DETAILED ACTION
The following is a Final Office action. In response to Examiner’s communication of 12/11/25, Applicant, on 4/13/2026, amended claims 1, 7, 8, 15, and 17, cancelled claims 3-6, 11-14, and 19-20, and added new claims 21-29. Claims 1, 2, 7-10, 15-18, and 21-29 are pending in the present application and are under examination on the merit.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 USC 101 rejections of claims 1, 2, 7-10, 15-18, and 21-29 are withdrawn in light of Applicant’s amendments and explanations.
Revised 35 USC 103 rejections of claims 1, 2, 7-10, 15-18, and 21-29 are applied in light of Applicant’s amendments and explanations.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 7-10, 15-18, and 21-29 are rejected under 35 U.S.C. 103 as being unpatentable over WIPO Patent Application Publication Number WO 2021/222384 to Strong Force Intellectual Capital LLC (hereafter referred to as Strong Force) in view of U.S. Patent Application Publication Number 2017/0289187 to Noel et al. (hereafter referred to as Noel).
As per claim 1, Strong Force teaches:
A computer-implemented method for generating a graph data model of supply chain exchanges, the method comprising (Paragraph Number [0393] teaches market feedback factors may be used to optimize various elements of transportation systems as described throughout this disclosure, such as current and predicted pricing and/or cost (e.g., of fuel, electricity and the like, as well as of goods, services, content and the like that may be available along the route and/or in a vehicle), current and predicted capacity, supply and/or demand for one or more transportation related factors (such as fuel, electricity, charging capacity, maintenance, service, replacement parts, new or used vehicles, capacity to provide ride sharing, self-driving vehicle capacity or availability, and the like), and many others).
receiving, from a plurality of sources at a computer system, sensor data, wherein the plurality of sources comprise at least one database and at least one sensor, wherein each piece of the sensor data comprises information associated with a supply chain exchange defining a product transfer between two supply chain entities (Paragraph Number [0865] teaches depending on the type of transportation system, the types of objects, devices, and sensors that are found in the environments will differ. Non-limiting examples of physical objects 222 include raw materials, manufactured products, excavated materials, containers (e.g., boxes, dumpsters, cooling towers, ship funnels, vats, pallets, barrels, palates, bins, and the like), furniture (e.g., tables, counters, workstations, shelving, etc.), and the like. Non-limiting examples of devices 265 include robots, computers, vehicles (e.g., cars, trucks, tankers, trains, forklifts, cranes, etc.), machinery /equipment (e.g., tractors, tillers, drills, presses, assembly lines, conveyor belts, etc.), and the like. The sensors 227 may be any sensor devices and/or sensor aggregation devices that are found in a sensor system 25 within a transportation system. Non-limiting examples of sensors 227 that may be implemented in a sensor system 25 may include temperature sensors 231, humidity sensors 233, vibration sensors 235, LIDAR sensors 238, motion sensors 239, chemical sensors 241, audio sensors 243, pressure sensors 253, weight sensors 254, radiation sensors 255, video sensors 270, wearable devices 257, relays 275, edge devices 277, switches 278, infrared sensors 297, radio frequency (RF) Sensors 215, Extraordinary Magnetoresistive (EMR) sensors 280, and/or any other suitable sensors" §816; "the digital twin creation module 264 may create a graph database. Paragraph Number [0867] teaches the digital twin update module 266 receives sensor data from a sensor system 25 of a transportation system and updates the status of the digital twin of the transportation system and/or digital twins of any affected systems, subsystems, devices, workers, processes (See also Paragraph Number [0911])).
[determining] via at least one processor of the computer system, the sensor data to identify components of each piece of the sensor data, resulting in parsed sensor data (Paragraph Number [0867] teaches the digital twin 1/0 system 204 may receive the sensor data in one or more sensor packets. The digital twin 1/0 system 204 may provide the sensor data to the digital twin update module 266 and may identify the environment from which the sensor packets were received and the sensor that provided the sensor packet. In response to the sensor data, the digital twin update module 266 may update a state of one or more digital twins based on the sensor data. In response to the sensor data, the digital twin update module 266 may update a state of one or more digital twins based on the sensor data).
resolving, via the at least one processor, missing data within the parsed sensor data, resulting in parsed, resolved sensor data, wherein resolving the missing data comprises: (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor. Paragraph Number [0765] teaches a plurality of streams of vehicle-related data from multiple data sources is received by the digital twin 60136. This includes vehicle specifications like mechanical properties, data from maintenance records, operating data collected from the sensors 60112, historical data including failure data from multiple vehicles running at different times and under different operating conditions and so on. At 61004, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the vehicle 60104 at the time of collection of data).
identifying portions of the parsed sensor data that are missing values for one or more data fields (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor. Paragraph Number [0765] teaches a plurality of streams of vehicle-related data from multiple data sources is received by the digital twin 60136. This includes vehicle specifications like mechanical properties, data from maintenance records, operating data collected from the sensors 60112, historical data including failure data from multiple vehicles running at different times and under different operating conditions and so on. At 61004, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the vehicle 60104 at the time of collection of data).
filling in the missing values by traversing a previously stored graph data model to identify relationships between entities therein and infer the missing values (Paragraph Number [0808] teaches the machine learning model 65102 may be defined via semi- supervised learning, i.e. one or more algorithms using training data wherein some training examples may be missing training labels. The semi-supervised learning may be weakly supervised learning, wherein the training labels may be noisy, limited, and/or imprecise. The noisy, limited, and/or imprecise training labels may be cheaper and/or less labor intensive to produce, thus allowing the machine learning model 65102 to train on a larger set of training data for less cost and/or labor. Paragraph Number [0812] teaches the machine learning model 65102 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 65102, 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.” Paragraph Number [0747] teaches the digital twin 60136 may check for compatibility between one or more components selected by the dealer 60702 with the model of the vehicle 60104 using a set of predefined database of valid relationships).
resolving timing differences between pieces of the parsed sensor data by aligning the pieces of parsed sensor data to a common timestamp (Paragraph Number [0835] teaches if a product was produced on an assembly line, one relationship that may be documented between a digital twin of the product and the assembly line may be “created by”. In these embodiments, an example edge representing the “created by” relationship may include a timestamp indicating a date and time that the product was created. In another example, a sensor may take measurements relating to a state of a device, whereby one relationship between the sensor and the device may include “measured” and may define a measurement type that is measured by the sensor. In this example, the metadata stored in an edge may include a list of N measurements taken and a timestamp of each respective measurement. (See also Paragraph Number [0849] teaching more information about aligning data based on timestamped information)).
mapping, via the at least one processor of the computer system, the parsed, resolved sensor data to a graph data structure, the graph data structure comprising nodes and edges, wherein each node and each edge of the graph data structure comprises metadata associated with the exchange (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor and may update a record (e.g., a node in a graph database) to reflect the current sensor data. Paragraph Number [0866] teaches "the digital twin creation module 264 may create a node for the respective entity and may include any relevant data. For example, the digital twin creation module 264 may create a node representing a machine in the environment. In this example, the digital twin creation module 264 may include the dimensions, behaviors, properties, location, and/or any other suitable data relating to the machine in the node representing the machine. The digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. In representing a process, the digital twin creation module 264 may create a node for the entire process or may create a node for each step in the process. In some of these embodiments, the digital twin creation module 264 may relate the process nodes to the nodes that represent the machinery/devices that perform the steps in the process. In embodiments where an edge connects the process step nodes to the machinery /device that performs the process step, the edge or one of the nodes may contain information that indicates the input to the step, the output of the step, the amount of time the step takes, the nature of processing of inputs to produce outputs, a set of states or modes the process can undergo)
the nodes represent the supply chain exchange and corresponding entities, the nodes comprising an exchange node, an exchange location node, a supplier node, a product node, a customer node, and a sales contract and terms node (Paragraph Number [0837] teaches an enterprise node. Paragraph Number [0836] teaches a node representing the manufacturing environment ... nodes representing an HV AC system, a lighting system, a manufacturing system ... subsystem node representing a heating system of the facility, a third subsystem node representing the fan system of the facility, and one or more nodes representing a thermostat of the facility (or multiple thermostats) ... nodes representing various sensors (e.g., temperature sensors, humidity sensors, and the like)" §836; Paragraph Number [0845] teaches "a node representing a robot. Paragraph Number [0845] teaches the transportation system node. Paragraph Number [0866] teaches the digital twin creation module 264 may create a node for the entire process or may create a node for each step in the process. (Examiner asserts that a skilled person could create any kind of node which derives from the business they want to represent in a digital manner)).
the relationship edges represent relationships between the nodes for each piece of the parsed, resolved sensor data, thereby representing the supply chain exchange in the graph data structure (Paragraph Number [0747] teaches the digital twin 60136 may check for compatibility between one or more components selected by the dealer 60702 with the model of the vehicle 60104 using a set of predefined database of valid relationships. Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. Paragraph Number [0099] teaches the method further includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the transportation system digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In some embodiments, each edge represents a relationship between two respective digital twins. In some of these embodiments embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective transportation entity represented by the respective discrete digital twin and the transportation system. In some embodiments, each edge represents a spatial relationship between two respective digital twins. In some embodiments, each edge represents an operational relationship between two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins).
the affinity edges self-reference nodes and store metadata characterizing similarity or proximity between the supply chain exchange or entity represented by the self-referenced node and other supply chain exchanges or entities of the same node type (Paragraph Number [0099] teaches the method further includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the transportation system digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In some embodiments, each edge represents a relationship between two respective digital twins. In some of these embodiments embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective transportation entity represented by the respective discrete digital twin and the transportation system. In some embodiments, each edge represents a spatial relationship between two respective digital twins. In some embodiments, each edge represents an operational relationship between two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge).
storing the graph data structure in a graph database as a graph data model for the supply chain. (Paragraph Number [0867] teaches the digital twin update module 266 may update a record (e.g., a node in a graph database) corresponding to the sensor that provided the sensor data to reflect the current sensor data. Paragraph Number [0865] teaches the digital twin creation module 264 may create a graph database that defines the relationships between a set of digital twins. In these embodiments, the digital twin creation module 264 may create nodes for the environment, systems and subsystems of the transportation system, devices in the environment, sensors in the environment, workers that work in the environment, processes that are performed in the environment, and the like. In embodiments, the digital twin creation module 264 may write the graph database representing a set of digital twins to the digital twin datastore 269).
wherein the graph database stores relationships between nodes as edges to enable queries of the graph data model to search for particular types of relationships between particular nodes (Paragraph Number [0806] teaches once optimized, the objective function may provide the machine learning model 65102 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 65102 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 65102, a user and/or an information source to label new data points with desired outputs. Paragraph Number [0867] teaches the digital twin update module 266 may update a record (e.g., a node in a graph database) corresponding to the sensor that provided the sensor data to reflect the current sensor data. (See also Paragraph Numbers [0099], [0747], and [0866])).
Strong Force teaches gathering data and creating graph models with the data but does not explicitly teach parsing sensor data to determine associations as described by the following citations from Noel:
parsing, via at least one processor of the computer system, the sensor data (Paragraph Number [0068] teaches with a priori knowledge of all of the possible sensor inputs (relevant for example to the data model of FIG. 2), the system 100 of FIG. 1 can implement one or more algorithms to parse the received data and convert it into a common data format that is compatible with a graph database. As an example, the ingested data 1002 can be parsed so as to provide data to build a multitude of nodes 1004 and relationships 1006 that will eventually be converted into nodes and edges of a graph database. The ingested data 1002 from the sensor can be parsed for data related to the nodes 1004 and relationships 1006. As example, the ingested data 1002 can be parsed to provide information about an instance of a node 1004 including attributes such as: unique identified (UID) 1008, time 1010, name 1012, type 104, as well as various properties 1016, defined by key 1010 and value 1020. An algorithm can be programmed such that the ingested data is parsed to provide the above values).
Both Strong Force and Noel are directed to generating graphical models. Strong Force discloses gathering data and creating graph models with the data. Noel improves upon Strong Force by disclosing parsing sensor data to determine associations. One of ordinary skill in the art would be motivated to further include parsing sensor data to determine associations, to efficiently determine and utilize associations between gathered data so as to provide that information in a graphical visualization. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method of gathering data and creating graph models with the data in Strong Force to further utilize parsing sensor data to determine associations as disclosed in Noel, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, Strong Force teaches:
A system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: (Paragraph Number [0997] teaches the term system may define any combination of one or more computing devices, processors, modules, software, firmware, or circuits that operate either independently or in a distributed manner to perform one or more functions. A system may include one or more subsystems. Paragraph Number [0998] teaches various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. Paragraph Number [0999] teaches the computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 17, Strong Force teaches:
A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising: (Paragraph Number [0997] teaches the term system may define any combination of one or more computing devices, processors, modules, software, firmware, or circuits that operate either independently or in a distributed manner to perform one or more functions. A system may include one or more subsystems. Paragraph Number [0998] teaches various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. Paragraph Number [0999] teaches the computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media.).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 2, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
further comprising: executing, via the at least one processor, an Artificial Intelligence (AI) algorithm using the graph data structure as an input, wherein the AI algorithm comprises at least one of: Centrality, Community Analysis, Graph Machine Learning and Embeddings, Path Optimization, Classification Analysis, Similarity Analysis, Topological Link Prediction, and Frequent Pattern Mining (Paragraph Number [0845] teaches in scenarios where the digital twin system 200 is providing data representations of digital twins (e.g., for dynamic modeling, simulations, machine learning), the digital twin system 200 may traverse a graph database and may determine a configuration of the transportation system to be depicted based on the nodes in the graph database that are related (either directly or through a lower level node) to the transportation system node of the transportation system and the edges that define the relationships between the related nodes. In some scenarios, the digital twin system 200 may receive real-time sensor data from a sensor system 25 of a transportation system 11 and may apply one or more dynamic models to the digital twin based on the sensor data. Paragraph Number [0854] teaches a machine learned prediction model may be used to predict the cause of irregular vibrational patterns (e.g., a suboptimal, critical, or alarm vibration fault state) for a bearing of an engine in a transportation system).
As per claims 10 and 18, the combination of Strong Force and Noel teaches each of the limitations of claims 9 and 17 respectively. Additionally, the claims 10 and 18 recite claim language that is substantially similar to language contained in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claims 7 and 15, the combination of Strong Force and Noel teaches each of the limitations of claims 1 and 9 respectively.
In addition, Strong Force teaches:
wherein the edges further identify at least one self-referencing relationship (Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. (Examiner notes that the self-reference edges in the graph theory was part of the common general knowledge of the skilled person in the art of data processing. They would use the common general knowledge if the business they wants to represent in a digital manner has self-reference relationships)):
As per claims 8 and 16, the combination of Strong Force and Noel teaches each of the limitations of claims 1 and 9, respectively.
In addition, Strong Force teaches:
further comprising: retrieving, at the computer system from the graph database, the graph data structure and a plurality of additional graph data structures, resulting in graph data (Paragraph Number [0873] teaches it is noted that while a graph database is discussed, the digital twin system 200 may employ other suitable data structures to store information relating to a set of digital twins. In these embodiments, the data structures, and any related storage system, may be implemented such that the data structures provide for some degree of feedback loops and/or recursion when representing iteration of flows).
executing, via the at least one processor, a machine learning algorithm using the graph data, wherein output of the machine learning model comprises a pattern between relationships of nodes and edges within the graph data (Paragraph Number [0845] teaches in scenarios where the digital twin system 200 is providing data representations of digital twins (e.g., for dynamic modeling, simulations, machine learning), the digital twin system 200 may traverse a graph database and may determine a configuration of the transportation system to be depicted based on the nodes in the graph database that are related (either directly or through a lower level node) to the transportation system node of the transportation system and the edges that define the relationships between the related nodes. In some scenarios, the digital twin system 200 may receive real-time sensor data from a sensor system 25 of a transportation system 11 and may apply one or more dynamic models to the digital twin based on the sensor data. Paragraph Number [0854] teaches a machine learned prediction model may be used to predict the cause of irregular vibrational patterns (e.g., a suboptimal, critical, or alarm vibration fault state) for a bearing of an engine in a transportation system).
communicating, from the computer system to a remote computing device, the pattern (Paragraph Number [1010] teaches the server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs).
As per claim 21, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
further comprising, prior to the parsing, normalizing the sensor data from the plurality of sources into a common format (Paragraph Number [0386] teaches a data processing system 758, which may process data from various sources, including social media data sources 769, weather data sources 770, road profile sources 771, traffic data sources 772, media data sources 773, sensors sets 774, and many others. The data processing system may be configured to extract data, transform data to a suitable format (such as for use by an interface system, an AI system/expert system, or other systems), load it to an appropriate location, normalize data, cleanse data, deduplicate data, store data (such as to enable queries) and perform a wide range of processing tasks as described throughout this disclosure)
As per claim 22, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the at least one sensor comprises: at least one physical sensor to track objects associated with supply chain exchanges (Paragraph Number [0828] teaches the digital representation may include a set of data structures (e.g., classes) that collectively define a set of properties of a represented physical object 222, device 265, sensor 227, or transportation system 11 and/or possible behaviors thereof. For example, the set of properties of a physical object 222 may include a type of the physical object, the dimensions of the object, the mass of the object, the density of the object, the material(s) of the object, the physical properties of the material(s), the surface of the physical object, the status of the physical object, a location of the physical object, identifiers of other digital twins contained within the object, and/or other suitable properties).
at least one virtual sensor configured to monitor a database or data lake for changes associated with supply chain exchanges (Paragraph Number [0732] teaches digital twin 60136 of the vehicle 60104 is a virtual replication of hardware, software, and processes in the vehicle 60104 that combines real-time and historical operational data and includes structural models, mathematical models, physical process models, software process models, etc. In embodiments, digital twin 60136 encompasses hierarchies and functional relationships between the vehicle and various components and subsystems and may be represented as a system of systems. Paragraph Number [0791] teaches the machine learning model 65102 and the digital twin system 65330 may process sensor data and create a digital replica of a set of transportation entities of the plurality of transportation entities to facilitate design, real-time simulation, predictive simulation, and/or hypothetical simulation of a related group of transportation entities. The digital replica of the set of transportation entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the set of transportation entities and provide for simulation of one or more possible future states of the set of transportation entities. Paragraph Number [0940] teaches a client application 217 may be an application relating to a component or system (e.g., monitoring a transportation system or a component within, simulating a transportation system, or the like). In embodiments, the client application 217 may be used in connection with both fixed and mobile data collection systems. In embodiments, the client application 217 may be used in connection with network connected sensor system 25).
As per claim 23, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the supply chain exchange comprises a transfer of one or more products from the supplier node to the customer node at the exchange location node under terms specified by the sales contract and terms node (Paragraph Number [0683] teaches an interface 56133 may be configured to provide a host, manager, operator, service provider, vendor, or other entity interacting within or with a transportation system 5611 with the ability to review a range of models, expert systems 5657, neural network categories, and the like. Such an interface 56133 may allow a user 5690 to configure one or more parameters of an expert system 5657, such as one or more input data sources to which a model is to be applied and/or one or more inputs to a neural network, one or more output types, targets, durations, or purposes, one or more weights within a model or an artificial intelligence system, one or more sets of nodes and/or interconnections within a model, graph structure, neural network, or the like, one or more time periods of input, output, or operation, one or more frequencies of operation, calculation, or the like, one or more rules (such as rules applying to any of the parameters configured as described herein or operating upon any of the inputs or outputs noted herein), one or more infrastructure parameters (such as storage parameters, network utilization parameters, processing parameters, processing platform parameters, or the like). Paragraph Number [0837] teaches where a graph database is implemented, a graph database may relate to a single environment, transportation entity or transportation system or may represent a larger enterprise. In the latter scenario, a company may have various manufacturing and distribution facilities, as well as transportation entities and systems. In these embodiments, an enterprise node representing the enterprise may connect to transportation system nodes of each respective facility. In this way, the digital twin system 200 may maintain digital twins for multiple facilities, and transportation systems of an enterprise. Paragraph Number [0866] teaches the digital twin creation module 264 may relate the process nodes to the nodes that represent the machinery/devices that perform the steps in the process. In embodiments where an edge connects the process step nodes to the machinery /device that performs the process step, the edge or one of the nodes may contain information that indicates the input to the step, the output of the step, the amount of time the step takes, the nature of processing of inputs to produce outputs, a set of states or modes the process can undergo, and the like).
As per claim 24, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the affinity edges comprise at least one of: a supplier affinity edge that stores similarity between suppliers based on overlap in supplied products and customers; a product affinity edge that stores similarity between products based on product catalog attributes; a customer affinity edge that stores similarity between customers based on industry segment or purchase behavior; a contract affinity edge that stores similarity between sales contracts and terms based on contract types; and an exchange affinity edge that stores similarity between supply chain exchanges based on exchange history (Paragraph Number [0099] teaches the method further includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the transportation system digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In some embodiments, each edge represents a relationship between two respective digital twins. In some of these embodiments embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective transportation entity represented by the respective discrete digital twin and the transportation system. In some embodiments, each edge represents a spatial relationship between two respective digital twins. In some embodiments, each edge represents an operational relationship between two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. (Examiner asserts that this section teaches at least the alternative of a supplier affinity edge that stores similarity between suppliers based on overlap in supplied products and customers)).
As per claim 25, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the plurality of sources comprise: a plurality of physical sensors that produce sensor data at a first set of sampling frequencies (Paragraph Number [0828] teaches the digital representation may include a set of data structures (e.g., classes) that collectively define a set of properties of a represented physical object 222, device 265, sensor 227, or transportation system 11 and/or possible behaviors thereof. For example, the set of properties of a physical object 222 may include a type of the physical object, the dimensions of the object, the mass of the object, the density of the object, the material(s) of the object, the physical properties of the material(s), the surface of the physical object, the status of the physical object, a location of the physical object, identifiers of other digital twins contained within the object, and/or other suitable properties).
a plurality of virtual sensors that produce sensor data at a second set of sampling frequencies different from the first set of sampling frequencies (Paragraph Number [0732] teaches digital twin 60136 of the vehicle 60104 is a virtual replication of hardware, software, and processes in the vehicle 60104 that combines real-time and historical operational data and includes structural models, mathematical models, physical process models, software process models, etc. In embodiments, digital twin 60136 encompasses hierarchies and functional relationships between the vehicle and various components and subsystems and may be represented as a system of systems. Paragraph Number [0791] teaches the machine learning model 65102 and the digital twin system 65330 may process sensor data and create a digital replica of a set of transportation entities of the plurality of transportation entities to facilitate design, real-time simulation, predictive simulation, and/or hypothetical simulation of a related group of transportation entities. The digital replica of the set of transportation entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the set of transportation entities and provide for simulation of one or more possible future states of the set of transportation entities. Paragraph Number [0940] teaches a client application 217 may be an application relating to a component or system (e.g., monitoring a transportation system or a component within, simulating a transportation system, or the like). In embodiments, the client application 217 may be used in connection with both fixed and mobile data collection systems. In embodiments, the client application 217 may be used in connection with network connected sensor system 25).
wherein the resolving of the timing differences between pieces of the parsed sensor data comprises aligning sensor data produced by the physical sensors and the virtual sensors to the common timestamp (Paragraph Number [0835] teaches if a product was produced on an assembly line, one relationship that may be documented between a digital twin of the product and the assembly line may be “created by”. In these embodiments, an example edge representing the “created by” relationship may include a timestamp indicating a date and time that the product was created. In another example, a sensor may take measurements relating to a state of a device, whereby one relationship between the sensor and the device may include “measured” and may define a measurement type that is measured by the sensor. In this example, the metadata stored in an edge may include a list of N measurements taken and a timestamp of each respective measurement. (See also Paragraph Number [0849] teaching more information about aligning data based on timestamped information) (See also Paragraph Number [0732[, [0791], [0828], and [0940])).
As per claim 26, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
further comprising: executing, at the computer system, a query of the graph data model to search for particular types of relationships between particular nodes (Paragraph Number [0806] teaches once optimized, the objective function may provide the machine learning model 65102 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 65102 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 65102, a user and/or an information source to label new data points with desired outputs. Paragraph Number [0867] teaches the digital twin update module 266 may update a record (e.g., a node in a graph database) corresponding to the sensor that provided the sensor data to reflect the current sensor data. (See also Paragraph Numbers [0099], [0747], and [0866])).
returning, in response to the query, relationship edges between the nodes that satisfy a relationship criterion (Paragraph Number [0806] teaches the machine learning model 65102 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 65102, a user and/or an information source to label new data points with desired outputs. Statistical classification may include identifying, by the machine learning model 65102, to which a set of subcategories, i.e. subpopulations, a new observation belongs based on a training set of data containing observations having known categories. Regression analysis may include estimating, by the machine learning model 65102 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 65102, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are).
As per claim 27, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the graph database stores the graph data model in memory as a plurality of store files, each store file comprising data for a specific part of the graph data structure, including at least one of: the nodes, the relationship edges, node labels, or attributes of the nodes or the relationship edges (Paragraph Number [0835] teaches a set of two or more digital twins may be represented by a graph database that includes nodes and edges that connect the nodes. In some implementations, an edge may represent a spatial relationship (e.g., “abuts”, “rests upon”, “contains”, and the like). In these embodiments, each node in the graph database represents a digital twin of an entity (e.g., a transportation entity) and may include the data structure defining the digital twin. In these embodiments, each edge in the graph database may represent a relationship between two entities represented by connected nodes. In some implementations, an edge may represent a spatial relationship (e.g., “abuts”, “rests upon”, “interlocks with”, “bears”, “contains”, and the like). In embodiments, various types of data may be stored in a node or an edge. In embodiments, a node may store property data, state data, and/or metadata relating to a facility, system, subsystem, and/or component).
As per claim 28, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein the graph data model treats the relationship edges as first-class citizens such that individual data records represented in the graph data model can be referenced by a key-value pair (Paragraph Number [0835] teaches a set of two or more digital twins may be represented by a graph database that includes nodes and edges that connect the nodes. In some implementations, an edge may represent a spatial relationship (e.g., “abuts”, “rests upon”, “contains”, and the like). In these embodiments, each node in the graph database represents a digital twin of an entity (e.g., a transportation entity) and may include the data structure defining the digital twin. In these embodiments, each edge in the graph database may represent a relationship between two entities represented by connected nodes. In some implementations, an edge may represent a spatial relationship (e.g., “abuts”, “rests upon”, “interlocks with”, “bears”, “contains”, and the like). In embodiments, various types of data may be stored in a node or an edge. In embodiments, a node may store property data, state data, and/or metadata relating to a facility, system, subsystem, and/or component).
As per claim 29, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
wherein queries to and from the graph data model stored in the graph database are computationally simpler than corresponding queries over a tabular data structure storing supply chain exchange data in entity tables with primary and foreign keys (Paragraph Number [0943] teaches the digital twin system 200 may have a digital twin dynamic model datastore 228 for storing dynamic models that may be represented in digital twins. In embodiments, digital twin dynamic model datastore can be searchable and/or discoverable. In embodiments, digital twin dynamic model datastore can contain metadata that allows a user to understand what characteristics a given dynamic model can handle, what inputs are required, what outputs are provided, and the like. In some embodiments, digital twin dynamic model datastore 228 can be hierarchical (such as where a model can be deepened or made more simple based on the extent of available data and/or inputs, the granularity of the inputs, and/or situational factors (such as where something becomes of high interest and a higher fidelity model is accessed for a period of time)).
Response to Arguments
Applicant’s arguments filed 4/13/2026 have been fully considered but they are not persuasive.
Applicant argues that the previously cited references do not teach the newly amended portions including the new limitations recited by the independent claims. (See Applicant’s Remarks, 4/13/2026, pgs. 16-20). Examiner respectfully disagrees. Examiner notes that new citations from the previously cited Strong Force reference have been applied to the newly presented claim limitations as indicated in the above in the revised 103 rejections. As such, Applicant’s arguments directed towards the previous rejection are moot. In response to Applicant’s arguments, Examiner directs Applicant to review the new citations and explanations provided in the new 103 rejections presented above
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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