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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/16/2025 has been entered.
Response to Arguments
112 Rejections
Applicant’s filed amendments dated 12/16/2025 have overcome the previously set forth 112 rejections.
101 Rejections
Based on applicant filed amendments dated 12/16/2025 have overcome the previously set forth 101 rejections.
103 Rejections
Applicant has amended the claims to overcome the prior art, however, the examiner finds the combination of Snedden and Altamura to read on the instant claims.
The examiner disagrees with applicant assertion that “wherein various colors of the vector map at a given time correspond to various combinations of the color components and their corresponding intensities” recite a limitation that requires the multiple spectrums are displayed at the same time, simultaneously. As, under the broadest and most reasonable interpretation of the claim language, the phrase "at a given time" refers to the instantaneous state of the display, where each pixel (or vector element) on the map displays a specific color based on a unique combination of color components (e.g., Red, Green, and Blue in an RGB system) and their corresponding intensities relative to the data in which they represent. Therefore, combination produces respective colors, but only the colors relevant to the data being visualized at that moment are shown. As Altamura teaches displaying various time series data, and their assigned colors/intensities based on the magnitudes of those variables at their respective times. Therefore, claim does not recite all the variables are display simultaneously. Therefore, the claimed invention is taught by the combination as a whole.
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 (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 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) 46-65 is/are rejected under 35 U.S.C. 103 as being unpatentable over Snedden et al. (WO 2011/049648) in view of Altamura et al. (2012/0240072).
With respect to claim 46, Snedden et al. a method of interpreting a plurality of time-series datasets generated from operation of a hydrocarbon well, the method comprising: obtaining the plurality of time-series datasets (step 11; [0026]), wherein the plurality of time-series datasets is generated from an operation of a hydrocarbon well (Snedden et al. teaches the obtained time series data being data related to downhole pressure and flow rate of hydrocarbon wells) and includes a first time-series dataset that includes values of a first variable at a plurality of corresponding times (i.e. the downhole pressure of the well over time) and a second time-series dataset that includes values of a second variable at the plurality of corresponding times (i.e. the flow rate of the well over time); displaying a vector map (Fig. 6) and making an operational change based on the vector map (as the vector map is displayed so to perform a variety of management activities; [0035]), wherein the operational change comprises modifying at least one parameter (i.e. well injection and/or extractions; [0035]) of a subsequent operation performed on the hydrocarbon well [0035].
Snedden et al. remains silent regarding the vector map includes a time axis and a plurality of points distributed along the time axis at the plurality of corresponding times, wherein a color of each point of the plurality of points is defined in a plural-component color space and includes a first color component at a first intensity and a second color component at a second intensity, and optionally a third color component at a third intensity, and further wherein: (i) the first intensity at a given time of the plurality of corresponding times is based upon a magnitude of the first variable at the given time; and (ii) the second intensity at the given time is based upon a magnitude of the second variable at the given time; and (iii) the third intensity at the given time is based upon a magnitude of the third variable at the given time.
Altamura et al. teaches a similar method that displays a map that includes a time axis [0009] and a plurality of points distributed along the time axis at the plurality of corresponding times (as seen in Fig. 12), wherein a color of each point of the plurality of points (as each point is color-coded based on its respective intensity to form a color intensity plot; [0034-0035]) is defined in a plural-component color space (as Altamura teaches in para. [0093] using color gradients to define each point of the plurality of points in a plural-component color space, as seen in Fig. 12) and includes a first color component at a first intensity (as Altamura teaches in para. [0036] and [0037], Fig. 12 utilizes an intensity transformation, which allows magnitudes of data to be color coded) and a second color (as part of the defined color-coded intensity data) component at a second intensity (depending on a selected color for a specific magnitude defining the intensity), and a third color component at a third intensity (as part of the defined color-coded intensity data), wherein various colors of the vector map (seen in Fig. 12) at a given time (defined by the y axis of the figure) correspond to various combinations of the color components and their corresponding intensities (for example fig. 12 depicts on July 1 2010, the first, second and third color components representing their respective intensity at a use given time, which can be view by the user; [0092]); and further wherein: (i) the first intensity at a given time of the plurality of corresponding times is based upon a magnitude of a first variable (as Altamura teaches the data points can include kilowatts; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a first variable based on the color-coding of that data and its respective intensity); (ii) the second intensity at the given time is based upon a magnitude of the second variable (as Altamura teaches the data points can include gas metrics; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a second variable based on the color-coding of that data and its respective intensity); and (iii) the third intensity at the given time is based upon a magnitude of the third variable (as Altamura teaches the data points can include temperature; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a second variable based on the color-coding of that data and its respective intensity).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the vector map of Snedden et al. to include the color-coded intensity transformation plot as taught in Altamura et al. because Altamura et al. teaches such a modification allows for a user to quickly determined from the displayed data any anomalies and patterns [0083], thereby improving the displaying details of Snedden et al.
With respect claim 63, Snedden et al. as modified teaches a non-transitory computer-readable storage media (i.e. a computer memory; [0022]) including computer- executable instructions (as seen in Fig. 1 and as modified by Altamura et al.) that, when executed, direct a display [0035] to display a vector map (as modified) according to the reject method of claim 1.
With respect to claim 47, Snedden et al. as modified teaches the method wherein, prior to the displaying, the method further includes scaling the plurality of time-series datasets to generate a plurality of scaled time-series datasets (Snedden et al. teaches normalizing the time-data series, thereby reading on “scaling”, as normalizing is considered a type of feature scaling, insofar as what is structurally recited defining the scaling step) wherein the first intensity and the second intensity (of the intensity transformation taught in Altamura) are based upon the plurality of scaled time-series datasets (of the normalized data sets of Snedden et al.), and wherein the plurality of time-series datasets includes the third time-series dataset (i.e. for example time series data representing temperature; Altamura et al. [0040]) and the third intensity (taught by Altamura) is based upon the plurality of scaled time-series datasets (as Snedden et al., as modified, teaches normalizing the time-data series, thereby reading on “scaling”, as normalizing is considered a type of feature scaling, insofar as what is structurally recited defining the scaling step).
With respect to claim 48, Snedden et al. as modified teaches the method wherein the scaling the plurality of time-series datasets includes scaling such that the values of a corresponding variable of each time- series dataset range between a minimum variable scale value and a maximum variable scale value (as Snedden et al. teaches normalizing the data within a defined range; [0032]).
With respect to claim 49, Snedden et al. as modified teaches the method wherein a minimum variable value of each scaled time-series dataset of the plurality of time-series datasets is the minimum variable scale value (as defined in [0032] of Snedden, as the data series as normalized having a minimum value will define the minimum variable scale value).
With respect to claim 50, Snedden et al. as modified teaches the method wherein a maximum variable value of each scaled time-series dataset of the plurality of time-series datasets is the maximum variable scale value (as defined in [0032] of Snedden, as the data series as normalized having a maximum value will define the maximum variable scale value).
With respect to claim 51, Snedden et al. as modified teaches all that is claimed in the above rejection of claim 48 but remains silent regarding the minimum variable scale value is 0 and the maximum variable scale value is 46.
It has been held that it would have been obvious to try from a finite number of identified, predictable solutions, with a reasonable expectation of success. MPEP 2143(I)(E)
In this instance, a person having ordinary skill in the art, who would be a person having a degree in engineering, has the capability to perform the engineering practice of statistical analysis using a set of collected data to determine the minimum and maximum scale values. Such a statistical analysis contains a finite number of predictable solutions that would result in a range being defined, such as the minimum variable scale value is 0 and the maximum variable scale value is 46.
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the ranges of Snedden et al. to accommodate the dataset, because such an analysis ensures the range is defined by the dataset itself, thereby ensuring accurate data capturing for display.
With respect to claim 52, Snedden et al. as modified teaches the method wherein the scaling the plurality of time-series datasets includes linearly scaling the plurality of time-series datasets (para. [0029] of Snedden et al. teaches using a linear trend scaling of the time-series dataset to ensure the two a normalized in accordance with one another).
With respect to claim 53, Snedden et al. as modified teaches the method wherein the scaling the plurality of time-series datasets further includes filtering at least one outlier value from at least one time-series dataset of the plurality of time-series datasets (as S15 of Snedden et al. teaches filtering the data [0030], which removes outlier values, i.e. noise, from at least one time-series dataset that contains any outlier values).
With respect to claim 54, Snedden et al. as modified teaches all that is claimed in the rejection of claim 47 but remains silent regarding the scaling the plurality of time-series datasets includes scaling according to the formula:
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64
354
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Greyscale
where R(s) is the value of a given scaled time-series dataset of the plurality of time-series datasets at the given time, s is the value of a corresponding time-series dataset of the plurality of time-series datasets at the given time, Shigh is a high truncation value for the corresponding time-series dataset, and Slow is a low truncation value for the corresponding time-series dataset.
The scaling of data is taught in Snedden et al. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing the instant invention to derive the claimed scaling formula based on the time-series dataset, as there are only a finite number of predictable solutions, with a reasonable expectation of deriving the claimed equation from the taught variables of the prior art. MPEP 2141 III.
With respect to claim 55, Snedden et al. as modified teaches the method wherein, prior to the displaying, the method further includes mapping each time-series dataset of the plurality of time-series datasets to a corresponding plurality of color component intensity values (as Altamura et al. teaches in Fig. 2, s222, color component intensity values are assigned to time-series datasets).
With respect to claim 56, Snedden et al. as modified teaches the method wherein the first color component has a corresponding plurality of discrete first color component intensity values, and further wherein the mapping includes assigning a corresponding discrete first color component intensity value to the first variable at each time of the plurality of corresponding times; (ii) the second color component has a corresponding plurality of discrete second color component intensity values, and further wherein the mapping includes assigning a corresponding discrete second color component intensity value to the second variable at each time of the plurality of corresponding times (as Altamura teaches assigning certain colors according to a color coded plot, [0051] the color code plot is assigned according to intensities based on magnitudes as visual indicators [0034]; and these colored indicators, based on magnitudes are assigned to a series of variables, thereby reading on the claimed invention) and (iii) the plurality of time-series datasets includes the third time-series dataset (i.e. temperature; Altamura [0040]) as and the third color component has a corresponding plurality of discrete third color component intensity values (as Altamura teaches using different colors and intensities to represent the different data in a vector map), and further wherein the mapping includes assigning a corresponding discrete third color component intensity value to the third variable at each time of the plurality of corresponding times as Altamura teaches assigning certain colors according to a color coded plot, [0051] the color code plot is assigned according to intensities based on magnitudes as visual indicators [0034]; and these colored indicators, based on magnitudes are assigned to a series of variables, thereby reading on the claimed invention).
With respect to claim 57, Snedden et al. as modified teaches the method wherein the mapping includes mapping such that intensities of the corresponding variable of each time-series dataset range between a minimum color component value and a maximum color component value (as the combination as a whole teaches normalizing the data within a range and assigning colors that indicate each time-series dataset via the intensity transformation methodologies taught in Altamura).
With respect to claim 58, Snedden et al. as modified teaches all that is claimed in the above rejection of claim 55 but remains silent regarding the minimum color component value is 0 and the maximum color component value is 255.
It has been held that it would have been obvious to try from a finite number of identified, predictable solutions, with a reasonable expectation of success. MPEP 2143(I)(E)
In this instance, a person having ordinary skill in the art, who would be a person having a degree in engineering, has the capability to perform the engineering practice of statistical analysis using a set of collected data to determine the minimum and maximum scale values. Such a statistical analysis contains a finite number of predictable solutions that would result a range being defined, such as the minimum color component value is 0 and the maximum color component value is 255.
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the ranges of Snedden et al. to accommodate the dataset, because such an analysis ensures the range is defined by the dataset itself, thereby ensuring accurate data capturing for display.
With respect to claim 59, Snedden et al. as modified teaches the method wherein the plural-component color space is one of: (i) a two-component color space (i.e. as Altamura teaches in [0051] [0082] which teaches two colors ranging from green to red, thereby reading on a two-component color space).
With respect to claim 60, Snedden et al. as modified teaches the method wherein the first variable includes one of: (i) a slurry flow rate of a slurry stream provided to the hydrocarbon well during a completion operation of the hydrocarbon well [0028] and (iii) a pressure generated within a wellbore when the slurry stream is provided to the hydrocarbon well during the completion operation of the hydrocarbon well [0028].
With respect to claim 61, Snedden et al. as modified teaches the method wherein the second variable includes another one of: (i) a slurry flow rate of a slurry stream provided to the hydrocarbon well during a completion operation of the hydrocarbon well [0028] and (iii) a pressure generated within a wellbore when the slurry stream is provided to the hydrocarbon well during the completion operation of the hydrocarbon well [0028].
With respect to claim 61, Snedden et al. as modified teaches the method wherein the third variable includes (v) the water production rate during production from the hydrocarbon well; (vi) the liquid hydrocarbon production rate during production from the hydrocarbon well (i.e. time series produciton rate; [0033] of Snedden et al..
With respect to claim 64, Snedden et al. a hydrocarbon well [0004], comprising: a wellbore (i.e. borehole; [0004]) extending within a subsurface region [0007]; a computing device [0022]; and a display [0035]; wherein the computing device [0022] is programmed to direct the display [0035] to display a vector map (Fig. 6) utilizing a method of interpreting a plurality of time-series datasets [0025] generated from operation of the hydrocarbon well [0004], the method comprising: obtaining the plurality of time-series datasets (step 11; [0026]), wherein the plurality of time-series datasets is generated from an operation of a hydrocarbon well (Snedden et al. teaches the obtained time series data being data related to downhole pressure and flow rate of hydrocarbon wells) and includes a first time-series dataset that includes values of a first variable at a plurality of corresponding times (i.e. the downhole pressure of the well over time) and a second time-series dataset that includes values of a second variable at the plurality of corresponding times (i.e. the flow rate of the well over time); displaying a vector map (Fig. 6) and making an operational change based on the vector map (as the vector map is displayed so to perform a variety of management activities; [0035]), wherein the operational change comprises modifying at least one parameter (i.e. well injection and/or extractions; [0035]) of a subsequent operation performed on the hydrocarbon well [0035].
Snedden et al. remains silent regarding the vector map includes a time axis and a plurality of points distributed along the time axis at the plurality of corresponding times, wherein a color of each point of the plurality of points is defined in a plural-component color space and includes a first color component at a first intensity and a second color component at a second intensity, and optionally a third color component at a third intensity, and further wherein: (i) the first intensity at a given time of the plurality of corresponding times is based upon a magnitude of the first variable at the given time; and (ii) the second intensity at the given time is based upon a magnitude of the second variable at the given time; and (iii) the third intensity at the given time is based upon a magnitude of the third variable at the given time.
Altamura et al. teaches a similar method that displays a map that includes a time axis [0009] and a plurality of points distributed along the time axis at the plurality of corresponding times (as seen in Fig. 12), wherein a color of each point of the plurality of points (as each point is color-coded based on its respective intensity to form a color intensity plot; [0034-0035]) is defined in a plural-component color space (as Altamura teaches in para. [0093] using color gradients to define each point of the plurality of points in a plural-component color space, as seen in Fig. 12) and includes a first color component at a first intensity (as Altamura teaches in para. [0036] and [0037], Fig. 12 utilizes an intensity transformation, which allows magnitudes of data to be color coded) and a second color (as part of the defined color-coded intensity data) component at a second intensity (depending on a selected color for a specific magnitude defining the intensity), and a third color component at a third intensity (as part of the defined color-coded intensity data), wherein various colors of the vector map (seen in Fig. 12) at a given time (defined by the y axis of the figure) correspond to various combinations of the color components and their corresponding intensities (for example fig. 12 depicts on July 1 2010, the first, second and third color components representing their respective intensity at a use given time, which can be view by the user; [0092]); and further wherein: (i) the first intensity at a given time of the plurality of corresponding times is based upon a magnitude of a first variable (as Altamura teaches the data points can include kilowatts; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a first variable based on the color-coding of that data and its respective intensity); (ii) the second intensity at the given time is based upon a magnitude of the second variable (as Altamura teaches the data points can include gas metrics; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a second variable based on the color-coding of that data and its respective intensity); and (iii) the third intensity at the given time is based upon a magnitude of the third variable (as Altamura teaches the data points can include temperature; [0040]) at the given time (Altamura teaches in [0034] the intensity transformation allows a user to ascertain magnitude of a second variable based on the color-coding of that data and its respective intensity).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the vector map of Snedden et al. to include the color-coded intensity transformation plot as taught in Altamura et al. because Altamura et al. teaches such a modification allows for a user to quickly determined from the displayed data any anomalies and patterns [0083], thereby improving the displaying details of Snedden et al.
The method step of claim 65 are performed during the operation of the rejected well structure of claim 64.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma et al. (2017/0140244) which teaches color-coded sensor data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/Primary Examiner, Art Unit 2853