DETAILED ACTION
Claims 1-20 are presented for examination.
Claims 1, 8, 15, and 19 have been amended.
This office action is in response to the amendment submitted on 17-Nov-2025.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 29-AUG-2025 has been entered.
Response to Arguments – 35 USC 101
On pgs. 7-12 of the Applicant/Arguments Remarks dated 17/11/2025 (hereinafter ‘Remarks’), Applicant argues the amended claims have overcome the rejection under 35 USC 101. Examiner respectfully disagrees and finds Claim 2 of Example 47 from the July 2024 Subject Matter Eligibility Examples relevant.
The applicant argues on pg. 8 the invention fills in missing gaps in training NN with graph representations instead of original data for security considerations. However, as shown in the 103 rejection the technology of representing data as graphs and using it for training purposes does exist in the field.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Additionally, the applicant argues the claimed steps require an objective function calculated at every step and hence can’t be done in the abstract.
The applicant is reminded that objective functions are evaluations. The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
The applicant on pg. 9-12 argues there are practical applications to the exception in the field of medical coding. However, the claim language is written at a high level of generality and does not reflect any medical application, let alone solving a problem, ‘automating’ a process in the field of medical record processing.
Again these arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments have been fully considered but they are not persuasive. Rejection under 35 USC 101 is maintained.
Response to Arguments – 35 USC 103
On pgs. 12-15 of the Applicant/Arguments Remarks (hereinafter ‘Remarks’), Applicant argues the amended claims overcome the rejection under 35 USC 103 over Cella et al. (US20220108262A1) in view of Iida et al. (US20220253321A1) and further in view of Graph Representation Forecasting referred to as GRF.
In response to the applicant’s argument that Cella and Iida are specific to manufacturing, monitor and control workflow, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). Both Cella and Iida are pertinent references in the field of digital twins and data modeling, whether the data reflects physical machinery or other types of information.
Notwithstanding the aforementioned, the specification of the instant application also provides similar embodiments and examples in the same field as Cella and Lida, industrial IoT, see [0041] and control workflow, such as, [0045] “For example, the digital twins of the present example embodiment, which respectively represent sensor set 1 110 and sensor set 2 112, are configured to produce the outputs of their respective sensor sets. That is, the first digital twin, when receiving one or more inputs, can produce outputs indicating the ambient temperature, ambient lighting, and fan power state for the industrial fan. Similarly, the second digital twin, when receiving one or more inputs, can produce outputs indicating the ambient temperature, ambient lighting, and light power state for the light fixture.”
Applicant's arguments have been fully considered but they are not persuasive. Rejection under 35 USC 103 is maintained.
Claim Objections
Claims 1, 8 and 15 are objected to because of the following informalities: the word ‘date’ is a misspelling of ‘data’. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 15 teach a large volume of data. The specification additionally does not define ‘large volume’ (Specification [0014] “Data management systems, such as Master Data Management (MDM) systems, that organize large amounts of data for organizations often rely on data models that provide underlying definitions and relationships between types of data”). Large is a relative term and is rejected for its lack of clarity.
Claims 2-7, 9-14, and 16-20 are dependents on 1, 8, and 15 respectively and are rejected for the same reason.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: Statutory class – process.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes
“3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).” MPEP § 2106.04(a).
The claims are directed to an abstract idea of data processing and analysis. The claim recites:
generating a set of digital twin replicas, where a digital twin replica of the set of digital twin replicas corresponds to a respective node of the data model;
generating output from said set of digital twin replicas, wherein each digital twin node is directed as an incoming edge input and directed to generate a corresponding output, wherein the output has a singular value or vector denoting a task
utilizing the set of digital twin replicas to generate simulated data corresponding to the types of information represented by the nodes of the data model; and
combining the simulated data generated by the set of digital twin replicas into a combined set of simulated data based, at least in part, on the edges of the data model.
training a neural network using the simulated data combined to predict future values of information represented by the nodes according to relationship between types of information.
Generating, combining, and training are mental processes and mathematical manipulations.
By way of example, one can mentally create a data driven model representing the digital twin from a set of data, mentally manipulate the data, simulate certain actions and predictions, combine it as necessary, update it with production data and train a machine learning model to predict future values.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The additional elements are:
a computer implemented method
receiving a large volume of date and extracting a data model, the data model including nodes representing types of information and edges representing relationships between the types of information;
computer implemented method is mere instructions to apply an exception on a generic computer. MPEP § 2106.05(f).
receiving is an insignificant extra solution activity – mere data collection. MPEP § 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than judicial exception?
No. The additional elements are a generic computer performing conventional functions and mere data gathering.
Claim 2 recites the digital twin replica of the set of digital twin replicas includes a neural architecture comprising a plurality of neurons, which is a mathematical process under Step 2A Prong One.
Claim 3 recites the digital twin replica of the set of digital twin replicas generates, as output, a row of simulated data, which is a mathematical process under Step 2A Prong One.
a neuron of the plurality of neurons generates simulated data corresponding to a respective column of the row of simulated data, which is a mathematical process under Step 2A Prong One.
Claim 4 recites the digital twin replica of the set of digital twin replicas corresponds to a respective table in a master data management (MDM) system, which is a mental process under Step 2A Prong One.
the row of simulated data corresponds to a row of the table, which is a mental process under Step 2A Prong One.
Claim 5 recites evaluating the row of simulated data utilizing a loss minimization based objective function, which is a mathematical process under Step 2A Prong One.
Claim 6 recites training a graph neural network utilizing the combined set of simulated data as training data, which is a mathematical process under Step 2A Prong One.
Claim 7 recites receiving a graph corresponding to the data model, the graph including at least one incomplete type of information, which is mere data gathering under Step 2A Prong Two and Step 2B.
utilizing the trained graph neural network to complete the at least one incomplete type of information, which is a mental/mathematical process under Step 2A Prong One.
Claim 8 recites a computer program product comprising: (statutory category – machine)
one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by one or more computer processors to cause the one or more computer processors to perform a method comprising, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. MPEP § 2106.05(f).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claim 8 is further directed towards signal, per se. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re ZIetz, 893 F.2d 319 (Fed. Cir.1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer-readable medium (also called computer-readable storage and other such variations) typically covers forms of nontransitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable media, particularly when the specification is silent. See MPEP 2111.01.
This rejection may be overcome by specifying “non-transitory computer readable medium.”
Claims 9-14 recite limitations similar to claims 2-7 respectively, and are rejected under the same rationale.
Claim 15 recites a computer system comprising: (statutory category – machine)
one or more computer processors; and
one or more computer readable storage media; wherein:
the one or more computer processors are structured, located, connected and/or programmed to execute program instructions collectively stored on the one or more computer readable storage media; and the program instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform a method comprising, which is mere instructions to apply an exception on a generic computer under Step 2A Prong Two and 2B. MPEP § 2106.05(f).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claims 16-20 recite limitations similar to claims 2-5 and 7 respectively, and are rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8-12 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US20220108262A1) in view of Iida et al. (US20220253321A1).
Regarding Claim 1, Cella teaches a computer-implemented method (claim 1 “A method for configuring role-based digital twins … by the processing system”).
receiving a large volume of data and extracting a data model, the data model including nodes representing types of information and edges representing relationships between the types of information ([0024] “the present disclosure includes instantiating a graph database having a set of nodes connected by edges … with an edge representing a respective relationship between a respective industrial entity” and [5189] “information gathered and generated for industrial machine maintenance lifecycles, including predictive maintenance, manufacturer required maintenance, failure repairs, parts and service offerings and ordering, follow-up to maintenance activities, assessment of procedures and service providers, failure rate and prediction analysis, worker training, experience, and ratings, and the like may be captured throughout the service lifecycle, processed with artificial intelligence and other machine learning-type algorithms and accumulated in a database, such as a data model, linked database, columnar database, and the like. FIG. 315 depicts such a set of data embodied as a knowledge graph 33602”).
generating a set of digital twin replicas, where a digital twin replica of the set of digital twin replicas corresponds to a respective node of the data model ([0024] “wherein a first node of the set of nodes contains data defining the environment 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”).
generating output from said set of digital twin replicas, wherein each digital twin node is directed as an incoming edge input and directed to generate a corresponding output ([0024] “ In embodiments, the present disclosure 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 environment 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 embodiments, each edge represents a relationship between two respective digital twins. In 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 industrial entity represented by the respective discrete digital twin and the industrial environment. In embodiments, each edge represents a spatial relationship between two respective digital twins, and an operational relationship between two respective digital twins. In embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. In embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node. In embodiments, each entity node of the one or more entity nodes includes one or more behaviors of a respective properties of the respective industrial entity represented by the entity node.” [0025] “In embodiments, the present disclosure includes executing a simulation based on the environment digital twin and the one or more discrete digital twins. In embodiments, the simulation simulates one of an operation of a machine in the industrial environment that produces an output based on a set of inputs and movement of workers in the industrial environment”).
wherein the output has a singular value or vector denoting a task ([0047] “the present disclosure includes a method for updating one or more vibration fault level states of one or more digital twins including receiving a request from a client application to update one or more vibration fault level states of one or more digital twins; retrieving the one or more digital twins required to fulfill the request; retrieving one or more dynamic models required to fulfill the request, wherein the one or more dynamic models include a dynamic model that predicts when a vibration fault level occurs based on an input dataset; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating one or more vibration fault level states of the one or more digital twins based on the output of the one or more dynamic models.” [0064] “In embodiments, the vibration fault level states are selected from the set of normal, suboptimal, critical, and alarm.” EN: The output denotes the task of predicting when a fault level occurs. [5486] shows possible parameters including singular values and vectors).
utilizing the set of digital twin replicas to generate simulated data corresponding to the types of information represented by the nodes of the data model ([0025] “the present disclosure includes executing a simulation based on the environment digital twin and the one or more discrete digital twins. In embodiments, the simulation simulates one of an operation of a machine in the industrial environment that produces an output based on a set of inputs and movement of workers in the industrial environment.” The data related to the operation machine is represented by nodes in the data model).
based, at least in part, on the edges of the data model ([6526] “In embodiments, a digital twin model is based on a combination of data and its relationship to the digital twin environments and/or processes” where the edges are the relationships as specified earlier).
training a neural network using the simulated data combined to predict future values of information represented by the nodes according to relationship between types of information ([6546] “the CEO digital twin 60620 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. 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…. the digital twin simulation system 60320 may return the simulation results to the CEO digital twin 60620, 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. 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.” The outcomes are predictions based on the information presented by the edges).
However, Cella doesn’t explicitly teach combining the simulated data generated by the set of digital twin replicas into a combined set of simulated data.
Iida teaches combining the simulated data generated by the set of digital twin replicas into a combined set of simulated data (Fig. 3c shows the digital twin replicas being combined into a set, and [0020] “The digital twin computing apparatus 10 according to the embodiment of the present invention can arrange a virtual DT in a virtual space-time called a sandbox, and realize various simulations”).
Cella and Iida are analogous arts because they are from the same field of endeavor in digital twin modeling and simulation. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Cella and Iida to benefit from combining various simulated data to generate more predictive models with wider capabilities (Iida [0004] “fusing digital twins … and the like, to expand digital twins of objects and living creatures that do not actually exist.” [0005] “It is thought that by expanding the target range of digital twins to objects and living creatures that do not actually exist and performing simulation and the like using these digital twins, it is possible to assist new value creation, problem solving of various problems, and the like”).
Regarding Claim 2, Cella in view of Iida teaches the method of claim 1. Cella further teaches the digital twin replica of the set of digital twin replicas includes a neural architecture comprising a plurality of neurons ([5573] “the machine learning model 55052 may be and/or include an artificial neural network, e.g. a connectionist system configured to “learn” ... The machine learning model 55052 may be based on a collection of connected units and/or nodes that may act like artificial neurons that may in some ways emulate neurons in a biological brain. The units and/or nodes may each have one or more connections to other units and/or nodes” ).
Regarding Claim 3, Cella in view of Iida teaches the method of claim 2. Cella further teaches the digital twin replica of the set of digital twin replicas generates, as output, a row of simulated data ([5563] “The artificial intelligence system 55050 may also define the digital twin system 55070 to create a digital replica of one or more of the manufacturing entities 55010… The digital replica of the one or more of the manufacturing entities may use … and provides for simulation of one or more possible future states of the one or more manufacturing entities. ... The digital replica provides one or more simulations of both physical elements and properties of the one or more manufacturing entities” and [5579] “Each of the training examples may be represented in the machine learning model AIDLT102 by an array and/or a vector, i.e. a feature vector. The training data may be represented in the machine learning model 55052 by a matrix”).
a neuron of the plurality of neurons generates simulated data corresponding to a respective column of the row of simulated data ([5189] “information gathered and generated for industrial machine maintenance lifecycles, including predictive maintenance…prediction analysis, worker training, experience, and ratings, and the like may be … processed with artificial intelligence and other machine learning-type algorithms and accumulated in a database, such as a data model, linked database, columnar database, and the like”).
Regarding Claim 4, Cella in view of Iida teaches the method of claim 3. Cella further teaches the digital twin replica of the set of digital twin replicas corresponds to a respective table ([6520] “a digital twin data model may define a set of concepts (e.g., entities, attributes, relations, tables, and/or the like) used in defining such formalizations of data or metadata within the environment. For example, a “digital twin data model” used in connection with a banking application may be defined using the entity-relationship “data model” and how it is then related to the various executive digital twin views”).
in a master data management (MDM) system (Fig 32 5700 the master raw data server implements the MDM system. And [0809] “FIG. 32 is a diagrammatic view of components and interactions of a data collection architecture involving a master raw data server that processes new streaming data and data already extracted and processed in accordance with the present disclosure”).
the row of simulated data corresponds to a row of the table ([5148] an exemplary high level structure 32700 of a portion of such an RFID is presented and includes rows and columns.” and [5389] “the digital twin simulation system 40006 may iteratively adjust one or more parameters of a digital twin and/or one or more embedded digital twins” where [5432] “the digital twin will collect and/or store RFID data from RFID sensors within or associated with the corresponding environment 40020”).
Regarding Claim 5, Cella in view of Iida teaches the method of claim 3. Cella further teaches evaluating the row of simulated data utilizing a loss minimization based objective function ([5579] “The machine learning model 55052 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs. Once optimized, the objective function may provide the machine learning model 55052 with the ability to accurately determine an output for inputs other than inputs included in the training data.” Iterative optimization is the function to achieve loss minimization).
Regarding Claim 8, Cella teaches a computer program product comprising one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by one or more computer processors to cause the one or more computer processors to perform a method ([2666] “The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Regarding Claim 15, Cella teaches a computer system comprising: one or more computer processors; and one or more computer readable storage media; wherein: the one or more computer processors are structured, located, connected and/or programmed to execute program instructions collectively stored on the one or more computer readable storage media; and the program instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform a method comprising ([2666] “The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claims 9 and 16, 10 and 17, 11 and 18, and 12 and 19 are similar to claims 2, 3, 4, and 5 respectively and are rejected under the same rationale.
Claims 6, 7, 13, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US20220108262A1) in view of Iida (US20220253321A1) and further in view of Graph Representation Forecasting referred to as GRF.
Regarding Claim 6, Cella in view of Iida teaches the method of claim 1. Cella further teaches training utilizing the combined set of simulated data as training data ([5189] “information gathered and generated for industrial machine maintenance lifecycles, including predictive maintenance, manufacturer required maintenance, failure repairs, parts and service offerings and ordering, follow-up to maintenance activities, assessment of procedures and service providers, failure rate and prediction analysis, worker training, experience, and ratings, and the like may be captured throughout the service lifecycle, processed with artificial intelligence and other machine learning-type algorithms and accumulated in a database”).
However, Cella and Iida don’t teach training a graph neural network.
GRF teaches training a graph neural network (3.2.2 training “In a GNN-based model, each node learns a latent representation of the state using the messages received from its neighborhood…”).
Cella, Iida and GRF are analogous arts because they are from the same field of endeavor in digital twin modeling and simulation. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Cella, Iida and GRF to augment the standard neural networks to graph neural networks as “GNNs have started drawing the attention of both research and industry communities (Bronstein et al., 2017; Zhou et al., 2018). Such models are much more interpretable with respect to other neural approaches thanks to their graph structure, which is quite easy to understand from a human standpoint,” (Page 4, Line 1-6).
Regarding Claim 7, Cella in view of Iida and further in view of GRF teaches the method of claim 6. Cella further teaches receiving a graph corresponding to the data model, the graph including at least one incomplete type of information ([5190] “Relationships among data nodes, such as a relationship between the machine data node 33608 and the service data node 33612 may be depicted as the links 33616 between nodes. A goal of initiating and updating such a knowledge graph, among other things may be to further improve for collecting, discovering, capturing, disseminating, managing, and processing information about industrial machines, including factual information (such as about internal structures, parts and components), operational information and procedural information, including know-how and other information relevant to maintenance, service and repairs,” and [5191] “In embodiments, as maintenance/service/repair/upgrade/installation and other industrial machine-related activities are performed, data about the activities may be processed and used to enhance, augment, improve, refine, clarify, and correct the data nodes 33618, the relationships among the nodes, and the like” discovering is the process of assigning values to missing information. Correcting is the process of assigning accurate values to incorrect or missing values).
GRF teaches utilizing the trained graph neural network to complete the at least one incomplete type of information (3.2.2 training “In a GNN-based model, each node learns a latent representation of the state using the messages received from its neighborhood… the model estimates a 95% confidence interval of the evolution of each variable over time.” The trained model iterates to generate incomplete information).
Regarding Claims 13 and 14, Cella in view of Iida teaches the method of claim 8. The remaining limitations are similar to claim 6 and 7 respectively and are rejected under the same rationale.
Regarding Claim 20, Cella in view of Iida teaches the method of claim 1. The remaining limitations are similar to claim 7 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20230098596A1: Discloses digital twins, data models and machine learning in a VR context.
US20220083707A1: Discloses digital twins and data models.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.D./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187