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 .
Drawings
The drawings (12 total) following Fig.3 are objected.
The objected drawings contain no numbers/legends. Appropriate correction isrequired.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
The omitted structural cooperative relationships are: the nexus between / the effect of determining the operating property and the physical / mechanical component(s) of the pumpjack.
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.
Claims 1, 2, and 5-12 are rejected under 35 U.S.C. 103 as being unpatentable over Hoefel(US Pub.2020191136A1), hereinafter Hoefel in view of Li KUN ET AL: "A novel prediction method for down-hole working conditions of the beam pumping unit based on 8-directions chain codes and online sequential extreme learning machine", hereinafter Li and Ding (CN110600135A), hereinafter Ding.
Regarding Claim 1, Hoefel disclose a computer-implemented method for determining an operating property of a pumpjack (see paragraph §0003 and "Determining a Condition Associated with the Pump System"; see the submersible pump in Figure 1 and see also §0023), wherein the pumpjack has a pump head (Fig. 1, # 110), which is connected to a kinematic converter (Fig. 1, # 104) via a rod system(Fig. 1, #144(rod)), and the kinematic converter is driven by a motor (Fig. 1, #130, prime mover, see also para [0025]) during operation and a load-travel graph containing curve points (see the load distance diagram with curve points Fig. 3) is determined for the pumpjack by an analysis device (Fig. 2, # 332, para 0037) using a recording device(Fig. 3, # 332, 333, 334 and 335, para [0037]) and is provided as an operating load-travel graph containing operating curve points (Fig. 3, 370 and 390), the method comprising:
However, Hoefel does disclose a method comprising operating a pump system; determining a condition associated with the pump system; and controlling the pump system based at least in part on the condition (Abstract; Fig. 1; [0023] – [0026]).
Hoefel further discloses that the system comprises a dynamometer for acquiring dynamic data, which can be transmitted and/or accessed by equipment to diagnose various operating conditions via downhole graph (i.e. dynamometer card / dynagraph), wherein deviations from the ideal shape can indicate performance issues (e.g., gas interference, system leaks, stuck pumps, parted rods) and various other anomalies that may be identified and accounted for automatically or through manual intervention (Fig. 3; [0037]; [0041] – [0042]; [0046] – [0047]).
Hoefel does not disclose in a training mode, providing by the analysis device at least one model load-travel graph containing respective model curve points, which graph is normalized to a predefined reference variable, and recording at least two subsets of the model curve points as a first and at least one second feature on the basis of machine learning, and producing and training the first and the at least one second feature in the form of at least one random forest model using a K means algorithm, and
in an operating mode, normalizing the operating curve points to the reference variable and performing a check to ascertain whether there is a similarity between at least one subset of the operating curve points and the at least one random forest model, and, if so, determining the operating property of the pumpjack therefrom.
Li disclose in a training mode, providing by the analysis device at least one model load-travel graph containing respective model curve points (Fig. 6, page 76, Col. 2 “Simulation”: fault pattern classification with 88 dynamometer cards in the training set ), which graph is normalized to a predefined reference variable (Page 289, col.2, lines 6-8, where First, the dynamometer card is normalized to [0,1]), and recording at least two subsets of the model curve points as a first and at least one second feature on the basis of machine learning (page 290, para 3.2, where Eight feature vectors of each dynamometer card are extracted to construct eight time series data sequences; Page 291, col. 1, where Eight time series data sequences are denoted by Di/j, e.g., dimensional vectors represents the eight time series data sequences corresponds to the subsets of the model curve points); and Page 295, para 5, col. 2, where Eight predicted feature vectors of eight time series data sequences are obtained by the OS-ELM prediction model… and values of all feature vectors are recorded by the interval form, e.g., the first and second features corresponds to the (first and second time series data sequences) of the eight feature vectors which is recorded), and producing and training the first and the at least one second feature in the form of at least one model using a K means algorithm (Figures 2 and 3, four quadrants); (pages 74-76, 3-feature extraction of the downhole dynamometric card based on the curve movement); (Page 291, Col. 1, lines 10-11, where the mean square displacement Mi(k) is calculated by pi,c (k) and qi,c (k) as follows:
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).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide model load-travel graph containing respective model curve points, as taught by Li into Hoefel analysis of the operating condition of the pump system in order to more accurately determine operational property anomalies.
Ding disclose random forest model(Page 3, Step 2, where judging two decision tree is related; calculating the random forest model).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide two random forest models are formed that have a low correlation with respect to one another, as taught by Ding into combination of Li into Hoefel analysis in order to reduces overall model variance and prevents overfitting, leading to higher, more stable predictive accuracy and improve ensemble performance to cancel errors, reducing overall variance.
Regarding Claim 2, Hoefel and Li and Ding disclose the method as claimed in claim 1, but Hoefel and Li do not disclose wherein at least two random forest models are formed that have a low correlation with respect to one another.
Ding disclose at least two random forest models are formed that have a low correlation with respect to one another (Page 3, Step 2, where judging two decision tree is related; calculating the random forest model between the decision tree of similar value to the similarity matrix, then according to the set critical value and similarity matrix for clustering decision tree, the decision tree classification performance high, but with low correlation to filter).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide two random forest models are formed that have a low correlation with respect to one another, as taught by Ding into combination of Li into Hoefel analysis in order to reduces overall model variance and prevents overfitting, leading to higher, more stable predictive accuracy and improve ensemble performance to cancel errors, reducing overall variance.
Regarding Claim 5, Hoefel and Li and Ding disclose the method as claimed in claim 2, but Hoefel and Ding do not disclose wherein a sequence of the first and the at least one second feature within a pump cycle during operation of the pumpjack in the respective random forest model is taken into account for determining the operating property of the pumpjack.
Li a sequence of the first and the at least one second feature within a pump cycle(Fig. 2, Fig. 3, (a)-(d), data represented in the pump cycle), (Page 287, para 2, col. 2, (3), where the graphic feature of the dynamometer card under this work condition is as
follows: the load line changes quickly and the unload line changes slowly…; page 290, para 3.1, col. 2, where all time series data sequence for the dynamometer card) during operation of the pumpjack (Page 291, para 3.2, col. 2, where 228 dynamometer cards of one oil well were continuously collected during a production period with the sampling period "hour"; then different feature vectors were extracted to construct eight time series data sequences) in the respective model is taken into account for determining the operating property of the pumpjack (see Page 288, (a)-(c), para 2, 2.1 dynamometric card, where determining load for (a)-(d)); introduction, where method is used to build the prediction model, which can realize fast updating with dynamic work condition changes; finally, the grey interval relational degree between the predicted feature vectors and feature vectors of each fault type is calculated to determine the predicted fault type).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a sequence of the first and the at least one second feature within a pump cycle during operation of the pumpjack as taught by Li into Hoefel analysis of the operating condition of the pump system in order to more accurately determine operational property anomalies.
Regarding Claim 6, Hoefel and Li and Ding disclose the method as claimed in claim 1 wherein Hoefel and Ding do not disclose the first or the at least one second feature comprises an interval between operating curve points in the operating load-travel graph.
Li disclose the first or the at least one second feature comprises an interval between operating curve points in the operating load-travel graph (Fig. 3,(a)-(d), where shown the intervals between the points).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide a interval between operating curve points in the operating load-travel graph, as taught by Li in combination of Hoefel and Ding analysis of the operating condition of the pump system in order to more accurately determine operational property anomalies.
Regarding Claim 7, Hoefel and Li and Ding discloses the method as claimed in claim 1, further Hoefel discloses wherein the motor is electrically operated and the recording device is designed to record the electrical power consumption of the motor during the operation thereof, from which power consumption the operating properties of the pumpjack are determined ([0022]; [0025]; [0028]), wherein a controller / drive may be coupled to the motor to change operation of the motor based on operational determinations ([0130]).
Hoefel further discloses wherein the system may comprise one or more processors; memory accessible to at least one of the processors, and processor-executable instructions stored in the memory and executable by at least one of the processors to instruct the system to: operate a pump system; determine a condition associated with the pump system; and control the pump system based at least in part on the condition ([0212]).
Regarding Claim 8, Hoefel in view of Li and Ding discloses a computer program stored on a non-transitory computer readable medium, comprising:
Hoefel further discloses instructions stored thereon that, when executed by a computer, cause said computer to carry out the method as claimed in claim 1 (para [0212], where a system can include one or more processors; memory accessible to at least one of the processors; and processor-executable instructions stored in the memory and executable by at least one of the processors to instruct the system to: operate a pump system; determine a condition associated with the pump system).
Hoefel further discloses one or more computer-readable media which can include computer-executable instructions executable to instruct a computing system to: operate a pump system; determine a condition associated with the pump system; and control the pump system based at least in part on the condition ([0213], Fig. 7).
Regarding Claim 9, Hoefel in view of Li and Ding discloses a non-transitory electronically readable data carrier having comprising:
Further, Hoefel disclose readable control information stored thereon, which control information comprises at least a computer program configured in such a way that it performs a method as claimed in claim 1 when the data carrier is used in a computing apparatus(para [0212], where a system can include one or more processors; memory accessible to at least one of the processors; and processor-executable instructions stored in the memory and executable by at least one of the processors to instruct the system to: operate a pump system; determine a condition associated with the pump system).
Hoefel further discloses one or more computer-readable media which can include computer-executable instructions executable to instruct a computing system to: operate a pump system; determine a condition associated with the pump system; and control the pump system based at least in part on the condition ([0213], Fig. 7).
Regarding Claim 10, Hoefel in view of Li and Ding discloses a non-transitory data carrier comprising:
Further, Hoefel disclose the computer program as claimed in claim 8(para [0212], where a system can include one or more processors; memory accessible to at least one of the processors; and processor-executable instructions stored in the memory and executable by at least one of the processors to instruct the system to: operate a pump system; determine a condition associated with the pump system).
Hoefel further discloses one or more computer-readable media which can include computer-executable instructions executable to instruct a computing system to: operate a pump system; determine a condition associated with the pump system; and control the pump system based at least in part on the condition ([0213], Fig. 7).
Regarding Claim 11, Hoefel and Li and Ding disclose as claimed in claim 1, further Hoefel disclose an analysis device comprising:
a memory (para [0212], where memory accessible to at least one of the processors; and processor-executable instructions stored in the memory and executable by at least one of the processors to instruct the system to: operate a pump system; determine a condition associated with the pump system) for determining an operating property of a pumpjack(see paragraph §0003 and "Determining a Condition Associated with the Pump System"; see the submersible pump in Figure 1 and see also §0023),
wherein the analysis device is designed to analyze a provided operating load-travel graph (Fig. 3 and 5) using the method as claimed in claim 1 and to ascertain the operating property therefrom(Fig. 3 and 5,para [0091], where FIG. 5 shows some examples 500 of dynamometer card shape variations, which may facilitate control. The shapes correspond to dynamic conditions such as a perfect trace, rod stretch, partial pump fill, acceleration and harmonics, low filage, tapping down, tapping up, worn barrel, delayed tv sensing, bad tv, bad sv, pounding hard, gas lock or bad sv, deep rod part, excessive harmonics, high fluid level, excessive friction, excessive rod stretch, stuck pump, bad position signal, bad load signal or galded pump, etc.).
Regarding Claim 12, Hoefel and Li and Ding disclose as claimed in claim 11, specifically Hoefel disclose a pump system for determining an operating property of a pumpjack(see paragraph §0003 and "Determining a Condition Associated with the Pump System"; see the submersible pump in Figure 1 and see also §0023), wherein the pumpjack comprises a pump head (Fig. 1, # 110), which is connected to a kinematic converter(Fig. 1, # 104) via a rod system (Fig. 1, #144(rod)), and the kinematic converter is driven by a motor (Fig. 1, #130, prime mover, see also para [0025]) during operation(see the load distance diagram with curve points Fig. 3), the pump system comprising:
a recording means device(Fig. 3, # 332 (inclinometer), 333(Hall effect sensors), 334(load cell) and 335(current sensors), para [0037]), designed to record and provide a load-travel graph relating to the pumpjack containing curve points(Fig. 3, 370 and 390), and the analysis device as claimed in claim 11 designed to ascertain the operating property from the provided operating load-travel graph (Fig. 3 and 5, para [0085], where a system may include a 3D model, a pressure model and a load model. As an example, a system can provide for generation, recognition of and/or control of dynacard data); (para [0091], where FIG. 5 shows some examples 500 of dynamometer card shape variations, which may facilitate control. The shapes correspond to dynamic conditions such as a perfect trace, rod stretch, partial pump fill, acceleration and harmonics, low filage, tapping down, tapping up, worn barrel, delayed tv sensing, bad tv, bad sv, pounding hard, gas lock or bad sv, deep rod part, excessive harmonics, high fluid level, excessive friction, excessive rod stretch, stuck pump, bad position signal, bad load signal or galded pump, etc.).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Hoefel in view of Li, and Ding, as applied above and further in view of Lee (CN1391683A), hereinafter Lee.
Regarding Claim 3, Hoefel and Li and Ding disclose the method as claimed in claim 2, wherein the at least two random forest models having low correlation, as recited in claim 2.
Hoefel and Li and Ding do not disclose models are produced by randomly selecting a respective point from a set of operating curve points and providing said point from the set of operating curve points using substitution.
Lee disclose models are produced by randomly selecting a respective point from a set of operating curve points (Page 27, para 8, see B1, where to "simplify" the driving curve by selecting a subset of the two-dimensional point. operating on the drive curve comprises the outline point) and providing said point from the set of operating curve points using substitution (Page 28, para 5, see 4.1.6, where a new constrained substituted point maybe or probably is not with the point marked on the same position).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide two random forest models are formed that have a low correlation with respect to one another, as taught by Lee in combination of Hoefel and Ding analysis in order to reduces overall model variance and prevents overfitting, leading to higher, more stable predictive accuracy and improve ensemble performance to cancel errors, reducing overall variance.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hoefel in view of Li, and Ding, as applied above and further in view of Bahado-Singh (US Pub.20200135680A1) hereinafter Bahado-Singh.
Regarding Claim 4, Hoefel and Li and Ding disclose the method as claimed in claim 2, but do not disclose wherein the at least two random forest models having low correlation arc produced by continuing to take a subset into account for breaking up a node in a random forest model.
Bahado-Singh disclose the at least two random forest models having low correlation arc produced by continuing to take a subset into account for breaking up a node in a random forest model (para [0128], where difference between Random Forest algorithm and the decision tree algorithm is that in Random Forest, the processes of finding the root node and splitting the feature nodes will run randomly).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide two random forest models are continuing to take a subset into account for breaking up a node in a random forest model, as taught by Bahado-Singh into combination of Hoefel and Ding and Li analysis in order to reduces correlation among the individual trees.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KALERIA KNOX whose telephone number is (571)270-5971. The examiner can normally be reached M-F 8am-5pm.
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/KALERIA KNOX/
Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857