tuned to the operational conditions of the warehouse/facility, wherein [0044] teaches the controller of a flexible order fulfillment system uses reinforcement learning to control different types of agents in the warehouse facility and optimizes various objectives of the warehouse facility, wherein the controller adapts varying operating conditions and makes ongoing control and communication, and coordinates decisions amongst various systems and resources engaged in supporting the order fulfillment objective, wherein artificial intelligence and machine learning techniques can be used to retrain or tune and update the agent algorithms to react and adapt quickly to changes and keep the facility running at peak performance, wherein the training system includes reinforcement learning, wherein data can be received including simulation data from a digital twin and live operational data from the fulfillment facility itself, as well as synthesized operational data from GANs, wherein [0046] teaches the training module retrieves operational data, synthetic data, and simulation data from storage to retrain AI algorithms, wherein the data can be used to retrain the AI algorithm, wherein the updated neural network weights are copied again to the controller and the process of monitoring loops again; see also: [0045, 0073]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, Liu, and Krishnaswamy to incorporate the teachings of Haley to include wherein generating the digital twin of the facility further comprises: capturing real-time data from the facility using sensors, the real-time data representing at least one of changes in a layout inventory level, or operational status of the facility; and updating the digital twin in real-time based on changes detected in the facility based on the real-time data captured from the sensors. One would have been motivated to do so in order to generate an optimal order fulfillment operation using the simulation data of a digital twin and live operational data from the fulfillment facility (Haley, [0044]). By incorporating the teachings of Haley, one would have been able to adaptively control fulfillment activities by retraining a plurality of micro algorithms for optimal operation of the warehouse (Haley, [0006]).
Regarding claims 4 and 20, the combination of Jahani, Mendes, Liu, and Krishnaswamy teaches all the limitations of claims 2 and 19 above.
However, Jahani does not explicitly teach wherein generating the digital twin of the facility further comprises: applying machine learning to refine the digital twin based on discrepancies between predicted and actual facility operations.
From the same or similar field of endeavor, Haley teaches wherein generating the digital twin of the facility further comprises: applying machine learning to refine the digital twin based on discrepancies between predicted and actual facility operations ([0044] teaches the controller of a flexible order fulfillment system uses reinforcement learning to control different types of agents in the warehouse facility and optimizes various objectives of the warehouse facility, wherein the controller adapts varying operating conditions and makes ongoing control and communication, and coordinates decisions amongst various systems and resources engaged in supporting the order fulfillment objective, wherein artificial intelligence and machine learning techniques can be used to retrain or tune and update the agent algorithms to react and adapt quickly to changes and keep the facility running at peak performance, wherein the training system includes reinforcement learning, wherein data can be received including simulation data from a digital twin and live operational data from the fulfillment facility itself, as well as synthesized operational data from GANs, as well as in [0045] teaches the training module trains the AI algorithm based on the digital twin system simulations, wherein [0046] teaches the training module retrieves operational data, synthetic data, and simulation data from storage to retrain AI algorithms, wherein the data can be used to retrain the AI algorithm, wherein the updated neural network weights are copied again to the controller and the process of monitoring loops again, as well as in [0074] teaches identifying subtle differences between the simulated training environment and real-world environment that lead to sub-optimal performance in order to utilize match RL methods to incorporate data collected from real world operations to help close the simulation-to-reality gap, and wherein [0073] teaches the digital twin technology, when married with an RL training framework, can be used for simulation for on-policy exploration and learning, wherein the on-policy updates can be performed and new data can then be collected for the updated policy; see also: [0045, 0073]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, Liu, and Krishnaswamy to incorporate the teachings of Haley to include wherein generating the digital twin of the facility further comprises: applying machine learning to refine the digital twin based on discrepancies between predicted and actual facility operations. One would have been motivated to do so in order to generate an optimal order fulfillment operation using the simulation data of a digital twin and live operational data from the fulfillment facility (Haley, [0044]). By incorporating the teachings of Haley, one would have been able to adaptively control fulfillment activities by retraining a plurality of micro algorithms for optimal operation of the warehouse (Haley, [0006]).
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jahani et al. (US 20180253680 A1) in view of Mendes (“Slotting Strategy in a Pharmaceutical Wholesaler Warehouse,” 2021) in view of Liu (“Order Picking Method for Multi-entity Cooperation in Picking Warehouses,” August 5, 2023.) in view of Weidman et al. (US 10540682 B1).
Regarding claim 9, the combination of Jahani, Mendes, and Liu teaches all the limitations of claim 1 above.
However, Jahani does not explicitly teach wherein generating the product affinities includes: analyzing transaction records to identify frequently co-purchased items; and determining strengths of relationships between co-purchased items based on frequency and recency of purchases.
From the same or similar field of endeavor, Weidman teaches wherein generating the product affinities includes: analyzing transaction records to identify frequently co-purchased items (Col 4 lines 17-45 teach measuring the cannibalization impact by analyzing the impact and cannibalized impact including the items with strong co-selling relationships, a full picture of the promotion can be obtained, wherein the item-level results can be combined into custom grouping that allows for further insights, wherein Col 6 lines 57-64 teach the algorithm can find high-affinity items to the promoted items, wherein the affinity measures the co-selling relationship between two items, wherein affinity can be measured based on frequency and attach rate, wherein frequency is the percentage of all purchases containing item A and the attach rate is a percentage of the purchases containing product B that also contain product A, wherein the affinity can be a ratio of the attach rate/frequency, wherein Col 5 line 57 to Col 6 line 2 teach the database includes historical data regarding sales, promotions, pricing, and more; see also: Col 7 lines 14-32);
and determining strengths of relationships between co-purchased items based on frequency and recency of purchases (Col 4 lines 17-45 teach measuring the cannibalization impact by analyzing the impact and cannibalized impact including the items with strong co-selling relationships, a full picture of the promotion can be obtained, wherein the item-level results can be combined into custom grouping that allows for further insights, wherein Col 6 lines 57-64 teach the algorithm can find high-affinity items to the promoted items, wherein the affinity measures the co-selling relationship between two items, wherein affinity can be measured based on frequency and attach rate, wherein frequency is the percentage of all purchases containing item A and the attach rate is a percentage of the purchases containing product B that also contain product A, wherein the affinity can be a ratio of the attach rate/frequency, wherein Col 5 line 57 to Col 6 line 2 teach the database includes historical data regarding sales, promotions, pricing, and more; see also: Col 7 lines 14-32).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Jahani, Mendes, and Liu to incorporate the teachings of Weidman to include wherein generating the product affinities includes: analyzing transaction records to identify frequently co-purchased items; and determining strengths of relationships between co-purchased items based on frequency and recency of purchases. One would have been motivated to do so in order to increase the sales for an item by identifying the most significant high affinity products (Weidman, Col 10 line 50 to Col 11 line 13). By incorporating the teachings of Weidman, one would have been able to better assess the impact of a promoted item based upon the promoted item and the product with high affinity to the promoted item (Weidman, Col 9 lines 35-57).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Schneider et al. (US 20230128899 A1) discloses generating a digital twin of the entire production process in order to optimize the cost function based on simulations
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625