DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant’s election without traverse of invention I (claims 1-23) in the reply filed on 06/11/2026 is acknowledged.
3. Claims 1-23 are presented for examination with claims 24-28 being directed to non-elected invention and withdrawn from consideration.
Claim Rejections - 35 USC § 101
4. 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.
4.1 Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A- Prong One
The claim(s) recite(s) a system and method (claim 1, 13, 20, 22-23) for modeling a production environment, comprising: The step of: “identifying, by a computer system, a knowledge graph for a component in the production environment; and training, by the computer system, a machine learning model to predict a set of attributes for the component using the knowledge graph”, under the broadest reasonable interpretation fall under a mental process. Likewise, the step of: “predicting, by the computer system, the set of attributes for the components in the production environment using the machine learning models trained using the set of knowledge graphs” (claims 8, 10, 20, 23), under the broadest reasonable interpretation further fall under a mental process. Therefore, the claims are directed to an abstract idea, by use of generic computer components and thus are clearly directed to an abstract idea, as constructed.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the additional limitation such as: “a computer system; a model manager”; “a computer-readable medium”, “program instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0077-0079], and fig.2) which can be of any type, including general-purpose computer (para [0079]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; are well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as previously discussed above with reference to the integration of abstract idea into a practical application, the additional elements of: “a computer system; a model manager”; “a computer-readable medium”, “program instructions”, either alone or in combination, all serve to gather and process data and do not add anything more significantly to the judicial exception, but are mere instructions to apply the exception using a generic computer component that are well known, routine, and conventional activities (see specification at para [0077-0079], and fig.2) which can be of any type, including general-purpose computer (para [0079]) previously known in the industries. Merely adding a programmable computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice, 573 U.S. at 223-24. Furthermore, the use of a general-purpose computer to apply an otherwise ineligible algorithm does not qualify as a particular machine. See Ultramerciallnc. v. Hulu, LLC, 772F.3d 709, 716-17 (Fed. Cir. 20l4); In re TLI Commc 'ns LLC v. AV Automotive, LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept); Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785; are well-known, routine and conventional activities and are not sufficient to amount to significantly more than the judicial exception (See further MPEP 2106.05(d)(i-iv)-f); thus are not patent eligible under 35 USC 101. Therefore, using computer components amount to no more than mere instructions to perform the abstract, and thus are not sufficient to amount to significantly more than the recited abstract, as constructed.
4.2 Dependent claims 2-7, 9, 11-12, 14-19, 21 merely include limitations pertaining to further mathematical computations (claim 2), “predicting, by the computer system, the set of attributes for the component in the production environment using the machine learning model trained to predict the set of attributes using the knowledge graph” (mental process). (claim 3, 15); “receiving, by the computer system, sensor data from the component in the production environment; sending, by the computer system, the sensor data as an input to the machine learning model; and receiving, by the computer system, an output predicting the set of attributes for the component in response to sending the sensor data as the input to the machine learning model” (data gathering and processing or otherwise a mental process); (claims 4, 16); “wherein training, by the computer system, the machine learning model using the knowledge graph comprises: selecting, by the computer system, the set of attributes; sending, by computer system, inputs into the knowledge graph for the component in the production environment; receiving, by the computer system, outputs for set of attributes generated in response to sending the inputs into the knowledge graph; creating, by the computer system, a training dataset comprising the inputs and the outputs for the set of attributes; and training, by the computer system, the machine learning model using the training dataset” (data gathering and processing and/or otherwise mental process); (claims 5, 17); “wherein the knowledge graph is derived from an ontology of a production process in the component” (mental process and/or mathematical concept); (claim 6, 18) “wherein the component is one of a production facility, a manufacturing facility, a chemical plant, a refinery, oil well, an integrated circuit manufacturing plant, a chemical refinery, a petroleum refinery, a power plant, an oil well, a gas well, a chip fabrication plant, and an aircraft manufacturing facility” (mental process); (claim 7, 19); “wherein the machine learning model is a digital twin for the component in the production environment” (mental process); (claims 9, 21) “receiving, by the computer system, outputs from the machine learning models in response to sending input to the machine learning models; and sending, by the computer system, selected outputs to selected machine learning models that use the selected outputs as inputs to predict the set of attributes” (data gathering and processing or otherwise mental process); (claim 11) “identifying, by the computer system, the knowledge graph for the component in the production environment; and training, by the computer system, the machine learning model to predict the set of attributes for the component using the knowledge graph” (mental process); (claims 12, 14) “predicting, by the computer system, the set of attributes for the component in the production environment using the machine learning model trained using the knowledge graph” (mental process); all of which further amount to further mental process similar to that already recited by the independent claims and already addressed above and thus are further not patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 102
5. 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.
6. Claim(s) 1-6, 8-18, 20-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nixon et al. (USPG_PUB No. 2023/0394045).
6.1 In considering claims 1, 8, 10, 13, 20, 22-23, Nixon et al. teaches a computer implemented method for modeling a production environment, the computer implemented method comprising:
identifying, by a computer system, a knowledge graph for a component in the production environment (see para [0195], The process plant search query server 800 may identify a set of process plant entities for the user. For example, the set of process plant entities may be identified based on the user's organizational role or may be selected by the user. The process plant search query server 800 may then periodically (e.g., every 30 seconds, every minute, every five minutes, etc.) search the knowledge repository 42 for critical or safety-related alarm information related to the identified set of process plant entities. [0255], the process plant search engine 806 may obtain indications of previous states of process plant entities from the knowledge repository 42, automatically identify process parameters associated with each of the process plant entities, and obtain historical process parameter values for the identified process parameters at times corresponding to the previous states. The process plant search engine 806 may also identify related process plant entities and process parameters associated with the related process plant entities, and may obtain historical process parameter values for the related process plant entities at times corresponding to the previous states. [0607] obtain historical process parameter data from a knowledge repository in which process plant-related data that describes attributes of process plant entities in a process plant is organized according to semantic relations between the process plant-related data and the process plant entities); and training, by the computer system, a machine learning model to predict a set of attributes for the component using the knowledge graph (see para [0254] For example, the process plant search engine 806 trains machine learning models using historical process parameter data and/or relationships between process plant entities to predict a state of a particular process plant entity, [0255] The process plant search engine 806 may train a machine learning model using historical process parameter data and/or relationships between process plant entities indicated in the knowledge repository 42. For example, to train a machine learning model for predicting a state of a process plant entity, the process plant search engine 806 may obtain indications of previous states of process plant entities from the knowledge repository 42). [0607], train a machine learning model to identify an abnormal condition in a process plant entity using (i) the historical process parameter data corresponding to the plurality of process plant entities, (ii) one or more relationships between the plurality of process plant entities indicated in the knowledge repository,). Nixon et al. further teaches the step of predicting, by the computer system, the set of attributes for the components in the production environment using the machine learning models trained using the set of knowledge graphs, of claim 8, 10, 20 (see para [0051], FIG. 26 illustrates example results of a process parameter value prediction using a trained machine learning model; [0276], FIG. 24 depicts an example process plant search query 2400 within an IDE for predicting a process parameter value using a trained machine learning model. [0286] In other implementations, the process plant search engine 806 generates a machine learning model for perform the prediction by obtaining training data for performing the prediction from the knowledge repository 42 and training the machine learning model using the training data.).
6.2 As per claims 2, 12, 14, Nixon et al. teaches the step of predicting, by the computer system, the set of attributes for the component in the production environment using the machine learning model trained to predict the set of attributes using the knowledge graph (see para [0051] FIG. 26 illustrates example results of a process parameter value prediction using a trained machine learning model; [0276], FIG. 24 depicts an example process plant search query 2400 within an IDE for predicting a process parameter value using a trained machine learning model. [0286] In other implementations, the process plant search engine 806 generates a machine learning model for perform the prediction by obtaining training data for performing the prediction from the knowledge repository 42 and training the machine learning model using the training data.).
6.3 Regarding claims 3, 15, Nixon et al. teaches the step of receiving, by the computer system, sensor data from the component in the production environment, sending, by the computer system, the sensor data as an input to the machine learning model (see para [0385], The script may load and initialize the trained model, retrieve recent time-series parameters, clean the data (e.g., remove outliers), use the machine learning model to predict the soft-sensor value(s), store the soft-sensor values for later visualization, and, in embodiments in which the soft-sensor values are part of one or more control loops, write the soft-sensor values back to the controller.); and receiving, by the computer system, an output predicting the set of attributes for the component in response to sending the sensor data as the input to the machine learning model (see para [0387] In embodiments, the method 3250 may also include inputting, in real-time, into a trained AI model, the time-series data from the one or more process controllers and the context data and receiving as output from the trained AI model a real-time prediction of product quality based on the real-time, time-series data.).
6.4 As per claim 4, 16, Nixon et al. teaches that wherein training, by the computer system, the machine learning model using the knowledge graph comprises: selecting, by the computer system, the set of attributes (see para [0195], The process plant search query server 800 may identify a set of process plant entities for the user. For example, the set of process plant entities may be identified based on the user's organizational role or may be selected by the user); send inputs into the knowledge graph for the production environment (see para assigning, by a computing device, categories to process parameters in a knowledge repository in which process plant-related data that describes attributes of process plant entities in a process plant is organized according to semantic relations between the process plant-related data and the process plant entities; [0533], receiving, at the computing device, a process plant search query from a user related to one of the categories assigned to process parameters within the process plant); receive outputs for set of attributes generated in response to sending the inputs into the knowledge graph (see para [0533], receiving, at the computing device, a process plant search query from a user related to one of the categories assigned to process parameters within the process plant); create a training dataset comprising the inputs and the outputs for the set of attributes (see para [0286] In other implementations, the process plant search engine 806 generates a machine learning model for perform the prediction by obtaining training data for performing the prediction from the knowledge repository 42 and training the machine learning model using the training data.); and train the machine learning model using the training dataset (see para [0286] the process plant search engine 806 generates a machine learning model for perform the prediction by obtaining training data for performing the prediction from the knowledge repository 42 and training the machine learning model using the training data.)..
6.5 With regards to claims 5, 17, Nixon et al. teaches that wherein the knowledge graph is derived from an ontology of a production process in the component (see para [0328] While FIG. 29 illustrates a few examples of process plant equipment and respective batch information for the batch processes executed by the process plant equipment in the contextual knowledge repository 49, this is for ease of illustration only. The contextual knowledge repository 49 may include any suitable number of pieces of process plant equipment each associated with any suitable number of batch processes. [0329] FIG. 30 illustrates another example portion of the contextual knowledge repository 49 that includes process plant equipment, equipment utilization information for the process plant equipment, and batch information for the batch processes executed by the process plant equipment. The contextual knowledge repository 49 depicted in FIG. 30 may be a detailed portion of a larger contextual knowledge repository 49 depicted in FIG. 7 and described above which includes additional relationships, process plant entities, and process plant-related data. [0386] The context data obtained at block 3252 may be in the form of a graph database (e.g., the graph database 53), and may represent the relationships between the process control field devices and the process control configuration, the relationships between two or more control loops executed by the process controllers, or both.).
6.6 As per claims 6, 18, Nixon et al. teaches that wherein the component is one of a production facility, a manufacturing facility, a chemical plant, a refinery, oil well, an integrated circuit manufacturing plant, a chemical refinery, a petroleum refinery, a power plant, an oil well, a gas well, a chip fabrication plant, and an aircraft manufacturing facility (see para [0093], supply chain data, data related to the material properties of materials in the process plant 5, data from chemical feed tanks, etc.).
6.7 As per claims 9, 21, teaches the step of receiving, by the computer system, outputs from the machine learning models in response to sending input to the machine learning models (see para [0387] In embodiments, the method 3250 may also include inputting, in real-time, into a trained AI model, the time-series data from the one or more process controllers and the context data and receiving as output from the trained AI model a real-time prediction of product quality based on the real-time, time-series data. Based on the real-time prediction of product quality output from the trained AI model, the method 3250 may also include adjusting one or more of the plurality of production parameters, which, in turn, may cause the controller to change a value of the one or more production parameters. Alternatively, the trained AI model may output a recommendation of one or more parameters of the plurality of production parameters to adjust.); and sending, by the computer system, selected outputs to selected machine learning models that use the selected outputs as inputs to predict the set of attributes (see para [0139], In some situations, at least some of the knowledge access queries may be conditionally applied to obtain desired information. For example, a first knowledge access query may obtain information indicative of a yield of a certain range, and a second knowledge access query may obtain information indicative of different operator shifts during which the yield of the certain range was achieved. In some situations, the initial output of multiple repository access queries may be collectively processed to obtain the desired information. [0387] In embodiments, the method 3250 may also include inputting, in real-time, into a trained AI model, the time-series data from the one or more process controllers and the context data and receiving as output from the trained AI model a real-time prediction of product quality based on the real-time, time-series data. Based on the real-time prediction of product quality output from the trained AI model, the method 3250 may also include adjusting one or more of the plurality of production parameters, which, in turn, may cause the controller to change a value of the one or more production parameters. Alternatively, the trained AI model may output a recommendation of one or more parameters of the plurality of production parameters to adjust.).
6.8 As per claim 11, Nixon et al. teaches the step of identifying, by the computer system, the knowledge graph for the component in the production environment (see para [0195], The process plant search query server 800 may identify a set of process plant entities for the user. For example, the set of process plant entities may be identified based on the user's organizational role or may be selected by the user. The process plant search query server 800 may then periodically (e.g., every 30 seconds, every minute, every five minutes, etc.) search the knowledge repository 42 for critical or safety-related alarm information related to the identified set of process plant entities. [0255], the process plant search engine 806 may obtain indications of previous states of process plant entities from the knowledge repository 42, automatically identify process parameters associated with each of the process plant entities, and obtain historical process parameter values for the identified process parameters at times corresponding to the previous states. The process plant search engine 806 may also identify related process plant entities and process parameters associated with the related process plant entities, and may obtain historical process parameter values for the related process plant entities at times corresponding to the previous states); and training, by the computer system, the machine learning model to predict the set of attributes for the component using the knowledge graph (see para [0254] For example, the process plant search engine 806 trains machine learning models using historical process parameter data and/or relationships between process plant entities to predict a state of a particular process plant entity, further see [0255] The process plant search engine 806 may train a machine learning model using historical process parameter data and/or relationships between process plant entities indicated in the knowledge repository 42. For example, to train a machine learning model for predicting a state of a process plant entity, the process plant search engine 806 may obtain indications of previous states of process plant entities from the knowledge repository 42).
Claim Rejections - 35 USC § 103
7. 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.
7.0 Claim(s) 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nixon et al. (USPG_PUB No. 2023/0394045), in view of Mukherjee et al. (USPG_PUB No. 2024/0013582).
7.1 Regarding claims 7, 19, Nixon et al. teaches most of the instant invention; however, he does not expressly show that wherein the machine learning model is a digital twin for the component in the production environment. Mukherjee et al. teaches that wherein the machine learning model is a digital twin for the component in the production environment (see para [0002], The computing platform may train, using the historical vehicle information, a digital twin vehicle evaluation engine, configured to model a vehicle based on characteristics of the vehicle using a computer simulation, where: 1) training the digital twin vehicle evaluation engine may configure the digital twin vehicle evaluation engine to output event processing information for the vehicle, and 2) training the digital twin vehicle evaluation engine may include generating a knowledge graph, where each node of the knowledge graph may be a machine learning model and each edge of the knowledge graph may represent relationships between features corresponding to each machine learning model. [0040], In some instances, the digital twin module 112a may include a knowledge graph supported by (and storing relationships between) one or more feature models (which may, in some instances, be machine learning and/or other models). Nixon et al. and Mukherjee et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Mukherjee et al. is similar to that of Nixon et al, Therefore, it would have been obvious to a person of skilled in the art at the time of filing of the applicant’s invention to combine method of Mukherjee et al. with that of Nixon et al. because Mukherjee et al. teaches a model that increases accuracy (see para [0056]).
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
8.1 HAKIMI et al. (USPG_PUB No. 2023/0185997) teaches a method for machine-assisted collaborative product design that method includes training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models.
8.2 Lee et al. (USPG_PUB No. 2023/0152757) teaches systems and methods of managing a building that includes receiving, by a processing circuit, an indication to execute a digital twin, the digital twin including one or more fault detection or diagnostics functions and a virtual representation of a piece of equipment.
9. Claims 1-23 are rejected and this action is non-final. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached at 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 July 9, 2026
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