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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, Claims 1-9, in the reply filed on 07/29/2026 is acknowledged.
Claims 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 07/29/2026.
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 (i.e., changing from AIA to pre-AIA ) 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) 1-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu et al. (Pointer gauge adaptive reading method based on a double match).
Regarding claim 1, Xu discloses a method for process control with process measurements validation capability (Figure 1), the method comprising:
obtaining a first actual sensor reading of a process variable from a field sensor to be monitored (see: Source Image);
obtaining a first virtual sensor reading of the process variable from a virtual sensor, wherein the virtual sensor is camera-based (see: Reference Image);
calculating a first deviation between the first actual sensor reading and the first virtual sensor reading (see: Feature Points Detection And Matching);
making a first determination that the first deviation exceeds a prespecified threshold (see: Course Matching);
based on the first determination, making a second determination that the first actual sensor reading does not correspond to first related sensors readings (see: Fine Matching); and
based on the second determination, making the first virtual sensor reading a first trusted sensor reading for controlling an aspect of a process associated with the process variable (see: Final Pointer Center position).
Regarding claim 2, Xu further discloses based on the second determination, revalidating the field sensor to be monitored (Figure 1, see: (c) gauge reading calculation).
Regarding claim 3, Xu further discloses obtaining a second actual sensor reading (see: Zernike Principal Argument φ0); obtaining a second virtual sensor reading (see: Zernike Principal Argument φ1); calculating a second deviation between the second actual sensor reading and the second virtual sensor reading (see: Argument Difference α); making a third determination that the second deviation exceeds the prespecified threshold (see: MSE); based on the third determination, making a fourth determination that the second actual sensor reading does correspond to second related sensors readings (see: Pointer Angle θ); and based on the fourth determination, making the second actual sensor reading a second trusted sensor reading (see: Least Squares Method Calculate Gauge Reading).
Regarding claim 4, Xu further discloses based on the fourth determination, updating a virtual sensor model (see: Adaptive gauge reading flowcharts).
Regarding claim 5, Xu further discloses obtaining a second actual sensor reading (see: Zernike Principal Argument φ0); obtaining a second virtual sensor reading (see: Zernike Principal Argument φ1); calculating a second deviation between the second actual sensor reading and the second virtual sensor reading (see: Argument Difference α); making a third determination that the second deviation does not exceeds the prespecified threshold (see: MSE); and based on the third determination, making the second actual sensor reading a second trusted sensor reading (see: Least Squares Method Calculate Gauge Reading).
Regarding claim 6, Xu further discloses the process variable is one selected from a group consisting of a temperature, a pressure, a flow, and a level (see: pressure (MPa) gauges).
Regarding claim 7, Xu further discloses obtaining the first virtual sensor reading comprises: capturing an image of a physical gauge; and processing the image to obtain the virtual sensor reading (see: Image Registration).
Regarding claim 8, Xu further discloses the first related sensors readings are obtained using related field sensors, different from the field sensor to be monitored, and where the related field sensors are selected based on at least one selected from a group consisting of a physical process model of the process and a machine learning model (see: Adaptive gauge reading flowcharts).
Regarding claim 9, Xu further discloses the physical model or the machine learning model is used to identify a correlation between readings of the related field sensors and the field sensor to be monitored (see: Least Squares Method Calculate Gauge Reading).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alexeev et al. (A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions) discloses an analogous method of Automatic Meter Reading detection based on a neural network solution.
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/ROBERT J EOM/Primary Examiner, Art Unit 1797
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