DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following NON-FINAL Office Action is in response to Applicant’s communication filed 12/12/2024 regarding Application 18/978,761. The following is the first action on the merits.
Priority Acknowledgment
Examiner acknowledges Applicant’s claim to Foreign Application DE 102023134877.9 with priority filing date 12/13/2023.
Status of Claim(s)
Claim(s) 1-16 is/are currently pending and are rejected as follows.
Claim Objections
Applicant is advised that should Claim(s) 3-8 be found allowable, Claim(s) 9-14 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim(s) 9-14 recite the same limitation language as present within Claim(s) 3-8 with the only difference being that of order of dependency, however, despite this the claims would be understood to cover the same thing such that should Claim(s) 3 be found allowable, Claim(s) 9 would be objected to. This is followed by Claim(s) 10 being found to be a substantial duplicate of Claim(s) 4, Claim(s) 11 of Claim(s) 5, Claim(s) 12 of Claim(s) 6, Claim(s) 13 of Claim(s) 7, and Claim(s) 14 of Claim(s) 8, however, for the sake of compact prosecution the claims will be further analyzed below.
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.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 101 because the claimed invention
is/are directed towards a judicial exception (i.e. law of nature, a natural phenomenon, or an
abstract idea) without significantly more.
Claim(s) 1-16 are directed towards an invention for the detecting of at least one piece good information for a piece good handled during a handling process, controlling at least one element according to a control strategy based on the leas one piece of information, transmitting the information to a data center, receiving a new control strategy from the data center, and adapting the control strategy according to the new strategy. These actions fall under a subject
matter grouping which the courts have considered ineligible (Organizing Human Activity,
and Mental Process). These claims do not integrate the abstract idea into a practical application,
and do not include additional elements that provide an inventive concept (are sufficient to
amount to significantly more than the abstract idea).
Under Step 1 of the Alice/Mayo framework it must be considered whether the claims are
directed to one of the four statutory categories of invention. Claim(s) 1-15 are directed towards a method comprising at least one step. Claim(s) 16 is directed towards a product. Accordingly, the
claims fall within the four statutory categories of invention, (method and product) and will be
further analyzed under Step 2 of the Alice/Mayo framework.
Under Step 2, Prong One, of the Alice/Mayo framework it must be considered whether the claims recite any abstract ideas.
Independent claim(s) 1 and 16 recite an invention for the detecting of at least one piece good information for a piece good handled during a handling process, controlling at least one element according to a control strategy based on the leas one piece of information, transmitting the information to a data center, receiving a new control strategy from the data center, and adapting the control strategy according to the new strategy which recite the abstract ideas of Organizing Human Activity and a Mental Process in the following limitations:
detecting or obtaining at least one piece good information of at least one piece good which has been handled by the handling device;
controlling the at least one controllable element according to a control strategy based on the at least one piece of information
transmitting the at least one piece good information to a data center;
determining at least one key performance indicator of the handling process of the handling device based on the at least one piece good information; and
determining a control strategy for the at least one handling device based on the at least one key performance indicator.
Dependent claim(s) 2-15 merely further limit the abstract idea and are subject to the same rationale expressed above.
Under Step 2A, Prong Two, any additional elements are recited
Independent claim(s) 1 and 16 recite:
A computer
A handling device
A control device
A detection device
A transmission device
A receiving device
A controllable element
Dependent claim 15 recites:
A computing unit
These additional elements, considered both individually and as an ordered pair do no more than represent mere instructions to implement the abstract idea ("apply it" compute (See MPEP 2106.05(f)). Additionally, the claims represent insignificant extra solution activity (See MPEP 2106.05(g)). These elements are recited with a high degree of generality, and the specification sets forth the general purpose nature of the technologies required to implement the invention (emphasis added).
Support for this decision can be found in Paragraph(s) [0034]-[0036], and [0047]-[0049] of Applicant’s specification.
Under Step 2B eligibility analysis evaluates whether the claims as a whole amounts to significantly more than the recited exception, i.e. whether any additional element, or combination of elements, adds an inventive concept to the claims (MPEP 2106.05). As explained with respect to Step 2A, Prong Two, there are several additional elements. The computer, computing unit, handling device, control device, detection device, transmission device, receiving device, and controllable element are all, at best, the equivalent of merely adding the words “apply it" to the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (See MPEP 2106.05(f). Further the transmission device, detection device, and receiving device represent insignificant extra solution activity (See MPEP 2106.05(g)), specifically that of mere data gathering which is known to be well-understood, routine, or conventional within the art (See MPEP 2106.05(d)(II)). Insignificant extra solution activity, especially that which is well-understood, routine, or conventional in the art does not provide an inventive concept. Even when considered in combination, these additional elements to are not deemed to be sufficient enough to provide an inventive concept onto the abstract idea, therefore, they are not eligible. (Alice Corp., 134 S. Ct. at 2358 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984 (warning against a §101 that turns on "the draftsman's art")).
Dependent claim(s) 2-14 do not recite any further additional elements and are thus rejected for the same reasons enumerated above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 2021/0133670 A1).
Claim(s) 1 and 16 –
Cella discloses the following:
A computer (Cella: Paragraph 1573, “The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, client, firewall, gateway, hub, router, switch, infrastructure-as-a-service, platform-as-a-service, or other such computer and/or networking hardware or system. The software may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, infrastructure-as-a-service server, platform-as-a-service server, web server, and other variants such as secondary server, host server, distributed server, failover server, backup server, server farm, and the like. The server may include one or more of memories, processors, computer readable media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.”)
A handling device for handling piece goods in a handling process, comprising: (Cella: Paragraph 18, “In embodiments, the unified set of robotic process automation systems automate a process selected from the group consisting of selection of a quantity of product for an order, selection of a carrier for a shipment, selection of a vendor for a component, selection of a vendor for a finished goods order, selection of a variation of a product for marketing, selection of an assortment of goods for a shelf, determination of a price for a finished good, configuration of a service offer related to a product, configuration of product bundle, configuration of a product kit, configuration of a product package, configuration of a product display, configuration of a product image, configuration of a product description, configuration of a website navigation path related to a product, determination of an inventory level for a product, selection of a logistics type, configuration of a schedule for product delivery, configuration of a logistics schedule, configuration of a set of inputs for machine learning, preparation of product documentation, preparation of disclosures about a product, configuration of a product for a set of local requirements, configuration of a set of products for compatibility, configuration of a request for proposals, 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, inspection of data from a set of onboard diagnostics on a. product, inspection of diagnostic data from an Internet of Things system, review of sensor data from environmental sensors in a set of supply chain environments, selection of inputs for a digital twin, selection of outputs from a digital twin, selection of visual elements for presentation in a digital twin, diagnosis of sources of delay in a supply chain, diagnosis of sources of scarcity in a supply chain, diagnosis of sources of congestion in a supply chain, diagnosis of sources of cost overruns in a supply chain, diagnosis of sources of product defects in a supply chain, and prediction of maintenance requirements in supply chain infrastructure.”)
at least one controllable element for the physical handling of piece goods; (Cella: Paragraph 397, “In embodiments, the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of predictions for the coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict a fault condition or a problem state of a demand management application such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict a problem state with a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) based on a collection of machine operating information and preventive maintenance information for the machine.”; Paragraph 447, “In embodiments, robotic process automation 1442 may be provided for the wide range of value chain network processes mentioned throughout this disclosure and the documents incorporated herein by reference, including without limitation all of the applications 630. An environment for development of robotic process automation for value chain networks may include a set of interfaces for developers in which a developer may configure an artificial intelligence system 1160 to take inputs from selected data sources of the VCN data storage layer 624 and event data 1034, state data 1140 or other value chain network data objects 1004 from the monitoring systems layer 614 and supply them, such as to a neural network, either as inputs for classification or prediction, or as outcomes relating to the platform 102, value chain network entities 652, applications 630, or the like. The RPA development environment 1442 may be configured to take outputs and outcomes 1040 from various applications 630, again to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated. In embodiments, the development environment, and the resulting robotic process automation 1442 may involve monitoring a combination of both software program interaction observations 1500 (e.g., by workers interacting with various software interfaces of applications 630 involving value chain network entities 652) and physical process interaction observations 1510 (e.g., by watching workers interacting with or using machines, equipment, tools or the like in a value chain network 668). In embodiments, observation of software interactions 1500 may include interactions among software components with other software components, such as how one application 630 interacts via APIs with another application 630. In embodiments, observation of physical process interactions 1510 may include observation (such as by video cameras, motion detectors, or other sensors, as well as detection of positions, movements, or the like of hardware, such as robotic hardware) of how human workers interact with value chain entities 652 (such as locations of workers (including routes taken through a location, where workers of a given type are located during a given set of events, processes or the like, how workers manipulate pieces of equipment, cargo, containers, packages, products 650 or other items using various tools, equipment, and physical interfaces, the timing of worker responses with respect to various events (such as responses to alerts and warnings), procedures by which workers undertake scheduled deliveries, movements, maintenance, updates, repairs and service processes; procedures by which workers tune or adjust items involved in workflows, and many others). Physical process observation 1510 may include tracking positions, angles, forces, velocities, acceleration, pressures, torque, and the like of a worker as the worker operates on hardware, such as on a container or package, or on a piece of equipment involved in handling products, with a tool. Such observations may be obtained by any combination of video data, data detected within a machine (such as of positions of elements of the machine detected and reported by position detectors), data collected by a wearable device (such as an exoskeleton that contains position detectors, force detectors, torque detectors and the like that is configured to detect the physical characteristics of interactions of a human worker with a hardware item for purposes of developing a training data set). By collecting both software interaction observations 1500 and physical process interaction observations 1510 the RPA system 1442 can more comprehensively automate processes involving value chain entities 652, such as by using software automation in combination with physical robots.”)
a detection device for detecting at least one piece good information of at least one piece good handled during the handling process; (Cella: Paragraph 447, “In embodiments, robotic process automation 1442 may be provided for the wide range of value chain network processes mentioned throughout this disclosure and the documents incorporated herein by reference, including without limitation all of the applications 630. An environment for development of robotic process automation for value chain networks may include a set of interfaces for developers in which a developer may configure an artificial intelligence system 1160 to take inputs from selected data sources of the VCN data storage layer 624 and event data 1034, state data 1140 or other value chain network data objects 1004 from the monitoring systems layer 614 and supply them, such as to a neural network, either as inputs for classification or prediction, or as outcomes relating to the platform 102, value chain network entities 652, applications 630, or the like. The RPA development environment 1442 may be configured to take outputs and outcomes 1040 from various applications 630, again to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated. In embodiments, the development environment, and the resulting robotic process automation 1442 may involve monitoring a combination of both software program interaction observations 1500 (e.g., by workers interacting with various software interfaces of applications 630 involving value chain network entities 652) and physical process interaction observations 1510 (e.g., by watching workers interacting with or using machines, equipment, tools or the like in a value chain network 668). In embodiments, observation of software interactions 1500 may include interactions among software components with other software components, such as how one application 630 interacts via APIs with another application 630. In embodiments, observation of physical process interactions 1510 may include observation (such as by video cameras, motion detectors, or other sensors, as well as detection of positions, movements, or the like of hardware, such as robotic hardware) of how human workers interact with value chain entities 652 (such as locations of workers (including routes taken through a location, where workers of a given type are located during a given set of events, processes or the like, how workers manipulate pieces of equipment, cargo, containers, packages, products 650 or other items using various tools, equipment, and physical interfaces, the timing of worker responses with respect to various events (such as responses to alerts and warnings), procedures by which workers undertake scheduled deliveries, movements, maintenance, updates, repairs and service processes; procedures by which workers tune or adjust items involved in workflows, and many others). Physical process observation 1510 may include tracking positions, angles, forces, velocities, acceleration, pressures, torque, and the like of a worker as the worker operates on hardware, such as on a container or package, or on a piece of equipment involved in handling products, with a tool. Such observations may be obtained by any combination of video data, data detected within a machine (such as of positions of elements of the machine detected and reported by position detectors), data collected by a wearable device (such as an exoskeleton that contains position detectors, force detectors, torque detectors and the like that is configured to detect the physical characteristics of interactions of a human worker with a hardware item for purposes of developing a training data set). By collecting both software interaction observations 1500 and physical process interaction observations 1510 the RPA system 1442 can more comprehensively automate processes involving value chain entities 652, such as by using software automation in combination with physical robots.”; Paragraph 464, “In embodiments, opportunity mining may include facilities for solicitation of appropriate training data sets that may be used to facilitate process automation. For example, certain kinds of inputs, if available, would provide very high value for automation, such as video data sets that capture very experienced and/or highly expert workers performing complex tasks. Opportunity miners 1460 may search for such video data sets as described herein; however, in the absence of success (or to supplement available data), the platform may include systems by which a user, such as a developer, may specify a desired type of data, such as software interaction data (such as of an expert working with a program to perform a particular task), video data (such as video showing a set of experts performing a certain kind of delivery process, packing process, picking process, a container movement process, or the like), and/or physical process observation data (such as video, sensor data, or the like). The resulting library of interactions captured in response to specification may be captured as a data set in the data storage layer 624, such as for consumption by various applications 630, adaptive intelligence systems 614, and other processes and systems. In embodiments, the library may include videos that are specifically developed as instructional videos, such as to facilitate developing an automation map that can follow instructions in the video, such as providing a sequence of steps according to a procedure or protocol, breaking down the procedure or protocol into sub-steps that are candidates for automation, and the like. In embodiments, such videos may be processed by natural language processing, such as to automatically develop a sequence of labeled instructions that can be used by a developer to facilitate a map, a graph, or other models of a process that assists with development of automation for the process. In embodiments, a specified set of training data sets may be configured to operate as inputs to learning. In such cases the training data may be time-synchronized with other data within the platform 604, such as outputs and outcomes from applications 630, outputs and outcomes of value chain entities 652, or the like, so that a given video of a process can be associated with those outputs and outcomes, thereby enabling feedback on learning that is sensitive to the outcomes that occurred when a given process that was captured (such as on video, or through observation of software interactions or physical process interactions). For example, this may relate to an instruction video such as a video of a person who may be building or rebuilding (e.g., rebuilding a bearing set). This instruction video may include individual steps for rebuild that may allow a staging of the training to provide instructions such as parsing the video into stages that mimic the experts staging in the video. For example, this may include tagging of the video to include references to each stage and status (e.g., stage one complete, stage two, etc.) This type of example may utilize artificial intelligence that may understand that there may be a series of sub-functions that add up to a final function.”; Paragraph 562, “In some embodiments, the machine learning model 3000 may be defined via anomaly detection, i.e. by identifying rare and/or outlier instances of one or more items, events and/or observations. The rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data. Unsupervised anomaly detection may include detecting of anomalies, by the machine learning model 3000, in an unlabeled training data set under an assumption that a majority of the training data is “normal.” Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.””)
a control device for controlling the at least one controllable element according to a control strategy based on the at least one piece of information; (Cella: Paragraph 414, “Embodiments are described herein for using artificial intelligence systems or capabilities to identify, configure and regulate automated control signals. Such embodiments may further include a closed loop of feedback from the coordinated set of demand management and supply chain applications (e.g., state information, output information, outcomes and the like) that is optionally processed with machine learning and used to adapt the automated control signals for at least one of the goods in the category of goods. An automated control signal may be adapted based on, for example, an indication of feedback from a supply chain application that yield of a good suggests a production problem. In this example, the automated control signal may impact production rate and the feedback may cause the signal to automatically self-adjust to a slower production rate until the production problem is resolved.”; Paragraph 571, “The adaptive edge computing and other edge intelligence systems 1420 may thus provide, in embodiments, intelligence for monitoring, managing, controlling, or otherwise handling a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 650 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer. This unification may provide a number of advantages, including improved monitoring, improved remote control, improved autonomy, improved prediction, improved classification, improved visualization and insight, improved visibility, and others. These may include adaptive edge computing and other edge intelligence systems 1420 that are used in connection with demand factors 1540 and supply factors 1550, so that an application 630 may benefit from information collected by, processed by, or produced by the adaptive edge computing and other edge intelligence systems 1420 for other applications 630 of the platform 604, and a user can develop insights about connections among the factors and control one or both of them with coordinated intelligence. For example, coordinated intelligence may include, but is not limited to, analytics and processing for monitoring data streams, as described herein, for the purposes of classification, prediction or some other type of analytic modeling. Such coordinated intelligence methods and systems may be applied in an automated manner in which differing combinations of intelligence assets are applied. As an example, within an industrial environment the coordinated intelligence system may monitor signals coming from machinery deployed in the environment. The coordinated intelligence system may classify, predict or perform some other intelligent analytics, in combination, for the purpose of, for example, determining a state of a machine, such as a machine in a deteriorated state, in an at-risk state, or some other state. The determination of a state may cause a control system to alter a control regime, for example, slowing or shutting down a machine that is in a deteriorating state. In embodiments, the coordinated intelligence system may coordinate across multiple entities of a value chain, supply chain and the like. For example, the monitoring of the deteriorating machine in the industrial environment may simultaneously occur with analytics related to parts suppliers and availability, product supply and inventory predictions, or some other coordinated intelligence operation. The adaptive edge computing and other edge intelligence systems 1420 may be adapted over time, such as by learning on outcomes 1040 or other operations of the other adaptive intelligent systems 614, such as to determine which elements collected and/or processed by the adaptive edge computing and other edge intelligence systems 1420 should be made available to which applications 630, what elements and/or content provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.”; Paragraph 1048, “In embodiments, the management platform 604 can manage port gate-in and gate-out improvements to the logistics of the flow of assets and cargoes around the maritime facilities 622. In embodiments, the management platform 604 can manage road improvements both within and connecting to the maritime facilities 622. In embodiments, the management platform 604 can manage rail improvements both within and connecting to the maritime facilities 622. In embodiments, the management platform 604 can manage berth improvements in the maritime facilities 622 including to docks, wharves, piers and the like. In embodiments, the management platform 604 can manage berth improvements including dredging at the berths, approach and departure areas adjacent to the berth, and in areas around maritime facilities. In embodiments, the management platform 604 can manage cargo moving equipment used on land. In embodiments, the management platform 604 can manage facilities necessary to improve cargo transport including silos, elevators, conveyors, container terminals, roll-on/roll-off facilities including parking garages necessary for intermodal freight transfer, warehouses including refrigerated facilities, bunkering facilities for oil or gas products, lay-down areas, transit sheds, and the like. In embodiments, the management platform 604 can manage utilities necessary for standard operations including lighting, stormwater, and the like that can be incidental to a larger set of maritime facilities. In embodiments, the management platform 604 can manage port-related intelligent transportation system hardware and software including all technologies used to promote efficient port movements including routing and communications for vessels, trucks, and rail cargo movements as well as flow-through processing for import/export requirements, storage and tracking, and asset/equipment management. In embodiments, the management platform 604 can manage phytosanitary treatment facilities to support phytosanitary treatment requirements. In embodiments, the management platform 604 can manage, configure and re-configure fully automated cargo-handling equipment”)
a transmission device configured to transmit the piece goods information to a data center; (Cella: Paragraph 569, “In embodiments, the platform 604 may include a unified set of adaptive edge computing and other edge intelligence systems 1420 that provide coordinated edge computation and other edge intelligence 1420 capabilities for a set of multiple applications 630 of various types, such as a set of supply chain management applications 1500, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652. In embodiments, edge intelligence capabilities of the systems and methods described herein may include, but are not limited to, on-premise edge devices and resources, such as local area network resources, and network edge devices, such as those deployed at the edge of a cellular network or within a peripheral data center, both of which may deploy edge intelligence, as described herein, to, for example, carry out intelligent processing tasks at these edge locations before transferring data or other matter, to the primary or core cellular network command or central data center.”; Paragraph 1142, “In embodiments, the digital twin I/O system 8104 is configured to obtain data from a set of data sources (e.g., users, sensor systems, internal and/or external databases, software platforms (e.g., CRMs, ERPs, CRMs, workflow management system), surveys, customers, and the like). In some embodiments, the digital twin I/O system 8104 (or other suitable component) may provide a graphical user interface that allows a user affiliated with an enterprise to upload various types of data that may be leveraged to generate the enterprise digital twins of the enterprise. For example, in providing data to support an environment digital twin, a user may upload 3D scans, still and video images, LIDAR scans, structured light scans, blueprints, 3D floor plans, object types (e.g., products, sensors, machinery, furniture, and the like), object properties (e.g., materials, physical properties, descriptions, price, and the like), output type (e.g., sensor units), architectural drawings, CAD documents, equipment specifications, and many others via the digital twin I/O system 8104. In embodiments, the digital twin I/O system 8104 may subscribe to or otherwise automatically receive data streams (e.g., publicly available data streams, such as RSS feeds, news streams, event streams, log streams, sensor system streams, and the like) on behalf of an enterprise. Additionally or alternatively, the digital twin system I/O system 8104 may periodically query and/or receive data from a connected data source 8020, such as a sensor system 8022 having sensors that sensor data from facilities (e.g., manufacturing facilities, shipping facilities, warehouse facilities, logistics facilities, retail facilities, distribution facilities, agricultural facilities, resource extraction facilities, computing facilities, transportation facilities, infrastructure facilities, networking facilities, data center facilities, and many others) and/or other physical entities of the enterprise, a sales database 8024 that is updated with sales figures in real time, a CRM system 8026, a content marketing platform 8028, financial databases 8030, surveys 8032, org charts 8034, workflow management systems 8036, third-party data sources 8038, customer databases 8040 that store customer data, and/or third-party data sources 8038 that store third-party data, edge devices 8042 that report data relating to physical assets (e.g., smart machinery/manufacturing equipment, sensor kits, autonomous vehicles, of the enterprise, wearable devices, and the like), enterprise resource management systems 8044, HR systems 8046, content management systems 8026, and the like). In embodiments, the digital twin I/O system 8104 may employ a set of web crawlers to obtain data. In embodiments, the digital twin I/O system 8104 may include listening threads that listen for new data from a respective data source. In embodiments, the digital twin I/O system 8104 may be configured with a set of webhooks that receive data from a respective set of data sources. In these embodiments, the digital twin I/O system 8104 may receive data that is pushed from an external data source, such as real-time data.”)
a receiving device configured to receive a new control strategy from the data center; (Cella: Paragraph 59, “In embodiments, an information technology system, includes a cloud-based management platform with a micro-services architecture, the platform having a set of interfaces that are configured to access and configure features of the platform, a set of network connectivity facilities that are configured to direct a set of value chain network entities to connect to the features of the platform, a set of adaptive intelligence facilities that are configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform, a set of data storage facilities that are configured to store data collected and handled by the platform, and a set of monitoring facilities that are configured to monitor the value chain network entities, wherein the interfaces, the network connectivity facilities, the adaptive intelligence facilities, the data storage facilities, and the monitoring facilities are coordinated for monitoring and management of the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; 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.”; Paragraph 1179, “In embodiments, the CEO digital twin 8302 may be configured to simulate one or more aspects of the enterprise. Such simulations may assist the user (e.g., the CEO) in making executive level decisions. For example, simulations of a proposed executive initiative may be tested, for example using the modeling, machine learning, and/or AI techniques, as described herein, by simulating temporal effects on initiatives (e.g., introduction of a new product), varying financial parameters (e.g., potential investment levels), targeting parameters (e.g., geographic, demographic, or the like), and/or other suitable executive parameters. In embodiments, the digital twin simulation system 8116 may receive a request to perform an executive simulation requested by the CEO digital twin 8302, where the request indicates one or more parameters that are to be varied in one or more enterprise digital twins. In response, the digital twin simulation system 8116 may return the simulation results to the CEO digital twin 8302, which in turn outputs the results to the user via the client device display. In this way, the user may be provided with various outcomes corresponding to different parameter configurations. For example, a user may request a set of simulations to be run to test different supply chain strategies to see how the different strategies affect the throughput of a manufacturing facility and the overall impact on the profits and losses of the enterprise. The digital twin simulation system 8116 may perform the simulations by varying the different supply chain strategies and may output the throughputs and P&L forecasts for each respective supply chain strategy. In some embodiments, the user may select a parameter set based on the various outcomes, and iterate simulations based at least on the varied prior outcomes. Drawing from the previous example, the user may decide to select the supply chain strategy that maximizes P&L forecasts but does not adversely affect throughput of the manufacturing facility. In some embodiments, an executive agent may be trained to recommend and/or select a parameter set based on the respective outcomes associated with each respective parameter set.”; Paragraph 1215, “In some of these embodiments, the COO digital twin 8306 may be configured to simulate operations activities, such as a proposed new operational plan, process or program. In these embodiments, the digital twin simulation system 8116 may receive a request to perform the simulation requested by the COO digital twin 8306, where the request indicates features and the parameters of the operational plan or other activity that is proposed for implementation, the associated variables for which may be altered or varied to produce differing simulation environments. In response, the digital twin simulation system 8116 may return the simulation results to the COO digital twin 8306, which in turn outputs the results to the user via the client device display. In this way, the user is provided with various outcomes corresponding to different operational parameter configurations. In embodiments, an executive agent trained by the user may select the parameter sets based on the various outcomes.”; Paragraph 1251, “In embodiments, the CTO digital twin 8310 may be configured to manage operational planning, based at least in part by leveraging predictive analytics for development planning, and supply chain management in order to increase company efficacy while optimizing operating expenses. In embodiments, the CTO digital twin 8310 may be configured to obtain and depict oversight activity that includes, but is not limited to, internal controls design, testing, and reporting while directing listed actions the appropriate personnel.”)
and wherein the control device configured to adapt the control strategy according to the new control strategy. (Cella: Paragraph 373, “These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction management systems 1900 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems.”; Paragraph 391, “Referring to FIG. 15, a management platform of an information technology system, such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provide coordinated intelligence through the application of artificial intelligence, such as through application of a hybrid artificial intelligence system 3060, and optionally through one or more expert systems, machine learning systems, and the like for use with a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The hybrid adaptive intelligence systems 614 may deliver two types of artificial intelligence systems, type A 3052 and type B 3054 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the hybrid adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the hybrid adaptive intelligence systems 614 can operate on or in cooperation with the sets of supply chain applications (e.g., demand management applications 824 and supply chain applications 812). The hybrid adaptive intelligence systems 614 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The hybrid adaptive intelligence systems 614 may facilitate applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence system 1160 can operate independently, cooperatively, and optionally coordinate operation to provide coordinated intelligence for operation of the value chain that produces at least one of the goods in the category of goods 3010.”; Paragraph 395, “Referring to FIG. 16, a management platform of an information technology system, such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of predictions 3070. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide a set of predictions 3070 through the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The adaptive intelligence systems 614 may deliver the set of prediction 3070 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications. The adaptive intelligence systems 614 may include an artificial intelligence system that provides artificial intelligence capabilities known to be associated with artificial intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The adaptive intelligence systems 614 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of predictions 3070 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets.”; Paragraph 403, “In embodiments, the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 for the coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to classify fault conditions or problem states of a demand management application, such as a classification of a lack of sufficient validated feedback. The probabilistic neural network may be used to classify a problem state of a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) as pertaining to at least one of machine operating information and preventive maintenance information for the machine.”)
Claim(s) 2 –
Cella discloses the limitations of claim(s) 1
Cella further discloses the following:
wherein the piece good information comprises a position and location information of the piece good in the handling device, a time stamp, a weight of the piece good, a nature of the piece good, a packaging information, an address information, a sender information, and/or an information about a center of gravity of the piece good. (Cella: Paragraph 13, “In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors.”; Paragraph 642, “FIG. 41 illustrates an example of a packaging design system that interfaces with the adaptive intelligent systems layer 614. In embodiments, the packaging design system may be configured to design one or more aspects of packaging for a physical object being conveyed in the value chain network. In some embodiments, the packaging design system may select one or more packaging attributes (e.g., size, material, padding, etc.) of the packaging to optimize one or more outcomes associated with the transport of the physical object. For example, the packaging attributes may be selected to reduce costs, decrease loss/damage, decrease weight, decrease plastic or other non-biodegradable waste, or the like. In embodiments, the packaging design system leverages the artificial intelligence system 2010 to obtain packaging attribute recommendations. In embodiments, the packaging design system may provide one or more features of the physical object. In embodiments, the features of the physical object may include the dimensions of the physical object, the mass of the physical object, the source of the physical object, one or more potential destinations of the physical object, the manner by which the physical object is shipped, and the like. In embodiments, the packaging design system may further provide one or more optimization goals for the package design (e.g., reduce cost, reduce damage, reduce environmental impact). In response, the artificial intelligence system 2010 may determine one or more recommended packaging attributes based on the physical asset features and the given objective. In embodiments, the packaging design system receives the packaging attributes and generates a package design based thereon. The package design may include a material to be used, the external dimensions of the packaging, the internal dimensions of the packaging, the shape of the packaging, the padding/stuffing for the packaging, and the like.”)
Claim(s) 3 and 9 –
Cella discloses the limitations of claims 1-2
Cella further discloses the following:
wherein the at least one key performance indicator is indicative of a gap between two piece goods, an efficiency of piece goods per unit time, a position of a piece good at the exit of the handling device, and/or an error rate of the handling of the piece good. (Cella: Paragraph 126, “In embodiments, a training set of outcomes may include data relating to at least one of a financial outcome, an operational outcome, a fault outcome, a success outcome, a performance indicator outcome, an output outcome, a consumption outcome, an energy utilization outcome, a resource utilization outcome, a cost outcome, a profit outcome, a revenue outcome, a sales outcome, and a production outcome.”; Paragraph 360, “In embodiments, each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 624. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and/or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another. In embodiments, the data handling layers 624 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and/or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. For example, a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity can be distributed via the monitoring systems layer 614 to multiple distinct applications in the value chain management platform layer 604, such as one that facilitates monitoring the physiological, psychological, performance level, attention, or other state of a worker and another that facilitates operational efficiency and/or effectiveness. In embodiments, the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652. For example, one or more video streams or other sensor data collected of or with respect to a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled IoT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like. In such an example, the monitoring systems layer 614 may further align a set of videos, camera images, sensor data, or the like, with other data, such as a stream of data from wearable devices, a stream of data produced by value chain network systems (such as ships, lifts, vehicles, containers, cargo handling systems, packing systems, delivery systems, drones/robots, and the like), a stream of data collected by mobile data collectors, and the like. Configuration of the monitoring systems layer 614 as a common platform, or set of microservices, that are accessed across many applications, may dramatically reduce the number of interconnections required by an owner or other operator within a value chain network in order to have a growing set of applications monitoring a growing set of IoT devices and other systems and devices that are under its control.”; Paragraph 418, “In embodiments, an artificial intelligence system 1160 may provide an information routing recommendation based on goals, such as goals of a value chain network, goals of information routing, and the like. Goal-based information routing recommendations may include routing goals, such as Quality of Service routing goals, routing reliability goals (which may be measured based on a transmission failure rate and the like). Other goals may include a measure of latency associated with one or more candidate routes. An information routing recommendation may be based on the availability of information in a selected value chain network, such as when information is available and when it needs to be delivered. For information that is available well ahead of when it is needed (e.g., a nightly production report that is available for routing at 2 AM is first needed by 7 AM), routing recommendations may include using resources that are lower cost, may involve short delays in routing and the like. For information that is available just before it is needed (e.g., a result of product testing is needed within a few hundred milliseconds of when the test is finished to maintain a production operation rate, and the like).”)
Claim(s) 4 and 10 –
Cella discloses the limitations of claims 1-3
Cella further discloses the following:
wherein the determination of a control strategy is carried out via a digital twin of the handling device. (Cella: Paragraph 437, “In embodiments, a digital twin 1700 may be used in connection with shared or converged processes among the various pairs of the applications 630 of the application layer 604, such as, without limitation, of a converged process involving a security application 834 and an inventory management application 820, integrated automation of blockchain-based applications 844 with facility management applications 850, and many others. In embodiments, converged processes may include shared data structures for multiple applications 630 (including ones that track the same transactions on a blockchain but may consume different subsets of available attributes of the data objects maintained in the blockchain or ones that use a set of nodes and links in a common knowledge graph) that may be connected to with the digital twin 1700 such that the digital twin 1700 is updated accordingly. For example, a transaction indicating a change of ownership of an entity 652 may be stored in a blockchain and used by multiple applications 630, such as to enable role-based access control, role-based permissions for remote control, identity-based event reporting, and the like that may be connected to and shared with the digital twin 1700 such that the digital twin 1700 may be updated accordingly. In embodiments, converged processes may include shared process flows across applications 630, including subsets of larger flows that are involved in one or more of a set of applications 630 that may be connected to and shared with the digital twin 1700 such that the digital twin 1700 may be updated accordingly. For example, an inspection flow about a value chain network entity 652 may serve an analytics solution 838, an asset management solution 814, and others.”; Paragraph 441, “In example embodiments, a digital twin system may be configured to generate a variety of enterprise digital twins 1700 in connection with a value chain (e.g., specifically value chain network entities 652). For example, an enterprise that produces goods internationally (or at multiple facilities) may configure a set of digital twins 1700, such as supplier twins that depict the enterprise's supply chain, factory twins of the various production facilities, product twins that represent the products made by the enterprise, distribution twins that represent the enterprise's distribution chains, and other suitable twins. In doing so, the enterprise may define the structural elements of each respective digital twin as well as any system data that corresponds to the structural elements of the digital twin. For instance, in generating a production facility twin, the enterprise may the layout and spatial definitions of the facility and any processes that are performed in the facility. The enterprise may also define data sources corresponding to the value chain network entities 652, such as sensor systems, smart manufacturing equipment, inventory systems, logistics systems, and the like that provide data relevant to the facility. The enterprise may associate the data sources with elements of the production facility and/or the processes occurring the facility. Similarly, the enterprise may define the structural, process, and layout definitions of its supply chain and its distribution chain and may connect relevant data sources, such as supplier databases, logistics platforms, to generate respective distribution chain and supply chain twins. The enterprise may further associate these digital twins to have a view of its value chain. In embodiments, the digital twin system may perform simulations of the enterprise's value chain that incorporate real-time data obtained from the various value chain network entities 652 of the enterprise. In some of these embodiments, the digital twin system may recommend decisions to a user interacting with the enterprise digital twins 1700, such as when to order certain parts for manufacturing a certain product given a predicted demand for the manufactured product, when to schedule maintenance on machinery and/or replace machinery (e.g., when digital simulations on the digital twin indicates the demand for certain products may be the lowest or when it would have the least effect on the enterprise's profits and losses statement), what time of day to ship items, or the like. The foregoing example is a non-limiting example of the manner by which a digital twin may ingest system data and perform simulations in order to further one or more goals.”)
Claim(s) 5 and 11 –
Cella discloses the limitations of claims 1-4, and 10
Cella further discloses the following:
wherein a new control strategy is tested by using the digital twin (Cella: Paragraph 632, “FIG. 38 illustrates example embodiments of a system for controlling and/or making decisions, predictions, and/or classification on behalf of a value chain system 2030. In embodiments, an artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030. In some embodiments, a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022. As used herein, the term machine-learned model may refer to any suitable type of model that is learned in a supervised, unsupervised, or hybrid manner. Examples of machine-learned models include neural networks (e.g., deep neural networks, convolution neural networks, and many others), regression based models, decision trees, hidden forests, Hidden Markov models, Bayesian models, and the like. In embodiments, the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models. The machine learning system may in-turn reinforce/retrain the machine-learned models 2004 based on the feedback. Furthermore, in embodiments, the machine-learning system 2002 may train the machine-learning models based on simulation data 2022 generated by the digital twin simulation system 2020. In these embodiments, the digital twin simulation system 2020 may be instructed to run specific simulations using one or more digital twins that represent objects and/or environments that are managed, maintained, and/or monitored by the value chain system. In this way, the digital twin simulation system 2020 may provide richer data sets that the machine-learning system 2002 may use to train/reinforce the machine-learned models. Additionally or alternatively, the digital twin simulation system 2020 may be leveraged by the artificial intelligence system 2010 to test a decision made by the artificial intelligence system 2010 before providing the decision to the value chain entity.”; Paragraph 643, “In some embodiments, the packaging design system may provide a packaging design to the digital twin system 2020, which generates a digital twin of the packaging and physical asset based on the packaging design. The digital twin of the packaging and physical asset may be used to run simulations that test the packaging (e.g., whether the packaging holds up in shipping, whether the packaging provides adequate insulation/padding, and the like). In embodiments, the results of the simulation may be returned to the packaging design system, which may output the results to a user. In embodiments, the user may accept the packaging design, may adjust the packaging design, or may reject the design. In some embodiments, the digital twin system may run simulations on one or more digital twins to test different conditions that the package may be subjected to (e.g., outside in the snow, rocking in a boat, being moved by a forklift, or the like). In some embodiments, the digital twin system may output the results of a simulation to the machine-learning system 2002, which can train/reinforce the packaging design models based on the properties used to run the simulation and the outcomes stemming therefrom.”)
Claim(s) 6 and 12 –
Cella discloses the limitations of claims 1-5 and 10
Cella further discloses the following:
comparing the control strategy of the handling device with the new control strategy of the digital twin; and (Cella: Paragraph 631, “In some embodiments, the adaptive intelligent systems layer 614 is configured to execute simulations using the digital twin. For example, the adaptive intelligent systems layer 614 may iteratively adjust one or more parameters of a digital twin and/or one or more embedded digital twins. In embodiments, the adaptive intelligent systems layer 614 may, for each set of parameters, execute a simulation based on the set of parameters and may collect the simulation outcome data resulting from the simulation. Put another way, the adaptive intelligent systems layer 614 may collect the properties of the digital twin and the digital twins within or containing the digital twin used during the simulation as well as any outcomes stemming from the simulation. For example, in running a simulation on a digital twin of a shipping container, the adaptive intelligent systems layer 614 can vary the materials of the shipping container and can execute simulations that outcomes resulting from different combinations. In this example, an outcome can be whether the goods contained in the shipping container arrive to a destination undamaged. During the simulation, the adaptive intelligent systems layer 614 may vary the external temperatures of the container (e.g., a temperature property of the digital twin of an environment of the container may be adjusted between simulations or during a simulation), the dimensions of the container, the products inside (represented by digital twins of the products) the container, the motion of the container, the humidity inside the container, and/or any other properties of the container, the environment, and/or the contents in the container. For each simulation instance, the adaptive intelligent systems layer 614 may record the parameters used to perform the simulation instance and the outcome of the simulation instance. In embodiments, each digital twin may include, reference, or be linked to a set of physical limitations that define the boundary conditions for a simulation. For example, the physical limitations of a digital twin of an outdoor environment may include a gravity constant (e.g., 9.8 m/s2), a maximum temperature (e.g., 60 degrees Celsius), a minimum temperature (e.g., −80 degrees Celsius), a maximum humidity (e.g., 110% humidity), friction coefficients of surfaces, maximum velocities of objects, maximum salinity of water, maximum acidity of water, minimum acidity of water. Additionally or alternatively, the simulations may adhere to scientific formulas, such as ones reflecting principles or laws of physics, chemistry, materials science, biology, geometry, or the like. For example, a simulation of the physical behavior of an object may adhere to the laws of thermodynamics, laws of motion, laws of fluid dynamics, laws of buoyancy, laws of heat transfer, laws of cooling, and the like. Thus, when the adaptive intelligent systems layer 614 performs a simulation, the simulation may conform to the physical limitations and scientific laws, such that the outcomes of the simulations mimic real world outcomes. The outcome from a simulation can be presented to a human user, compared against real world data (e.g., measured properties of a container, the environment of the container, the contents of the container, and resultant outcomes) to ensure convergence of the digital twin with the real world, and/or used to train machine learning models.”; Paragraph 793, “Machine twin 1770 then coordinates with artificial intelligence system to select one or more of models based on the kind and quality of available data and the desired answers or outcomes. For example, physical models 5320 may be selected if the intended use of machine twin 1770 is to simulate what-if scenarios and predict how the machine will behave under such scenarios. Fault Detection and Diagnostics Models 5322 may be selected to determine the current health of the machine and any fault conditions. A simple fault detection model may use one or more condition indicators to distinguish between regular and faulty behaviors and may have a threshold value for the condition indicator that is indicative of a fault condition when exceeded. A more complex model may train a classifier to compare the value of one or more condition indicators to values associated with fault states and returns the probability of presence of one or more fault states.”; Paragraph 1094, “In embodiments, a digital twin may be provided to represent the organizational structure of a third-party organization in the cohort of the organization of the user of the EMP, such as a supplier, vendor, distributor, logistics partner, value added reseller, representative, agent, venture partner, competitor, advertiser, marketplace or the like. An organizational digital twin of a cohort organization may represent structure, relationships, roles, identities, and competencies of individuals within roles or the organization, such that a user of the EMP may quickly and readily view salient information about the relevant parts of the organization. The organizational digital twin of the cohort organization may be automatically maintained by an artificial intelligence system of the EMP, such as by spidering, webscraping, and parsing websites, news feeds, press releases, social media data, and other available data sources, in order to maintain an accurate representation of the organization. The artificial intelligence system may be trained on a training set of data labelled by human users and/or automatically labelled to maintain an updated organizational structure. The resulting cohort digital twin may be configured to provide various role-specific views within the EMP. For example, a salesperson may be presented a digital twin view of the part of the cohort organization that is most likely to include individuals who are likely to be involved in a decision to purchase the user's offerings, while an HR person's view may be configured to present a digital twin view of the part of the cohort organization that provides the most comparable benchmark information for human resources. Digital twin views of cohort organizations may be automatically populated and/or configured, by training artificial intelligence systems on a process-specific or role-specific basis, to support a wide range of processes and features within the EMP, such as identification of recruiting candidates, benchmarking as to organizational structures, benchmarking as to competencies and talent, identification and/or configuration of sales and business development targets, identification of competitive offerings and/or projects, identification of targets for mergers and acquisitions, identification of targets for competitive research, and many others.”)
creating an evaluation of the control strategies based on the comparison. (Cella: Paragraph 1173, “In embodiments, the types of data that may be populate a CEO digital twin 8302 may include, but are not limited to: macroeconomic data, microeconomic analytic data, forecast data, demand planning data, employment and salary data, analytic results of AI and/or machine learning modeling (e.g., financial forecasting), prediction data, recommendation data, securities-relevant financial data (e.g., earnings, profitability), industry analyst data (e.g., Gartner quadrant), strategic competitive data (e.g., news and events regarding industry trends and competitors), business performance metrics by business unit that may be relevant to evaluating performance of the business units (e.g., P&L, head count, factory health, supply chain metrics, sales metrics, R&D metrics, marketing metrics, and many others), Board package data, or some other type of data relevant to the operations of the CEO and/or executive department. In embodiments, the digital twin system 8004 may obtain securities-relevant financial data from, for example, the enterprise's accounting software (e.g., via an API), publicly disclosed financial statements, third-party reports, tax filings, and the like. In embodiments, the digital twin system 8004 may obtain strategic competitive data from public news sources, from publicly disclosed financial reports, and the like. In embodiments, macroeconomic data may be derived analytically from various financial and operational data collected by the EMP 8000. In embodiments, the business performance metrics may be derived analytically, based at least in part on real time operations data, by the artificial intelligence services system 8010 and/or provided from other users and/or their respective executive digital twins. The CEO digital twin 8302 may be used to define real time operations data parameters of interest and to monitor, collect, analyze, and interpret real time operations data for conformance to and alignment with an organization's stated business objects, Board requirements, industry best practice, regulation, or some other criterion.”; Paragraph 1212, “In embodiments, a COO digital twin 8306 may depict a twin of the operations department, which the user may use to identify, assign, instruct, oversee and review operations department personnel and third-party personnel that are associated with the design, implementation and evaluation of operational processes, internal infrastructures, reporting systems, company policies, and the like.”)
Claim(s) 7 and 13 –
Cella discloses the limitations of claims 1-6
Cella further discloses the following:
wherein the handling process of the handling device comprises the application of an algorithm, wherein the algorithm is configured based on the control strategy to output control instructions as output data based on piece goods information as input data. (Cella: Paragraph 366, “The value chain management platform layer 604, referred to in some cases herein for convenience as the platform layer 604, may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and/or common adaptive intelligence of the adaptive intelligence layer 614. Outputs from the applications 630 in the platform layer 604 may be provided to the other data handing layers 624. These may include, without limitation, state and status information for various objects, entities, processes, flows and the like; object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others. Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614. The cross-application nature of the platform layer 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services and other capabilities that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that may generally improve any of process and application outputs and outcomes 1040 pursued by use of the platform 604. In some examples, outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.”; Paragraph 539, “Referring to FIG. 43, the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652. The machine learning model 3000 is an algorithm and/or statistical model that performs specific tasks without using explicit instructions, relying instead on patterns and inference. The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The sensor data input to the machine learning model 3000 may be used to train the machine learning model 3000 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the value chain entities 652. The machine learning model 3000 may also use input data from a user or users of the information technology system. The machine learning model 3000 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof. The machine learning model 3000 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning.”; Paragraph 549, “In some embodiments, a user of the information technology system may input a modeling goal into the machine learning model 3000. The machine learning model 3000 may learn to analyze training data to output suggestions to the user of the information technology system regarding which types of sensor data are most relevant to achieving the modeling goal, such as one or more types of sensors positioned in, on, or near a value chain entity or a plurality of value chain entities that is relevant to the achievement of the modeling goal is and/or are not sufficient for achieving the modeling goal, and how a different configuration of the types of sensors, such as by adding, removing, or repositioning sensors, may better facilitate achievement of the modeling goal by the machine learning model 3000 and the digital twin system 1700. In some embodiments, the machine learning model 3000 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 3000 may make suggestions or predictions to a user of the information technology system related to increasing or decreasing collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 3000 may use sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more value chain entities 652 of the plurality of value chain entities 652 to automatically create and/or propose modeling goals. In some embodiments, modeling goals automatically created by the machine learning model 3000 may be automatically implemented by the machine learning model 3000. In some embodiments, modeling goals automatically created by the machine learning model 3000 may be proposed to a user of the information technology system, and implemented only after acceptance and/or partial acceptance by the user, such as after modifications are made to the proposed modeling goal by the user.”; Paragraph 641, “In some embodiments, the simulations run by the digital twin system 2010 may be used to train the recommendation models. Furthermore, when the design recommendations are implemented by an organization, the logistics system of the organization may be configured to report (e.g., via sensors, computing devices, manual human input) outcome data corresponding to the design recommendations to the machine learning system 2002, which may use the outcome data to reinforce the logistics design recommendation models.”)
Claim(s) 8 and 14 –
Cella discloses the limitations of claims 1-7
Cella further discloses the following:
wherein unit load information from a plurality of handling devices is recorded in the data center. (Cella: Paragraph 1142, “In embodiments, the digital twin I/O system 8104 is configured to obtain data from a set of data sources (e.g., users, sensor systems, internal and/or external databases, software platforms (e.g., CRMs, ERPs, CRMs, workflow management system), surveys, customers, and the like). In some embodiments, the digital twin I/O system 8104 (or other suitable component) may provide a graphical user interface that allows a user affiliated with an enterprise to upload various types of data that may be leveraged to generate the enterprise digital twins of the enterprise. For example, in providing data to support an environment digital twin, a user may upload 3D scans, still and video images, LIDAR scans, structured light scans, blueprints, 3D floor plans, object types (e.g., products, sensors, machinery, furniture, and the like), object properties (e.g., materials, physical properties, descriptions, price, and the like), output type (e.g., sensor units), architectural drawings, CAD documents, equipment specifications, and many others via the digital twin I/O system 8104. In embodiments, the digital twin I/O system 8104 may subscribe to or otherwise automatically receive data streams (e.g., publicly available data streams, such as RSS feeds, news streams, event streams, log streams, sensor system streams, and the like) on behalf of an enterprise. Additionally or alternatively, the digital twin system I/O system 8104 may periodically query and/or receive data from a connected data source 8020, such as a sensor system 8022 having sensors that sensor data from facilities (e.g., manufacturing facilities, shipping facilities, warehouse facilities, logistics facilities, retail facilities, distribution facilities, agricultural facilities, resource extraction facilities, computing facilities, transportation facilities, infrastructure facilities, networking facilities, data center facilities, and many others) and/or other physical entities of the enterprise, a sales database 8024 that is updated with sales figures in real time, a CRM system 8026, a content marketing platform 8028, financial databases 8030, surveys 8032, org charts 8034, workflow management systems 8036, third-party data sources 8038, customer databases 8040 that store customer data, and/or third-party data sources 8038 that store third-party data, edge devices 8042 that report data relating to physical assets (e.g., smart machinery/manufacturing equipment, sensor kits, autonomous vehicles, of the enterprise, wearable devices, and the like), enterprise resource management systems 8044, HR systems 8046, content management systems 8026, and the like). In embodiments, the digital twin I/O system 8104 may employ a set of web crawlers to obtain data. In embodiments, the digital twin I/O system 8104 may include listening threads that listen for new data from a respective data source. In embodiments, the digital twin I/O system 8104 may be configured with a set of webhooks that receive data from a respective set of data sources. In these embodiments, the digital twin I/O system 8104 may receive data that is pushed from an external data source, such as real-time data.”)
Claim(s) 15 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
when the program is executed by a computing unit, cause the computing unit to execute the method according to claim 1. (Cella: Paragraph 1158, “In embodiments, digital twins, as described herein, may operate in coordination with an adaptive edge computing system and/or a set of adaptive edge computing systems that provide coordinated edge computation include a wide range of systems, such as classification systems (such as image classification systems, object type recognition systems, and others), video processing systems (such as video compression systems), signal processing systems (such as analog-to-digital transformation systems, digital-to-analog transformation systems, RF filtering systems, analog signal processing systems, multiplexing systems, statistical signal processing systems, signal filtering systems, natural language processing systems, sound processing systems, ultrasound processing systems, and many others), data processing systems (such as data filtering systems, data integration systems, data extraction systems, data loading systems, data transformation systems, point cloud processing systems, data normalization systems, data cleansing system, data deduplication systems, graph-based data storage systems, object-oriented data storage systems, and others), predictive systems (such as motion prediction systems, output prediction systems, activity prediction systems, fault prediction systems, failure prediction systems, accident prediction systems, event predictions systems, event prediction systems, and many others), configuration systems (such as protocol selection systems, storage configuration systems, peer-to-peer network configuration systems, power management systems, self-configuration systems, self-healing systems, handshake negotiation systems, and others), artificial intelligence systems (such as clustering systems, variation systems, machine learning systems, expert systems, rule-based systems, deep learning systems, and many others), system management and control systems (such as autonomous control systems, robotic control systems, RF spectrum management systems, network resource management systems, storage management systems, data management systems, and others), robotic process automation systems, analytic and modeling systems (such as data visualization systems, clustering systems, similarity analysis systems, random forest systems, physical modeling systems, interaction modeling systems, simulation systems, and many others), entity discovery systems, security systems (such as cybersecurity systems, biometric systems, intrusion detection systems, firewall systems, and others), rules engine systems, workflow automation systems, opportunity discovery systems, testing and diagnostic systems, software image propagation systems, virtualization systems, digital twin systems, IoT monitoring systems, routing systems, switching systems, indoor location systems, geolocation systems, and others.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cook (US 2023/0144325 A1) discloses a method for generating digital twin systems for Multiphysics systems
Patil (US 2023/0062676 A1) discloses a method for mobile robot assembly for unloading parcels
Smith (US 2010/0245105 A1) discloses a method for a transport system evaluator
Sun (US 2022/0374829 A1) discloses a method for warehousing control applied to a warehousing system
Chakrabarti (US 2023/0401518 A1) discloses a method for optimizing warehouse operations
Surjaatmadja (US 2021/0300739 A1) discloses a method for autonomous lifting systems in organized use sites
Balaoro (US 2022/0374855 A1) discloses a method for automatic inventory tracking in brick and mortar stores
Diankov (US 10,369,701 B1) discloses a method for automated package registration systems
Schuitema (US 8,260,574 B1) discloses a method for material handling system and method of diagnosing
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624