DETAILED ACTION
Claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-16 and 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Havner et al., US Patent No. 6,854,111 (hereinafter Havner).
Regarding claims 1-16 and 18-19, Havner discloses all the claimed limitations, as outlined below.
Claim 1. A system comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a configuration component configured to: construct a graphical representation of an industrial process, wherein the graphical representation comprises: a model representation comprising a group of models, wherein a first model in the group of models represents operation of at least one device operating in the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
Claim 2. The system of claim 1, wherein the model representation further comprises a second model, the second model has a second input and a second output, and the first model has a first input and a first output, wherein, the first output of the first model connects to the second input of the second model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
Claim 3. The system of claim 2, wherein the second output of the second model is based, at least in part, on the first output of the first model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
4. The system of claim 3, wherein a second parameter measured at the second output of the second model is based, at least in part, on a first parameter outputted by the first model as an input to the second model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
5. The system of claim 4, further comprising a visualization component configured to: present the graphical representation on a human-machine interface (HMI); and present at least one of the first parameter or the second parameter on the HMI (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
6. The system of claim 2, further comprising an artificial intelligence (AI) component, configured to: monitor operation of the first model, wherein the first model is operating with a first configuration;
determine a change in operation of the first model from the first configuration to a second configuration; determine a third model that represents the second configuration of operation of the first model; and replace the first model with the third model in the graphical representation of the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
7. The system of claim 6, wherein the AI component is further configured to: monitor operation of the second model, wherein the second model is initially operating with a third configuration; determine whether the second configuration of the first model affects operation of the second model, wherein operation of first model with the second configuration causes operation of the second model to have an operation different to the third configuration; in response to determining operation of the second model has an operation different to the third configuration, determine a fourth model to represent current operation of the second model; and replace, in the graphical representation of the industrial operation, the second model with the fourth model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
8. The system of claim 2, wherein the parameter output by the first model is represented as a node connecting the first model to the second model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
9. The system of claim 8, further comprising a visualization component configured to present a value of the node as a value on the model representation (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
10. The system of claim 2, wherein at least one of the first model or the second model are generated based in part on: a piping and instrumentation diagram representing operation of the at least one device operating in the industrial process; or historical data previously captured regarding operation of the at least one device operating in the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
11. The system of claim 1, wherein the group of models comprise at least one of a parametric model, a parametric hybrid model, a linear model, a non-linear model, a kinetic model, a first principles reasoning model, a solver, a historical data model, a cost function analysis model, a regression cost function model, a binary classification cost function model, a multi-class classification cost function model, a mixed-integer non-liner program model, a deep learning-based model, a backpropagation model, a static backpropagation model, a recurrent backpropagation model, a gradient computation model, a chain rule model, an error determination model, or a mathematical model configured to represent operation of a component in the process, wherein the component is a device, a group of devices, or a component block (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
12. The system of claim 1, wherein the configuration component is further configured to construct a layout representation comprising a group of icons, wherein a first icon in the group of icons represents operation of a device included in the in the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
13. A computer-implemented method for visualizing an industrial process, comprising: constructing a graphical representation of the industrial process, wherein the graphical representation comprises: a first model representing operation of a first device operating in the industrial process, the first model having a first input and a first output; and a second model representing operation of a second device operating in the industrial process, the second model has a second input and a second output, wherein the first output of the first model connects to the second input of the second model; and presenting the graphical representation of the process on a human-machine interface (HMI) (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
14. The computer-implemented method of claim 13, wherein a second parameter measured at the second output of the second model is based, at least in part, on a first parameter outputted by the first model as an input to the second model; and presenting at least one of the first parameter or the second parameter on the HMI (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
15. The computer-implemented method of claim 14, further comprising: monitoring operation of the first model, wherein the first model is operating with a first configuration; determining a change in operation of the first model from the first configuration to a second configuration; determining a third model that represents the second configuration of operation of the first model; and replacing the first model with the third model in the graphical representation of the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
16. The computer-implemented method of claim 14, further comprising: monitoring operation of the second model, wherein the second model is initially operating with a third configuration; determining whether the second configuration of the first model affects operation of the second model, wherein operation of first model with the second configuration causes operation of the second model to have an operation different to the third configuration; in response to determining operation of the second model has an operation different to the third configuration, determining a fourth model to represent current operation of the second model; and replacing, in the graphical representation of the industrial operation, the second model with the fourth model (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
18. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a process to cause the processor to: construct a graphical representation of the industrial process, wherein the graphical representation comprises: a first model representing operation of a first device operating in the industrial process, the first model having a first input and a first output; and a second model representing operation of a second device operating in the industrial process, the second model has a second input and a second output, wherein the first output of the first model connects to the second input of the second model, and a second parameter measured at the second output of the second model is based, at least in part, on a first parameter outputted by the first model as an input to the second model; and present the graphical representation of the process on a human-machine interface (HMI), wherein the graphical representation includes at least one of the first parameter or the second parameter (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
19. The computer program product of claim 18, wherein the program instructions are further executable by the processor to cause the processor: monitor operation of the first model, wherein the first model is operating with a first configuration; determine a change in operation of the first model from the first configuration to a second configuration; determine a third model that represents the second configuration of operation of the first model; and replace the first model with the third model in the graphical representation of the industrial process (C4 L10-27, C5 L56-67, C6 L1-17, C7 L60-63 - - Adding a plurality of equipment as needed. Operations are associated with the equipment using drag and drop operations. Control variables are associated with the equipment enabling input and output connections. The developer and wizard are being interpreted as artificial intelligence components).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Havner et al., US Patent No. 6,854,111 (hereinafter Havner) in view of Brandt et al., US Patent Application Publication No. 2004/0153171 (hereinafter Brandt).
Regarding claims 17 and 20, Havner discloses all the limitations of the base claims as outlined above.
Havner fails to clearly specify wherein the program instructions are further executable by the processor to cause the processor to: generate a layout representation comprising a group of icons, wherein a first icon in the group of icons represents operation of the first device; and grant access to either of the layout representation or the model representation based on user role, wherein a first user role is granted access to the model representation while a second user role is not granted access to the model representation.
However, Brandt teaches wherein program instructions are executable by a processor to cause the processor to: generate a layout representation comprising a group of icons, wherein a first icon in the group of icons represents operation of a first device; and grant access to either of the layout representation or a model representation based on user role, wherein a first user role is granted access to the model representation while a second user role is not granted access to the model representation (Para 0010).
The applied prior art is considered analogous art to the claimed invention because they relate to same field of endeavor. They relate to industrial control.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the above automated programming system, as taught by Havner, and incorporating the concept of access control, as taught by Brandt.
One of ordinary skill in the art would have been motivated to do this modification in order to facilitate security in an industrial environment, as suggested by Brandt (Para 0010).
Citation of Pertinent Prior Art
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent No. 5485620 – relates to industrial automation.
US Patent No. 5980078 – relates to process control.
US Patent No. 6834370 – relates to process configuration.
US Patent No. 7505817 – relates to programming automation.
US Patent No. 7962472 – relates to an optimization algorithm.
US Patent No. 9904263– relates to smart process objects used in a process plant modeling system.
US Patent No. 10496061 – relates to modeling of an industrial automation environments.
US Patent Application Publication No. 20020123864 – relates to process control.
Feiler, Peter H. Configuration management models in commercial environments. Pittsburgh, PA: Carnegie Mellon University, Software Engineering Institute, 1991 – relates to configuration management.
Conradi, Reidar, and Bernhard Westfechtel. "Version models for software configuration management." ACM Computing Surveys (CSUR) 30.2 (1998): 232-282 – relates to configuration management.
Leng, Jiewu, et al. "Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model." Robotics and computer-integrated manufacturing 63 (2020): 101895 – relates to virtual models and configuration.
Morariu, Cristina, et al. "Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems." Computers in Industry 120 (2020): 103244 – relates to learning models.
Essien, Aniekan, and Cinzia Giannetti. "A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders." IEEE Transactions on Industrial Informatics 16.9 (2020): 6069-6078 – relates to learning models.
Wang, Qiyue, et al. "A tutorial on deep learning-based data analytics in manufacturing through a welding case study." Journal of Manufacturing Processes 63 (2021): 2-13 – relates to learning models.
Liu, Bufan, et al. "A cost-effective manufacturing process recognition approach based on deep transfer learning for CPS enabled shop-floor." Robotics and computer-integrated manufacturing 70 (2021): 102128 – relates learning models.
Conclusion
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/CARLOS R ORTIZ RODRIGUEZ/ Primary Examiner, Art Unit 2119