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 .
In response to the restriction requirement, Applicant elected claims 1-6, 10-14, and 16-17 for further examination. As a result, claims 7-9, 15, and 18-20 are withdrawn from further prosecution.
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.
Claim(s) 1-6, 10-14, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sugay (US 20230221208) in view of Wang et al. (US 2023/0176023).
Regarding to claims 1, 10, 16:
Sugay discloses a system for training a machine learning gas detection model used in monitoring an industrial site for gas leaks, comprising:
a gas sensor module containing data representing predefined locations of gas sensors located at the industrial site (FIG. 3, element 350: System sensoring. Paragraph [0050]: One or more sensing devices are installed at predefined locations);
a gas sensor response model that models the responses of the gas sensors (FIG. 3, element 230: ML Decision Model); and
a digital twin (FIG. 3, element 310: Simulation Digital Twin) containing a virtual representation of physical equipment located at the industrial site, generates time-series gas sensor responses that are used to train the machine learning gas detection model for detecting gas leaks at the industrial site (FIG. 3: Data training sets 340 is provided to train the ML model 230. Paragraph [0043]: The training data sets are utilized by the ML decision model for testing and demonstration purposes).
Sugay however does not teach wherein the digital twin executes simulations of gas leaks using the virtual representation of the industrial site and varying simulated wind patterns, gas leak locations and leak rates including simulated gas sensor responses.
Wang et al. discloses a method/apparatus for gas leakage detection, comprising a facility digital twin (FIG. 2, element 52) used as a virtual representation of a system at a worksite for the simulation (pagraph [0051]) based on sensed weather data (FIG. 2, element 62) such as wind speed/direction (pagraph [0052]), gas sensor data such as locations and leak rates (FIG. 2, elements 64, 66 and paragraph [0057]).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify the digital twin in Sugay’s system to perform the simulation based on wind patterns, gas leak locations and leak rates as disclosed by Wang et al. to create a digital and mathematical model that may be broadly provided as a simulation service (paragraph [0007]).
Wang et al. also discloses the following claims:
Regarding to claims 2-3, 11, 17: wherein the virtual representations of the physical equipment located at the industrial site is data input into an industrial site model, wherein the system includes: predefined leak locations and leak rate data used by the digital twin to execute the gas leaks simulations (paragraph [0057]: A list of potential leak sources with location data and anticipated leak rates).
Regarding to claims 4, 12: wherein the system includes: predefined weather data, used to simulate the varying simulated wind patterns used in the gas leak simulations (paragraph [0057]: Wind history data measured prior to planning/deploying the sensors).
Regarding to claims 5, 13: wherein the system includes: a gas dispersion model (FIG. 2, element 54: Gas Plume Model) executed by the digital twin that receives the virtual representation of the industrial site and predefined data representing varying simulated wind patterns, gas leak locations and leak rates that generate estimated gas leak locations for the gas leak simulations (paragraph [0007]: The probable leak source information may include gas leak rate as well as leak location).
Regarding to claims 6, 14: wherein the gas sensor response model receives the estimated locations for the simulated gas leaks from the gas dispersion model and generates the time-series gas sensor responses to train the machine learning gas detection model (Sugay: FIG. 3 and paragraph [0043]: The training data sets are utilized by the ML decision model for testing and demonstration purposes).
Regarding to claim 16: A gas detection system executing in a plant server used for monitoring
an industrial site for gas leaks, the plant server communicatively coupled to a plurality of gas
sensors and at least one weather station, and a training system coupled to the plant server, wherein the trained machine learning gas detection model is coupled to the plant server and the gas leak detection system and used by the plant server to monitor the industrial site for gas leaks using the gas sensor responses (Wang et al.: FIG. 1: The worksite 10 has a plurality of sensors 12 including gas sensors, wind sensors, temperature sensors,… and a weather station 12G. Sugay: FIG. 3 shows the machine learning model 230 predicts leak (170) and identifies leak (160)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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