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 .
Status of Claims
Applicant’s submission filed 10/10/25 has been entered. Claims : 1-44, 46, 49, 53, 71, 74, 76, 84
are cancelled. Claims 45, 47, 48, 50-52, 54-70, 72, 73, 75, 77-83 are presented for examination.
Claim Rejections - 35 USC § 112
The amendment to claim 47 has been considered. The rejection under 35 U.S.C. 112(a) is withdrawn.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a simulation module, configured to”, “a determination module configured to”, in claim 70.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
STEP 1
Are the claims directed to a process, machine, manufacture or composition of matter?
Claims 45, 47, 48, 50-52, 54-70, 72, 73, 75, 77-83 are all directed to a statutory category (e.g., a process, machine, manufacture, or composition of matter). The answer is YES.
STEP 2A. Prong 1
The claims disclose the abstract idea of determining operating conditions of an intralogistics system.
Exemplary claim 45 recites the following abstract concepts that are found to include “abstract idea”:
“A computer-implemented method for determining operating conditions of an intralogistics system (10), wherein the intralogistics system comprises a simulation module, a centralized process data storage, a determination module, a real system, and a plurality of components, having hardware components and software components, wherein each component comprises component configuration parameters, component properties and/or interaction properties, the method comprising the steps of:
--simulating the intralogistics system with the simulation module based on the component configuration parameters, component properties and/or interaction properties;
--generating simulation process data as process data corresponding to the component configuration parameters, component properties and/or interaction properties with the simulation module and storing the generated process data in the centralized process data storage;
--determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system with the determination module; and -- stored process data in the centralized process data storage, wherein the determination module comprises a data processing unit configured to determine the modified component configuration parameters, component properties and/or interaction properties by analyzing the process data of the intralogistics system and to optimize a predefined target variable of the intralogistics system, wherein the determination of the modified component configuration parameters, component properties and/or interaction properties is based on the optimization of the predefined target variable of the intralogistics system, wherein the predefined target variable is related to a cycle time, a system performance, a peak performance, a system throughput, costs, an energy consumption, an order fulfillment rate and/or component failures; and
--operating a real system of the intralogistics system based on the modified component configuration parameters, component properties and/or interaction properties, using the hardware components and the software components; and
--repeating the steps of simulating, generating, and determining;
--wherein the step of generating process data comprises generating real system process data corresponding to the modified component configuration parameters, component properties and/or interaction properties with the real system.;
--wherein generating the real system process data comprises measuring the real system process data in the real system using a sensor system having a plurality of sensors; storing the measured real system process data in the centralized process data storage; receiving the measured real system process data by the determination module; and
--wherein the step of determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system comprises analyzing the real system process data of the intralogistics system to acquire modified component configuration parameters, component properties and/or interaction properties of the intralogistics system. “
The remaining limitations are no more than computer elements (i.e., a simulation module, a determination modules) to be used as a tool to perform this abstract idea.
The recited limitations cover a process that, under its broadest reasonable interpretation, covers subject matter viewed as a certain method of organizing human activity with the additional recitation of generic computer components. For example, but for the “by a determining module” language, “determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system”, in the context of this claim encompasses the user determining the change/modification.
The practice of simulating/testing data, as well as determining modification of data is a commercial or legal interaction long prevalent in our system of commerce. The claims recite the idea of performing various conceptual steps generically resulting in the determining operating conditions of an intralogistics system. As determined earlier, none of these steps recites specific technological implementation details, but instead get to this result by testing and determining data. Thus, the claims are directed to a certain method of organizing human activity
STEP 2A, Prong 2
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
The claim recites one additional element: that sensors are used to measure process data (i.e. claim 45); and training an artificial neural network with simulation process data to determine modified component data (claims 66, 67).
The sensors and the AI neural network in the steps are recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea.
STEP 2B
The next issue is whether the claims provide an inventive concept because the additional elements recited in the claims provide significantly more than the recited judicial exception. Taking the claim elements separately, the function performed by the sensors and the AI neural network at each step of the process is purely conventional. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using sensors to collect data as well as training AI neural network to determine modification amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Considered as an ordered combination, the computer components of Applicants' claims add nothing that is not already present when the steps are considered separately. The claimed invention does not focus on an improvement in computers as tools, but rather certain independently abstract ideas that use computers as tools. {Elec. Power, 830 F.3d at 1354). (Step 2B: NO).
There is no indication that indication that the sensors or the AI neural network is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. Court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Independent claim 70, 83, 84 recite similar limitations as claim 1 and are therefore rejected under the same rationale.
The other dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The claims provide minimal technical structure or components for further consideration either individually or as ordered combinations with the independent claims. As such, additional recited limitations in the dependent claims only refine the identified abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
See MPEP 2106.05(d)(II) The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350,1355,112 USPQ2d 1093,1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hoteis.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306,1334,115 USPQ2d 1681,1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363,115 USPQ2d at 1092-93.
The claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 45, 47, 50-52, 54-70, 72, 75, 77-83 are rejected under 35 U.S.C. 103 as being unpatentable over Eckman et al. (US 20180300435 A1), and further in view of CELLA et al. (US 20210133670 A1).
Re-claim 45, Eckman et al. t teach A computer-implemented method for determining operating conditions of an intralogistics system, wherein the intralogistics system comprises a simulation module, a centralized process data storage, a determination module, a real system, and a plurality of components, having hardware components and software components, wherein each component comprises component configuration parameters, component properties and/or interaction properties, the method comprising the steps of:
(see e.g. [0034] The system 100 also includes a Warehouse Management System 114 (also referred to as “WMS”) that is a specialized computer system to manage storage and retrieval of inventory at the facility, and to interface with devices and components within the facility, such as automated components within the automated warehouse.
[0059] The warehouse automation model can be a computer-based model that represents the component characteristics, the functional relationship among the components, and the physical relationship among the components within the warehouse. Other details can also be represented in the warehouse automation model. The warehouse automation model can be used to simulate different components of the automated warehouse design transporting pallets within the warehouse.)
--simulating the intralogistics system with the simulation module based on the component configuration parameters, component properties and/or interaction properties;
(see e.g. [0059] The warehouse automation model can be used to simulate different components of the automated warehouse design transporting pallets within the warehouse.
[0030] Simulations can be repeatedly performed while varying parts of the simulation, such as varying features that are part of the warehouse automation design, varying the dimensions and/or layout of the warehouse itself (presuming that the warehouse has not yet been constructed), and/or varying the inventory demands on the warehouse.)
--generating simulation process data as process data corresponding to the component configuration parameters, component properties and/or interaction properties with the simulation module and storing the generated process data in the centralized process data storage;
(see e.g. [0060, 0061] Machine parameters and historical inventory data can be obtained (406). For example, parameters for the automation components can be used to determine how the automation components behave and operate within the warehouse, such as information identifying acceleration, max speed, delays, capacity, and/or other details. The machine parameters may be included in and/or added to the warehouse automation model, or may be referenced by identifiers for the machines in the warehouse automation model.
[0062] Using the warehouse automation model, the machine parameters, and the historical inventory data use of the automated warehouse design can be simulated for the historical inventory data (408).
--determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system with the determination module; and -- stored process data in the centralized process data storage,
(see e.g. [0042] (F, 130) determining modification to the automation features that are included in the automation design (e.g., increasing/reducing number of conveyors, reducing/increasing size of dock, reducing/increasing number of doors, changing orientation/alignment of components) and repeating the simulation exercise (A-D), as necessary;
[0030] Simulations can be used to make a variety of determinations, such as whether a current automated warehouse design will be suitable for expected inventory demands for the warehouse and/or whether a modification to the inventory demands can be accommodated by the automated warehouse with less than a threshold degradation in performance.
[0057] As depicted in this example, the distribution of pallets at this facility changes over time. For example, at a first time depicted in the graph 350, the facility is storing a large number of tall pallets (around the 80″-85″ bucket) as indicated by the spike 352 and a smaller number of shorter pallets (around the 55″-60″ bucket) as indicated by the smaller spike 354. However, these distributions change so that there is nearly the same number of the tall pallets as there are of the shorter pallets at a last time depicted in the graph 350, as indicated by the spike 356 (around the 80″-85″ bucket) being at nearly the same level as the spike 358 (around the 55″-60″ bucket). The systems, devices, and techniques described throughout this document can account for such volatility and uncertainty represented in graphs 300 and 350, by identifying automated warehouse designs that will provide optimal solutions across the broad spectrum of variation could be encountered.)
wherein the determination module comprises a data processing unit configured to determine the modified component configuration parameters, component properties and/or interaction properties by analyzing the process data of the intralogistics system and to optimize a predefined target variable of the intralogistics system, wherein the determination of the modified component configuration parameters, component properties and/or interaction properties is based on the optimization of the predefined target variable of the intralogistics system,
(see e.g. [0042] (F, 130) determining modification to the automation features that are included in the automation design (e.g., increasing/reducing number of conveyors, reducing/increasing size of dock, reducing/increasing number of doors, changing orientation/alignment of components) and repeating the simulation exercise (A-D), as necessary; and
[0011] FIG. 1 depicts an example system for optimizing an example warehouse rack.
[0036] the example optimized automation design 132 is depicted in which multiple redundant conveyors, which can be determined to be the optimal configuration for the specific storage facility, its physical parameters, and its specific usage. The computer system 104 can determine customized optimizations storage facilities and may adjust them over time as the use of facilities changes over time (e.g., different products are stored at the facilities, different companies/client are using the storage facility).
wherein the predefined target variable is related to a cycle time, a system performance, a peak performance, a system throughput, costs, an energy consumption, an order fulfillment rate and/or component failures; and
(see e.g. [0011] FIG. 1 depicts an example system for optimizing an example warehouse rack.
[0005] Such warehouse simulations can be repeatedly run on different warehouse automation designs to identify an optimal warehouse automation design that will, for example, maximize the efficiency of the warehouse by minimizing pallet place and pull times, minimizing truck load/unload times, and minimizing/eliminating failures during which the warehouse is not able to meet threshold performance metrics.
[0048] Regarding truck performance, the amount of time that trucks 201 have to wait to be docked at the doors/bays 202 is a first performance factor that can be analyzed.
[0054] The systems, devices, and techniques described throughout this document are specifically configured to identify automated warehouse designs that will provide optimal automated solutions over time and across a wide variety of inventory conditions, including accommodating historic/expected spikes in inventory volume.)
--operating a real system of the intralogistics system based on the modified component configuration parameters, component properties and/or interaction properties, using the hardware components and the software components; and
(see e.g. [0063] If no more designs are to be tested (e.g., current design under test met performance metrics, universe of available options has been exhausted, current design performed better than other designs), then a warehouse automation design can be selected to use for the warehouse based on the simulation results (420). For example, a warehouse automation design with a.sub.— best simulation result (e.g., most efficient) can be selected.
[0077] The table below details the example historical inventory data (“real data”) that is used to drive the simulation and the example actions that are simulated, such as those described above with regard to FIG. 8: TABLE-US-00001 TABLE 1 Simulated Actions From Real Data: (Listed major actions, but not limited to): Trucks time into the facility The time the pallet gets into the facility Trucks ID's The time for pallets on a conveyor or other mechanical equipment The amount of pallets in the Time within the storage area truck The pallets SKUs The time it was requested (as given by outbound truck time) Inbound or Outbound Time it took to get out of the facility The time the truck pulled out of the dock).
--repeating the steps of simulating, generating, and determining;
(see e.g. [0042] (F, 130) determining modification to the automation features that are included in the automation design (e.g., increasing/reducing number of conveyors, reducing/increasing size of dock, reducing/increasing number of doors, changing orientation/alignment of components) and repeating the simulation exercise (A-D), as necessary;
--wherein the step of generating process data comprises generating real system process data corresponding to the modified component configuration parameters, component properties and/or interaction properties with the real system.;
(see e.g. [0036 -0043] The system 100 can optimize the automated warehouse design 102 (to be have an optimized design 132) over a variety of different steps to identify optimized automation features and to efficiently use the optimized features. ---an optimal automation design has been identified for the specific warehouse, the specific automation components available for inclusion in the warehouse, the specific combination of historic/expected inventory for the warehouse, and/or the layout/dimensions of the facility.)
--wherein the step of determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system comprises analyzing the real system process data of the intralogistics system to acquire modified component configuration parameters, component properties and/or interaction properties of the intralogistics system.
(see e.g. [0048-0052] Regarding truck performance, the amount of time that trucks 201 have to wait to be docked at the doors/bays 202 is a first performance factor that can be analyzed. For example, if all of the doors/bays 202 are occupied when a truck 201 arrives at the warehouse 200, that truck will have to wait until one of the doors/bays 202 opens up. This waiting is an inefficiency that the simulation can identify. The waiting can be indicated, for example, by a first record indicating a time when the truck 201 was at the warehouse 200 and ready to be docked (a first example checkpoint) and a second record indicating a later time when the truck 201 was able to be docked (a second example checkpoint). The difference between these two times is the wait time for the truck 201.)
Eckman et al. do not teach the limitations as claimed
However, CELLA et al. teach ----wherein generating the real system process data comprises measuring the real system process data in the real system using a sensor system having a plurality of sensors; storing the measured real system process data in the centralized process data storage; receiving the measured real system process data by the determination module;
(see e.g. [0051] In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.
[0059] and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.
[0018] In embodiments, the unified set of robotic process automation systems automate a process selected from the group consisting of -----ordering of equipment for a warehouse, ordering of equipment for a fulfillment center, classification of a product defect in an image, inspection of a product in an image, inspection of product quality data from a set of sensors).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Eckman et al., and include the steps cited above, as taught by Cella et al., in order to automate the process of monitoring the facilities (see e.g. [0018], [0025]).
Re-claim 47, Eckman et al. teach the method according to claim 45, wherein the hardware components comprise: components for storing of goods; components for order fulfillment; components for automated transporting of goods between the components for storing of goods and the components for order fulfillment; and at least one automated storage and retrieval system for transferring goods between the components for storing of goods and the components for automated transporting of goods;
(see e.g. [0002] This document generally describes technology for optimizing the design and use of automated warehouses for storing pallets and other physical goods.
[0009] Such implementations can optionally include one or more of the following features. The automation components can include conveyor belts to transport pallets from a dock to racks in the warehouse, cranes to carry pallets to different rack levels, and carts to transport pallets across rack openings on a same rack level. For each of the automation components, the movement parameters can include a rate of acceleration, a maximum velocity, a receiving delay, a process delay, and an eject delay.)
And wherein the software components comprise: a warehouse management system; and/or a material flow controller.
(see e.g. [0009] Such a system can further include a warehouse management system that is programmed to perform the following operations to direct the automation components on the placement of a pallets in the storage racks.)
Re-claim 50, Eckman et al. teach -the method according to claim 45, wherein the method further comprises a step of: providing an input/output unit configured to receive inputs from an operator defining the target variable.
(see e.g. [0130] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
[0115, 0128- to display graphical information for a GUI on an external input/output device, such as display 1816 coupled to high speed interface 1808. ---- at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.)
Re-claim 51, Eckman et al. teach -the method according to claim 45, wherein the method further comprises a step of: recommending adjusting configuration parameters, component properties and/or interaction properties of the plurality of components, according to the modified component configuration parameters, component properties and/or interaction properties.
(see e.g. [0063] The results for the current warehouse automation design can be evaluated (414), such as being compared against target performance metrics and/or being compared against the performance of alternate warehouse automation designs that have been simulated. Based on the evaluation, a determination can be made as to whether more designs should be tested (416). If more designs are to be tested (e.g., the current design under test did not meet performance metrics and/or did not perform better than other designs), then the design can be modified (418) and fed back into the technique 400 for another simulation.).
Re-claim 52, Eckman et al. teach -the method according to claim 45, wherein analyzing process data comprises a processing of simulation process data of the simulation module and/or virtual commissioning process data of the virtual commissioning module and/or real-system process data from the real system.
(see e.g. [0109] The analysis can be used to compare the simulation performance against one or more performance metrics
[0025] FIG. 15 is a flowchart of an example technique for analyzing the simulation data and determining simulation results.
[0062] Using the recorded data, the simulation data can be analyzed and used to determine results for the current warehouse automation design (412). For example, the simulation data can be used to identify time intervals for the place and pick operations and to determine the overall performance of the warehouse automation design.)
Re-claims 54, 55, Eckman et al. anticipates a unified process data format (see e.g. [0074] the data can be recorded in any of a variety of formats), but do not explicitly teach the limitations as claimed.
However Cella et al. teach --The method according to claim 53, wherein, before storing process data into the process data storage, the method comprises a step of converting the process data to a unified process data format.
(see e.g. [0541] The machine learning model 3000 may, for example, receive state data 1140 and event data 1034 related to a particular value chain entity 652 of the plurality of value chain entities 652 and perform a series of operations on the state data 1140 and the event data 1034 to format the state data 1140 and the event data 1034 into a format suitable for use by the digital twin system 1700 in creation of a digital replica of the value chain entity 652).
The method according to claim 53, wherein storing of process data comprises providing the process data in a domain data model.
(see e.g. [1095] . In embodiments, an artificial intelligence system may be trained, such as on a labeled industry-specific or domain-specific data set, to automatically generate an industry-specific or domain-specific digital twin for an instance of an EMP for an organization, with default configuration of data types, entities, features and granularities for various roles within an organization of that industry or domain. The defaults can then be reconfigured in a user interface of an authorized user to reflect company-specific variations from the industry-specific or domain-specific defaults.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Eckman et al., and include the steps cited above, as taught by Cella et al., in order to process and structure data into a format that can be consumed by an enterprise digital twin.
(see e.g. [1144]).
Re-claim 56, Eckman et al. teach -the method according to claim 45, wherein, after determining modified component configuration parameters, component properties and/or interaction properties, the method comprises a step of storing component configuration parameters in a configuration database and/or storing of component properties and/or interaction properties in a component property database.
(see e.g. 0068] The system 500 includes a variety of additional databases 514-520 to aid the components 502-506 and 522 in performing their respective operations. The equipment parameters 514 stores parameters for automation equipment (e.g., machines) that are available for incorporation into the automated warehouse design. The historical inventory data 516 stores historical and/or projected inbound and outbound trucks with pallets for the warehouse. The warehouse automation designs 518 stores warehouse automation designs that have and/or will be simulated and tested by the system 500. The simulation results 520 stores the results for the simulations so that they can be compared against each other and against one or more metrics to aid in identifying an optimal solution.)
Re-claim 57, Eckman et al. teach -the method according to claim 56, wherein the method further comprises a step of: subsequently simulating the intralogistics system based on parameters and properties comprising component configuration parameters stored in the configuration database and/or component properties and/or interaction properties stored in the component property database (80).
(see e.g. [0005] For example, warehouse automation can be simulated for the purpose of determining an optimal warehouse automation design given a variety of parameters that are specific to the warehouse, such as the expected customer inventory demands over time, the layout of the warehouse, and/or the specific automation features (e.g., machines) that are possible within the warehouse.
[0062] Using the warehouse automation model, the machine parameters, and the historical inventory data use of the automated warehouse design can be simulated for the historical inventory data (408). For example, the historical inventory data can be sequentially simulated as inputs for the automated warehouse design under test, and the warehouse automation model and machine parameters can be used to simulate the transportation and storage of the historical inventory in the automated warehouse design.
[0068] The system 500 includes a variety of additional databases 514-520 to aid the components 502-506 and 522 in performing their respective operations. The equipment parameters 514 stores parameters for automation equipment (e.g., machines) that are available for incorporation into the automated warehouse design.)
Re-claims 58, 59, Eckman et al. teach -the method according to claim 56, wherein the method further comprises a step of: setting component configuration parameters stored in the configuration database and/or component properties and/or interaction properties stored in the component property database of a component of the plurality of components to assumed component configuration parameters, assumed component properties and/or assumed interaction properties, when said component is altered.
Claim 59 --The method according to claim 45, wherein the step of simulating the intralogistics system (10) is furthermore based on assumed component configuration parameters, assumed component properties and/or assumed interaction properties and/or assumed logistics parameters.
(see e.g. [0006] Such warehouse simulations can be repeatedly run with changes to the new/modified customer profile at each iteration to determine whether and what type of adjustment to the new/modified customer storage profile would provide optimal performance for the automated warehouse.
[0009 generating a revised warehouse automation model for the revised automation design; repeating the chronological simulation, detecting, recording, and assessing with the revised warehouse automation model and the same historical inventory data;
[0042] (F, 130) determining modification to the automation features that are included in the automation design (e.g., increasing/reducing number of conveyors, reducing/increasing size of dock, reducing/increasing number of doors, changing orientation/alignment of components) and repeating the simulation exercise (A-D), as necessary)
Re-claim 60, Eckman et al. teach -the method according to claim 45, wherein the step of simulating the intralogistics system is further based on logistics parameters (86) derived from logistics process data of the real system.
(see e.g. [0077] The table below details the example historical inventory data (“real data”) that is used to drive the simulation and the example actions that are simulated, such as those described above with regard to FIG. 8:
TABLE-US-00001 TABLE 1 Simulated Actions From Real Data: (Listed major actions, but not limited to): Trucks time into the facility The time the pallet gets into the facility Trucks ID's The time for pallets on a conveyor or other mechanical equipment The amount of pallets in the Time within the storage area truck The pallets SKUs The time it was requested (as given by outbound truck time) Inbound or Outbound Time it took to get out of the facility The time the truck pulled out of the dock).
Re-claim 61, Eckman et al. teach -the method) according to claim 45, wherein, after the step of determining the modified component configuration parameters, component properties and/or interaction properties of the intralogistics system, the method comprises a step of subsequently simulating the intralogistics system based on the previously determined modified component configuration parameters, component properties and/or interaction properties.
(see e.g. [0060] Machine parameters and historical inventory data can be obtained (406). For example, parameters for the automation components can be used to determine how the automation components behave and operate within the warehouse, such as information identifying acceleration, max speed, delays, capacity, and/or other details. The machine parameters may be included in and/or added to the warehouse automation model, or may be referenced by identifiers for the machines in the warehouse automation model.
[0061] The historical inventory data can be, for example, historical inbound and outbound truckloads of pallets, including information identifying pallets within each truck. )
Re-claim 62, Eckman et al. teach -the method according to claim 61, wherein, following the step of subsequently simulating the intralogistics system, the method comprises a step of generating subsequent process data corresponding to the previously determined modified component configuration parameters with the simulation module.
(see e.g. [0061] Additionally and/or alternatively, the historical inventory data can include predictive inventory data that estimates expected inbound and outbound truckloads at various times in the future. Such predictive inventory data can be generated, for example, by analyzing historical inventory data (e.g., machine learning techniques) to identify trends and expected future behavior.
[0062] Using the warehouse automation model, the machine parameters, and the historical inventory data use of the automated warehouse design can be simulated for the historical inventory data (408). For example, the historical inventory data can be sequentially simulated as inputs for the automated warehouse design under test, and the warehouse automation model and machine parameters can be used to simulate the transportation and storage of the historical inventory in the automated warehouse design.)
Re-claim 63, Eckman et al. teach -the method according to claim 62, wherein the step of determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system comprises: a comparing of simulation process data with virtual commissioning process data and/or real-system process data by the determination module and, based on this comparison; and a selecting one of the simulation process data, virtual commissioning process data or real-system process data with regard to the predefined target variable of the intralogistics system.
(see e.g. [0063] The results for the current warehouse automation design can be evaluated (414), such as being compared against target performance metrics and/or being compared against the performance of alternate warehouse automation designs that have been simulated.
[0068] The simulation results 520 stores the results for the simulations so that they can be compared against each other and against one or more metrics to aid in identifying an optimal solution.)
Re-claim 64, Eckman et al. teach -the method according to claim 63, wherein, after comparing and selecting the process data, the method comprises a step of subsequently simulating of the intralogistics system and generating corresponding subsequent simulation process data based on the component configuration parameters, component properties, interaction properties and/or logistics parameters corresponding to the selected process data.
(see e.g. [0009] In response to determining that the current automation design failed, performing additional operations can include modifying one or more of the automation components in the current automation design to generate a revised automation design for the warehouse; generating a revised warehouse automation model for the revised automation design; repeating the chronological simulation, detecting, recording, and assessing with the revised warehouse automation model and the same historical inventory data).
Re-claim 65, Eckman et al. teach -the method according to claim 45, wherein, before the step of simulating the intralogistics system, the method comprises a step of deriving assumed component configuration parameters and/or assumed interaction properties and/or assumed logistics process data by analyzing process data by the determination module.
(see e.g. [0106] The log of actions for the simulation can be accessed (1502) and can be used to determine a variety of duration information indicating how long the automated warehouse being simulated took to perform various tasks on the historical inventory data being used for the simulation. For example, the log actions can be used to determine: the wait time for each inbound and outbound truck (1504), the load/unload time for each truck (1506), and the timing information for each pallet being placed/pulled in the warehouse (1508), which can include the dock time for each pallet (1510), the conveyor time for each pallet (1512), the crane and/or cart time for each pallet (1514), and the overall pull/place time for each pallet (1516).
Re-claims 66, 67, Eckman et al. do not explicitly teach the limitations as claimed.
However Cella et al. teach -- The method according to claim 45, wherein in the step of determining modified component configuration parameters of the intralogistics system the determination module is based on a trained artificial neural network and/or machine learning. ---The method according to claim 66, wherein the method further comprises the step of training the artificial neural network with simulation process data, virtual commissioning process data, real-system process data and corresponding component configuration parameters, component properties (54) and/or interaction properties.
(see e.g. [0074] an artificial intelligence system configured to input the sensor data into a machine learning model such that the sensor data is used as training data for the machine learning model, and the machine learning model is configured to transform the sensor data into simulation data; and a digital twin system configured to create a digital replica of the set of value chain entities based on the simulation data, wherein the digital replica of the value chain entities is configured to be used to provide a substantially real-time representation of the value chain entities and provide a simulation of a possible future state of the value chain entities via the simulation data.
[0070] In embodiments, a value chain system that provides recommendations for designing a logistics system includes a machine learning system that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems; an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine-learned model and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation and one or more physical asset digital twins of physical assets, wherein the digital twin system: executes a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins, issues a logistics system design request from the artificial intelligence system based on a state of the logistics simulation; and adjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Eckman et al., and include the steps of training the artificial neural network, as taught by Cella et al., in order to replicate the operation of the workforce in the defined set of roles. (see e.g. [0202]).
Re-claim 68, Eckman et al. teach -the method according to claim 45, wherein the method further comprises a step of applying the modified component configuration parameters to the virtual commissioning module and/or the real system.
(see e.g. [0109] For example, to identify suitable modifications, variations in the automated design can be generated (e.g., add conveyor, reconfigure conveyors) and a mini-simulation can be run on a window of time around instances when the performance metric was not met to determine whether the modification will remedy the problem. Such mini-simulations can be performed iteratively and repeatedly on modifications until a suitable modification is identified.
[0063] If more designs are to be tested (e.g., the current design under test did not meet performance metrics and/or did not perform better than other designs), then the design can be modified (418) and fed back into the technique 400 for another simulation.)
Re-claim 69, Eckman et al. anticipate – the method according to claim 45, wherein the process data and/or component configuration parameters, component properties and/or interaction properties comprise component failure and/or component outage data.
(see e.g. [0005] minimizing/eliminating failures during which the warehouse is not able to meet threshold performance metrics.)
Claim 70 recites similar limitations as claim 45 and is therefore rejected under the same arts and rationale.
Furthermore, Cella et al. teach --a centralized process data storage configured to store generated process data of the intralogistics system and configured to allow low latency data access;
(see e.g. [0365] For example, the adaptive intelligence layer 614 may manage and provision available network resources for both a supply chain management application and for a demand planning application (among many other possibilities), such that low latency resources are used for supply chain management application (where rapid decisions may be important) and longer latency resources are used for the demand planning application. )
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Eckman et al., and include the steps of training the artificial neural network, as taught by Cella et al., in order to replicate the operation of the workforce in the defined set of roles. (see e.g. [0202]).
Claim 72 recites similar limitations as claim 47 and is therefore rejected under the same arts and rationale.
Claim 75 recites similar limitations as claim 50 and is therefore rejected under the same arts and rationale.
Claim 77 recites similar limitations as claim 54 and is therefore rejected under the same arts and rationale.
Claim 78 recites similar limitations as claim 56 and is therefore rejected under the same arts and rationale.
Re-claim 79, Eckman et al. teach -the intralogistics system according to claim 78, further comprising an input/output unit configured to display component parameters for a selected component of the plurality of components and/or to receive inputs from an operator defining component parameters for the selected component.
(see e.g. [0130] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
[0115, 0128- to display graphical information for a GUI on an external input/output device, such as display 1816 coupled to high speed interface 1808. ---- at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.)
Re-claim 80, Eckman et al. teach -the intralogistics system according to claim 70, further comprising: a simulation scenario module configured to control the simulation module (40) to perform iterative simulation cycles to generate a plurality of simulation process data (48) sets based on a plurality of component configuration parameters, component properties, logistics parameters and/or interaction properties.
(see e.g. [0005] This document generally describes computer-based technology for simulating warehouse automation designs and evaluating the results of such simulations to inform a variety of decisions. For example, warehouse automation can be simulated for the purpose of determining an optimal warehouse automation design given a variety of parameters that are specific to the warehouse, such as the expected customer inventory demands over time, the layout of the warehouse, and/or the specific automation features (e.g., machines) that are possible within the warehouse. Such warehouse simulations can be repeatedly run on different warehouse automation designs to identify an optimal warehouse automation design that will, for example, maximize the efficiency of the warehouse by minimizing pallet place and pull times, minimizing truck load/unload times, and minimizing/eliminating failures during which the warehouse is not able to meet threshold performance metrics.
[0109] The analysis can be used to compare the simulation performance against one or more performance metrics which, if not met, can indicate that the automated warehouse design is not sufficient for the inventory that is being stress tested on the design----For example, to identify suitable modifications, variations in the automated design can be generated (e.g., add conveyor, reconfigure conveyors) and a mini-simulation can be run on a window of time around instances when the performance metric was not met to determine whether the modification will remedy the problem. Such mini-simulations can be performed iteratively and repeatedly on modifications until a suitable modification is identified.).
Claim 81 recites similar limitations as claim 63 and is therefore rejected under the same arts and rationale.
Claim 82 recites similar limitations as claims 66, 67 and is therefore rejected under the same arts and rationale.
Claim 83 recites similar limitations as claim 45 and is therefore rejected under the same arts and rationale.
Claims 48, 73 are rejected under 35 U.S.C. 103 as being unpatentable over Eckman et al. (US 20180300435 A1), and further in view of McGregor et al. (US 20210141870 A1).
Re-claim 48, Eckman et al. do not teach the limitations as claimed.
However, McGregor et al. teach -- The method according to claim 45, wherein the method further comprises a step of: emulating the intralogistics system with a virtual commissioning module based on the modified component configuration parameters, component properties and/or interaction properties determined by the determination module, wherein the virtual commissioning module is configured to emulate the intralogistics system by using the software components and by simulating the hardware components; and wherein the step of generating process data comprises generating virtual commissioning process data corresponding to the modified component configuration parameters, component properties and/or interaction properties; and
wherein the step of determining modified component configuration parameters, component properties and/or interaction properties of the intralogistics system comprises analyzing the virtual commissioning process data of the intralogistics system to acquire modified component configuration parameters, component properties and/or interaction properties of the intralogistics system.
(see e.g. [0015] FIG. 9 is a diagram illustrating exporting of the enhanced digital model to a control design and testing platform as part of a virtual commissioning procedure.
[0031] Control design platforms 106 may also include a digital simulation platform that emulates execution of the control program against a virtual model of the automation system in order to test the control programming and the mechanical design, a process referred to as virtual commissioning. Such simulation platforms can mimic the behavior of the automation system's mechanical assets in response to execution of the control program on an emulated industrial controller so that proper operation can be verified. During commissioning of the physical system, the completed control code, device configurations, and HMI applications are downloaded to the appropriate field devices 108 of the automation system.
[0068] Marking up the mechanical model 402 with aspect metadata 508 as described above yields an enhanced digital model 502 of the automation system being designed, which can be exported to a separate simulation platform for virtual commissioning. FIG. 9 is a diagram illustrating exporting of the enhanced digital model 502 to a control design and testing system 302 as part of a virtual commissioning procedure.
[0069] In the present example, control design and testing system 302 comprises a controller emulation component 308 that emulates execution of an industrial control program being testing on a virtualized (or emulated) industrial controller, and a simulation component 306 that simulates operation of a virtualized model of an industrial automation system under control of the industrial control program. Within the control design and testing system 302, the enhanced digital model 502 of the automation system—comprising the mechanical model 402 augmented with aspect metadata 508 and master I/O list 702—can be interfaced with control programming (e.g., ladder logic) being developed for the automation system to yield a virtual testing environment that allows both the mechanical and control designs to be virtually simulated and tested before finalizing the overall design and proceeding to the building and installation phase.
[0070] Since the enhanced digital model 502 models mechanical characteristics of the automation system as well as behavioral properties of components that make up the model (by virtue of the aspect metadata 508), the enhanced digital model 502 can be used to simulate expected operation and behavior of the automation system while under control of an emulated control program 1008. This can include viewing and verifying the simulated system's responses to control inputs in terms of movement, speeds, flows, temperatures, fill levels, movement of product through the system, etc. In the example depicted in FIG. 10, controller emulation component 308 of the testing system 302 acts as an industrial controller emulator to execute control program 1008, which is being developed and tested against the virtual model 502 of the automation system created within the CAD system 202.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Eckman et al., and include the steps cited above, as taught by McGregor et al., so that proper operation can be verified (see e.g. [0031])
Claim 73 recites similar limitations as claim 48 and is therefore rejected under the same arts and rationale.
Response to Arguments
Applicant's arguments filed 10/10/25 have been fully considered but they are not persuasive.
Applicant’s argument:
Contrary to the Examiner's position, Applicant's method as set forth in amended claim 45 improves existing technology and represents a technological solution to a technological problem. See MPEP 2106.05(a).
Applicant's method as set forth in amended claim 45 permits operators and engineers to handle the complex technology in intralogistics and flexibly adapt a configuration and settings of an effectively allow performance optimization in a novel and non-obvious way as discussed infra in connection with the prior art cited by the Examiner.
Examiner’s response:
The above paragraphs highlight the problem Applicants seek to solve, which is for determining operating conditions of an intralogistics system. It does use computer automation to solve the problem, but the problem is one of supply chain realm and uses computer technology as a solution, rather than solving a technical computer problem, or improving the performance of the computer itself.” The current invention does not disclose such an improvement in the claims or specification.
Relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, (use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept);
Applicant’s argument:
It is respectfully submitted, however, that no feedback from ongoing operations, e.g. via sensors, is disclosed. No real-time data is used for simulations.
In Eckman et al., modifications to designs are therefore carried out through simulation and design revision rather than through automatic control of the component's operation (in Applicant's amended claim 45 through the modified components). In Eckman et al., optimization is therefore planning-oriented rather than operational.
Examiner’s response:
The Examiner notes Cella et al. teach the sensor system and the feedback from the sensors which is being sent to a digital twin representing value chain network entities.
(see e.g. [0051] In embodiments, the set of monitoring facilities includes a sensor system deployed in an infrastructure facility operated by an enterprise.
[0059] and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.
[0111] The continuous loop of the optimization functional block diagram 800, in practice provides real time feedback and optimization of a procedure. )
Applicant’s argument:
Therefore, Cella et al. does not disclose a real-time adjustment of operational parameters, in particular the determination of modified component configuration parameters, component properties and/or interaction properties of the intralogistics system but develops recommendations for adjustments, see paragraph [0059] of Cella et al. A digital twin of the logistics network is created and decision-making at a high level is enabled (e.g., resource allocation, prioritization of shipments, sensor placement). Hence, a determination of modified component parameters based on a predetermined target parameter is not disclosed in Cella et al.
Examiner’s response:
Based on the following paragraph, Cella et al. clearly teach “a determination of modified component parameters based on a predetermined target parameter”. For example, the machine learning module provides the recommendations based on the determined optimal relationship between prestored data and usage of at least one of the item and the equipment in one or more reference medical procedures The recommendations may include one or more of changing the pattern of use, the sequence of use, and the priority of use of at least one of the item and the equipment during the medical procedure.
(see e.g. [0039] In still other embodiments, the mixed reality device 132 may provide a mixed reality interface in which electronically generated objects are inserted in a direct or indirect view of real-world environments in a manner such that they may co-exist and interact in real time with the real-world environment and real-world objects.
[0104] The machine learning module 812 continually evolves the specifics of execution of a medical procedure in real time with new data inputs. The machine learning intent is to continually implement optimized medical procedure overtime.
[0105] In an embodiment, the machine learning module 812 is configured to analyze the prestored data, in the data module 810, associated with usage of at least one of the item and the equipment in one or more reference medical procedures corresponding to the one or more of the medical practitioner identifier, the medical procedure identifier, and the medical procedure room identifier.
[0119] It will be appreciated that the machine learning module 812, in some embodiments, may analyze the data in the data module 810 prior to a next procedure room set up 802, during a procedure itself to modify a procedure room set up, and/or after a procedure for analytical and training purposes.
[0107] The machine learning module 812 is configured to provide recommendation to the module 802 for optimizing the medical procedure based on the comparison. In accordance with various embodiments, the machine learning module 812 is configured to provide recommendation to the module 802 for optimizing one or more steps of the medical procedure when the acquired data does not correspond to the determined optimal relationship. The machine learning module 812 is configured to provide the recommendations based on the determined optimal relationship. The recommendations may include one or more of changing the pattern of use, the sequence of use, and the priority of use of at least one of the item and the equipment during the medical procedure.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. CAI (20210294930)-- COMPUTER-AIDED WAREHOUSE SPACE PLANNING
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUNA CHAMPAGNE whose telephone number is (571)272-7177. The examiner can normally be reached M-F 8:00-5:00.
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/LUNA CHAMPAGNE/Primary Examiner, Art Unit 3627
January 28, 2026