DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is responsive to application filed on 06/08/2022.
Claims 1-20 are presented for examination.
Specification
The abstract of the disclosure is objected to because The Title “SYSTEM DESIGN BASED ON PROCESS FLOW DIAGRAM INFORMATION EXTRACTION AND GENERATIVE MODELS” may need to be canceled. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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.
Step 1 (Does this claim fall within at least one statutory category?): Yes, the claim recites a series of steps and, therefore, is a process.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A computer-implemented method for automated design of a physical system, the method comprising:
obtaining qualitative and quantitative design requirements associated with the physical system [insignificant extra solution, e.g. mere data-gathering];
inputting the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system, wherein the topology specifies a number of components and connections among the components [[“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion); and
determining parameters of the components based on the quantitative design requirements [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional elements “obtaining” and “trained machine-learning model”. This additional element is insignificant pre-solution (i.e. data gathering). Further, claim recites an additional element of “machine learning model”. The “machine learning model” is used to generally apply the abstract idea without placing any limits on how the machine learning model. The recitation of “using a machine learning model” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a machine learning model” limits the identified judicial exception “generate a topology of the physical system”, this type of limitation merely confines the use of the abstract idea to a particular technology environment (machine learning) and thus fails to add an inventive concept to the claims. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “obtaining” are insignificant pre/post-solutions (i.e. data gathering and/or mere data output). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Further, as explained above with respect to Step 2A, Prong two, the additional element of “using a machine learning model” is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
As per claim 2, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 3, the claim falls into ([insignificant post solution, data output] and/or [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 4, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 5, the claim falls into [mathematical concepts].
As per claim 6, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 7, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 8, the claim falls into generic computer component/machine learning/mathematical concepts.
As per claim 9, the claim falls into [mathematical concepts].
As per claim 10, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 11, independent claim 11 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 11 recites additional elements of “a processor” and “a storage device”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “processors” and “storage medium” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per Claims 12-20, claims 12-20 recite limitations analogous in scope to those of claims 2-10, and as such are similar rejected.
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.
The factual inquiries 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2022/0180024 A1 issued to Matei et al in view of US Publication No. 2022/0171891 A1 issued to Sinha et al.
1. Matei et al discloses a computer-implemented method for automated design of a physical system, the method comprising:
obtaining qualitative and quantitative design requirements associated with the physical system (See: Abstract, A component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received; [0047] all numbers expressing feature sizes, amounts, and physical properties used in the specification and claims are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the foregoing specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by those skilled in the art utilizing the teachings disclosed herein. The use of numerical ranges by endpoints includes all numbers within that range (e.g. 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5) and any range within that range);
determining parameters of the components based on the quantitative design requirements (See: [0017] When considering both discrete components and components with continuous parameters, the design space size is M.sup.C×S, where C is the number of discrete components, M is the number of options per component, and S⊂[AltContent: rect].sup.N with N as the number of continuous parameters. Even when assuming a discretization of continuous parameters, the search space can become unmanageable. Additional complexity comes for the design simulation time T that depends on the model topology, the number of components, and the switching frequency; [0018] The enumeration-based design approach is depicted in FIG. 1A. Based on a library of components 110, all possible designs 120 can be enumerated. For each design, the component parameters can be optimized 130 to satisfy the design criteria 135 to generate design solutions 140. This approach is not be scalable since the number of possible designs is exponential in the number of components).
Matei et al does not specify but Sinha et al discloses inputting the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system, wherein the topology specifies a number of components and connections among the components (See: Abstract, inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines; par [0073] inputting data from various plant engineering data sources, including plant engineering diagrams (e.g., PI&D, PFD, etc.), into the industrial plant control system 300. At block 506, the data is processed to extract relevant information and assets, such as names, numbers, symbols, lines, loops, and the like from the data. The asset extraction is done using machine learning via intelligent process 316 and the ML models therein. Processing the data using the ML-based asset extraction described herein constitutes a practical application (e.g., the design of an HMI for an industrial plant using a specialized automated design system). Processing systems and methods described herein may improve the process of HMI design by permitting an image recognition model that identifies plant components in a diagram to run more efficiently and accurately. This can be achieved by inputting images that were predetermined by the pre-processing module as likely to lead to a positive identification by the image recognition ML model. This may also permit better training of the model, thus improving the accuracy of the model).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
2. Matei et al discloses the method of claim 1, wherein the machine-learning model is a generative model (See: [0021] FIG. 1B shows a design approach that utilizes machine learning techniques to reduce the number of predicted designs based on an input library of components 150. According to various configurations, a generative model 160 is used to produce a reduced number of feasible electrical circuit designs 175 from all possible designs 170).
3. Sinha et al discloses the method of claim 2, further comprising training the generative model, which comprises: obtaining images of a plurality of process flow diagrams (PFDs) associated with known systems (See: par [0073] inputting images that were predetermined by the pre-processing module as likely to lead to a positive identification by the image recognition ML model); extracting, from the images, topology information associated with the known systems (See: [0065] Symbol extractor 316 is configured to detect the symbols extracted from the images. In some embodiments, symbol extractor 316 applies image processing algorithms to identify probable regions of symbols in the images, then detects the symbol types and locations in the images via a gross symbol identification technique); and converting the topology information into a predetermined format (See: [0079] ML-based asset extraction 506 generally begins at block 602 where engineering diagrams (e.g., PI&D, PFD, etc.) are converted from PDF to image format. At block 604, a user inputs information identifying a plant site and an area where each diagram is being used. At block 606, all text in the diagrams are found, and at block 608, unit identifiers for the diagrams are found).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
4. Sinha et al discloses the method of claim 3, wherein extracting the topology information comprises applying an image-processing technique to detect components and connections among the detected components within each image (See: par [0065] Symbol extractor 316 is configured to detect the symbols extracted from the images. In some embodiments, symbol extractor 316 applies image processing algorithms to identify probable regions of symbols in the images, then detects the symbol types and locations in the images via a gross symbol identification technique. The symbol extractor 316 maintains a running count of newly detected symbols in order to keep track of the number of detected symbols and determine whether any new symbols were detected during a given execution cycle).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
5. Sinha et al discloses the method of claim 3, wherein training the generative model further comprises associating a label with the converted topology information from each PFD, wherein the label comprises a functional description of a system corresponding to the PFD (See: [0079] ML-based asset extraction 506 generally begins at block 602 where engineering diagrams (e.g., PI&D, PFD, etc.) are converted from PDF to image format. At block 604, a user inputs information identifying a plant site and an area where each diagram is being used. At block 606, all text in the diagrams are found, and at block 608, unit identifiers for the diagrams are found).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
6. Sinha et al discloses the method of claim 3, wherein the predetermined format comprises a formal language representation of the topology information (See: [0147] In some embodiments, users and plant operators can also enter commands to initiate operations. The HMI may interpret commands written in natural language. For example, to instruct the HMI to initiate filling up a component labeled “Tank 01,” the command “Fill Tank 01” may be entered into a command bar (not expressly shown)).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
7. Matei et al discloses the method of claim 6, wherein the formal language representation comprises a number of component-connection sequences, wherein a respective component-connection sequence comprises a statement indicating a connection order of a number of components (See: [0044] In some cases, additional feasibility conditions may be imposed to ensure that the predicted model can be physically implemented. For example, two such feasibility conditions are: if “node.con” appears then “node” must be present and “node.con” must be connected to “node”. According to various configurations, connections of the type (“node1.con”, “node2”) and (“node1”,“node2”) are not allowed. In other words, components can interact through their interfaces only. Hence the only type of connections that may be allowed are (“node1.con”,“node2.con”) and (“node1.con”,“node1”)).
8. Sinha et al discloses the method of claim 2, wherein the generative model comprises a natural language processing (NLP) machine-learning model (See: [0137] For unstructured data, such as P&IDs, PFDs, Process Control Narratives (PCNs) and other image or unstructured text formats, the system extracts domain entities and associated relationships from the unstructured data sources into structured data using machine learning; [0147] In some embodiments, users and plant operators can also enter commands to initiate operations. The HMI may interpret commands written in natural language. For example, to instruct the HMI to initiate filling up a component labeled “Tank 01,” the command “Fill Tank 01” may be entered into a command bar (not expressly shown); comprising one or more of: an N-gram language model; a recurrent neural net (RNN) language model; a hidden Markov model; a model implementing probabilistic context-free grammars; a naive Bayes model; a latent Dirichlet allocation (LDA) model; a sequence to sequence (Seq2Seq) model; and a transformer model (See: [0039] The projected token is passed through a recurrent neural network (RNN) cell 630, 632, 634, with a hidden layer of size 32. The state of the RNN cell (a latent variable, for example) is passed through a linear layer 620, 622, 624 and converted 610, 612, 614 to a one hot encoding representation using the “softmax” function).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
9. Sinha et al discloses the method of claim 1, wherein determining the parameters of the components comprises using an optimization technique to search a parameter space associated with a respective component (See: [0010] the processor-executable instructions further cause the control system to allow a user to conduct a search of the knowledge base using natural language queries, display nodes responsive to the search along with assets that the nodes share in common, and/or display a legend for the nodes on a color-coded basis; [0080] In some embodiments, finding a unit identifier involves searching in the information block of a diagram or other predefined portion of the diagram for certain keywords, such as “Drawing” or “Unit” or “Section” or variations thereof).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
10. Sinha et al discloses the method of claim 9, further comprising: in response to failing to find parameters of the components meeting the quantitative design requirements, generating, by the trained machine-learning model, an additional topology of the physical system (See: Abstract, inputting an engineering diagram for a unit of the industrial plant, the engineering diagram including symbols representing assets of the industrial plant; extracting one or more assets from the engineering diagram using machine learning to recognize the one or more assets, the one or more assets including equipment, instruments, connectors, and lines, the lines relating the equipment, instruments, and connectors to one another; determining one or more relationships between the equipment, instruments, connectors, and lines to one another using machine learning to recognize the one or more relationships; and creating a flow graph from the equipment, instruments, connectors, and lines and the relationships between the equipment, instruments, connectors, and lines; par [0160] the system may aggregate alarms by obtaining a list of all equipment generating alarms and identifying the first equipment from the list. The system adds this equipment to a new alarm cluster and checks whether a neighboring equipment is also generating alarms. If yes, then the system adds the neighboring equipment to the current alarm cluster. The process is repeated with the next equipment on the list until all equipment and neighboring equipment have been processed).
It would have been obvious before the effective filing date to combine automatic extraction of assets data from engineering data sources as taught by SINHA et al to component design of Matei et al would be to control industrial process automation and control systems (SINHA et al, [0002]).
As per Claims 11-20, claims 11-20 recite limitations analogous in scope to those of claims 1-10, and as such are similar rejected.
Conclusion
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 10/09/2025