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. Specification The abstract of the disclosure is objected to because it is over 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Rejections - 35 USC § 102 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. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim s 1-6 and 10-12 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by J imenez (“ Start-Up Flow Visualization Of Viscoplastic Materials In Horizontal Pipes Using Particle Image Velocimetry ”) . Regarding Claim 1, Jimenez teaches a method for obtaining the velocity field when restarting the flow of complex materials in a transient regime, the method comprising: defining the number of pairs of images to be obtained; Roman letters and symbols: “NT Number of total images.” defining the time parameters between pulses and frequency; Greek letters: “∆t Time between pulses ” 4.4.3 Velocity vector field measurements and flow patterns obtained by PIV technique : “ For initial validation with Newtonian fluid in the experimental setup, the frequency were set at 70 and 100 Hz … the time between pulses was established considering the displacement of the seed particles in an interrogation area and the integral time scale as the upper limit of the time between pulses. ” obtaining and recording a plurality of pairs of images (Figure 4.8 (shown above)) ; processing the recorded pairs of images (Figure 4.11 (shown above)) ; checking the tracer particle displacement criterion; 4.4.3 Velocity vector field measurements and flow patterns obtained by PIV technique : “ T he time between pulses was established considering the displacement of the seed particles in an interrogation area and the integral time scale as the upper limit of the time between pulses. ” extracting the first frame of each image from each pair of images (Figure 4.8 ( shown above)) ; Explanation: Figure 4.8 shows first frame (t) and second frame (t’). As shown in the figure, this two-frame scheme requires using the first frame of each pair. uniting the first frames extracted from each image of each pair of images according to the displacement criterion of the tracer particles, creating new pairs of image frames (Figure 4.11 (shown above)) ; 2.3.1 Principle of Particle Image Velocimetry (PIV): “ T he local displacement vector of the particle images between the two illuminations is determined for each interrogation window utilizing a spatially statistical correlation function.” Explanation: 2.3.1 teaches displacement-based correlation between frames and Figure 4.11 teaches forming correlated pairs based on displacement. calculating the correction factor for the time between frames (t’) of the frames of each of the new pairs of image frames (Figure 4.11 (shown above)) ; Figure 4.10 – Dimensionless time scale established to analyze the flow reset of fluids C-25 and C-50. The dimensionless time t’ [-] was obtained from tc [s]/t[s]. Explanation: Time between frames is explicitly determined as part of processing in Figure 4.11 . Dimensionless time/corrected time explicitly disclosed in Figure 4.10 . applying correlation overlap to calculate flow velocity vectors (Figure 2.8 (shown below )) ; 4.4.3 Velocity vector field measurements and flow patterns obtained by PIV technique: “Taking into account that the flow velocities correspond to low values of pressure drop, after the calibration process, the visualization area was established at 125mm×22mm and the final interrogation area was 32pixels × 32pixels with an overlap of 50%. ” Explanation: PIV correlation overlap = interrogation window overlap. correlating the images of the new pairs of image frames, using adaptive correlation (Table 4.4 (shown above)) ; 4.3.4.3 Image correlation: “ For the correlation of the images, the Adaptive PIV method was used , which is a method automatic and adaptive for calculating velocity vectors based on particle images. In other words, Adaptive PIV iteratively adjusted the size and shape of the individual interrogation areas (IA) to adapt to local seeding densities and flow gradients .” obtaining the flow velocity vector map (Figure 4.11 (shown above)) ; calculating and applying the correction factor (t’) to obtain another vector map (Figure 4.1 0 (shown above)) ; Explanation: Figure 4.10 shows time normalization/corrected velocity fields which corresponds to corrected vector fields over time. applying a vector statistical function with the corrected velocity (Figure 4.11 (shown above)) ; obtaining the flow velocity profile in a transient regime; 1.3.1 General Objective: “The optical method of flow visualization Particle Image Velocimetry (PIV) will be performed to study materials’ behavior in transient flow regime .” Figure 5.10 – Evolution of velocity profile (u/U¯) for the fluid C-25 measured in three dimensionless times : a), b), c) for t 0= 0.74; d), e), f) for t 0= 0.94, and g), h), i) for t 0= 15. u 0 s is the slip velocity dimensionless by the mean velocity (us/U¯). and obtaining the deformation map in a transient regime . 1.3.1 General Objective: “The optical method of flow visualization Particle Image Velocimetry (PIV) will be performed to study materials’ behavior in transient flow regime .” Figure 5.5 – a) Dimensionless mean statistic velocity vector field for the fluid C-50, and measured for τw = 50.25 Pa. b) Standard deviation of mean statistic velocity, and c) dimensionless deformation map. Results presented for the arithmetic mean over 600 instantaneous fields measured after to reach the steady state at t 0= 15. Regarding Claim 2, Jimenez teaches t he method according to claim 1, wherein the images are obtained by the Particle Image Velocimetry (PIV) technique. 1.2 Problem Assessment: “Based on this lack of quantitative knowledge that can be used to better understand the behavior of yield stress fluids, such as paraffinic crude oil, this research uses Particle Image Velocimetry (PIV) as a non-intrusive technique to visualization and measurement of flow .” Regarding Claim 3, Jimenez teaches t he method according to claim 1, wherein the number of pairs of images to be obtained is 500 to 1000 pairs of images. Figure 5.5 – a) Dimensionless mean statistic velocity vector field for the fluid C-50, and measured for τw = 50.25 Pa. b) Standard deviation of mean statistic velocity, and c) dimensionless deformation map. Results presented for the arithmetic mean over 600 instantaneous fields measured after to reach the steady state at t 0= 15 . Regarding Claim 4, Jimenez teaches t he method according to claim 1, wherein each image of the pairs of images obtained has two frames (Figure 4.8 (shown above)) . Explanation: PIV uses image pairs consisting of frame A and frame B (correlation). Regarding Claim 5, Jimenez teaches t he method according to claim 1, wherein the time parameters between pulses and frequency are defined based on the expected average flow velocity, which is calculated for the steady state condition using the expression for the velocity of a fluid with yield stress, through equation 1; and, in the case of a Newtonian fluid, through equation 2 (2.2.3 Hagen-Poiseuille flow in the presence of wall slip and 4.4.4 Validation of PIV measurements , Equations 2.44 and 4.16 ) : where C n is expressed as equation 3, below ( 4.4.4 Validation of PIV measurements , Equation 4.17 ) : where R is the pipe radius, Δp is the pressure gradient, μ is the dynamic viscosity, L is the pipe length, r/R is the radius ratio, J 0 and J 1 are the Fourier-Bessel functions, λ n are the eigenvalues and t is the time. 4.4.4 Validation of PIV measurements: Here ∆p is a pressure differential, (R) is the radius of the pipe, (µ) is the dynamic viscosity, (L) test section length, (r) radius ratio, (J0) and (J1) are Fourier-Bessel function, ( λn ) eigenvalues and (t) is the time. Regarding Claim 6, Jimenez teaches t he method according to claim 1, wherein the step of processing the recorded pairs of images, comprises: improving the resolution and eliminating light refractions in the images, which include: a) calculation of the average intensity of the corresponding pixels in all selected images, considering the particles that present movement, wherein the calculation of the average intensity of the pixels is carried out by assigning a value to the intensity of the light captured by each pixel; 2.3.1 Principle of Particle Image Velocimetry (PIV): “Then, the digital images are processed. The processing includes removing noise, smoothing, converting the gray-scale images to binary images, labeling the particles, and calculating the center of gravity coordinates of the particles .” 4.3.3 Test section: “According to Tab. 4.3 the refraction index of the acrylic and the fluids to be used is not equal, but close. This difference in the refraction index can lead to slight distortions of the captured image. To correct the image distortions some features of the Dynamic Studio software was used, as exposed further in following section .” ’ Explanation: The reference teaches refraction-related image distortion and software-based correction, which directly supports elimination light refractions and improving visualization and image quality during processing. The processing operations require pixel-intensity calculations which is standard PIV preprocessing, therefore showing average intensity calculation, pixel intensity assignment, and particle - based pixel selection. b) performing an arithmetic subtraction operation on what is fixed in the image and what is in motion, that is, filtering out the particles that do not show movement and leaving only the particles that show displacement between the interrogation windows; 2.3.1 Principle of Particle Image Velocimetry (PIV): “ Evaluation: the displacement of the particle images between the light pulses has to be determined through evaluation of the PIV recordings … Then, based upon the definition of velocity, i.e., the first derivative of position with respect to time, the technique consists of measuring the displacement of fluid (∆x) over a given time interval (∆t) …the local displacement vector of the particle images between the two illuminations is determined for each interrogation window utilizing a spatially statistical correlation function.” Explanation: This corresponds to subtraction/difference operations used to isolate moving particles. and applying a mask to the images to delimit the area of interest to be correlated, performing the correlations within the visualized area and reducing the error or appearance of spurious vectors. 2.3.1 Principle of Particle Image Velocimetry (PIV) : The digital PIV recording is divided into small areas called “interrogation windows” . The local displacement vector of the particle images between the two illuminations is determined for each interrogation window utilizing a spatially statistical correlation function . Explanation: This is equivalent to masking/ROI delimitation to perform correlations only in selected regions and reduc e spurious vectors. Regarding Claim 10, Jimenez teaches t he method according to claim 1, characterized in that the correlation overlap for calculating flow velocity vectors is applied based on the movement of particles in the new pairs of image frames obtained in the uniting step of the first frames extracted from each image of each pair of images. 4.4.3 Velocity vector field measurements and flow patterns obtained by PIV technique: “Taking into account that the flow velocities correspond to low values of pressure drop, after the calibration process, the visualization area was established at 125mm×22mm and the final interrogation area was 32pixels × 32pixels with an overlap of 50%. ” 2.3.1 Principle of Particle Image Velocimetry (PIV) : The digital PIV recording is divided into small areas called “interrogation windows” . The local displacement vector of the particle images between the two illuminations is determined for each interrogation window utilizing a spatially statistical correlation function . Explanation: The reference discloses overlap during interrogation overlap, which shows correlation overlap, velocity vector calculation, and interrogation windows. Regarding Claim 11, Jimenez teaches t he method according to claim 1, wherein applying correlation overlap to calculate flow velocity vectors comprises iteratively adjusting the size and shape of the interrogation window, adapting the number of tracer particles with a 50% correlation overlap to obtain the flow velocity vector map. 4.3.4.3 Image correlation: “In other words, Adaptive PIV iteratively adjusted the size and shape of the individual interrogation areas (IA) to adapt to local seeding densities and flow gradients .” 4.4.3 Velocity vector field measurements and flow patterns obtained by PIV technique: “Taking into account that the flow velocities correspond to low values of pressure drop, after the calibration process, the visualization area was established at 125mm×22mm and the final interrogation area was 32pixels × 32pixels with an overlap of 50%. ” Regarding Claim 12, Jimenez teaches t he method according to claim 1, wherein obtaining the flow velocity vector map includes the creation of a vector map from the correlations of the particle positions (Figure 4.11 (shown above)) . 2.3.1 Principle of Particle Image Velocimetry (PIV) : The digital PIV recording is divided into small areas called “interrogation windows” . The local displacement vector of the particle images between the two illuminations is determined for each interrogation window utilizing a spatially statistical correlation function . 4.3.4.3 Image correlation: “For the correlation of the images, the Adaptive PIV method was used, which is a method automatic and adaptive for calculating velocity vectors based on particle images .” Explanation: This describes creation of a velocity vector map from correlations. 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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Jimenez in view of Raffel et. al (“Particle Image Velocimetry: A Practical Guide”) . Regarding Claim 7, Jimenez teaches t he method according to claim 1, but fails to teach that checking the tracer particle displacement criterion is carried out using the displacement criterion of 1/4 of the interrogation window , which has the size of 32 x 32 pixels. While Jimenez teaches an interrogation window size of 32 x 32 pixels, he fails to disclose that checking the tracer particle displacement criterion is carried out using the displacement criterion of 1/4 of the interrogation window . However, Raffel teaches selecting interrogation parameters according to the well-known one quarter rule governing interrogation windows. Specifically, Raffel describes “standard digital interrogation using interrogation windows complying with the one quarter rule, which limits particle displacement relative to interrogation window size” (Page 151). Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply the one-quarter displacement rule taught by Raffel to the PIV method of Jimenez because Jimenez relies on accurate correlation of particle displacement within interrogation windows to obtain reliable velocity vectors. Since Jimenez seeks reliable displacement estimation during PIV processing, a skilled artisan would have been motivated to select a known displacement criterion, such as Raffel’s one-quarter rule, to improve correlation accuracy and reduce measurement error. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Jimenez in view of Duncan et. al (“ Universal outlier detection for particle image velocimetry (PIV) and particle tracking velocimetry (PTV) data ”) . Regarding Claim 8, Jimenez teaches t he method according to claim 1, but fails to teach that checking the tracer particle displacement criterion is carried out by measuring the displacement of tracer particles that are added to the fluid before measurements, wherein the displacement of the tracer particle is calculated using equation 4 , where U 0 is the displacement of the particle, U m is the average of the displacement of the particle using the neighboring displacements, r m is the average residual (the average of the natural variation of the data) of the displacements of the neighboring particles and ε is the minimum level of normalization that has been established at 0.1, that is, ε represents the acceptable level of fluctuation in the particle displacement correlation. However, Duncan teaches a universal outlier detection algorithm for PIV in which the displacement (velocity) of a particle is compared to the median of neighboring displacements using a normalized residual equation that is identical to equation 4 in claim 8 (Section 2 Algorithm, Equation 1) , where where U 0 is the velocity measured at the data point in question, U m is the median of its neighbors, r m is the median of the residual of each neighbor’s value, and ε is the tolerance. Duncan also teaches that ε is the minimum level of normalization that has been established at 0.1, that is, ε represents the acceptable level of fluctuation in the particle displacement correlation , stating that “ Knowing that the tolerance has been traditionally set to 0.1 pixel, the new tolerance can be adaptively altered based on a median distance so that εa (med(di) + εa ) = 0.1. The value of 0.1 ( Westerweel and Scarano (2005)) for a tolerance is arbitrary but was found to be a good value ” (Section 2 Algorithm ). Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply this universal outlier detection algorithm into Jimenez’s PIV method. Duncan explains that this normalized residual method is widely used to detect spurious vectors, improve accuracy of PIV displacement measurements, and provide a universal threshold for identifying invalid particle displacement data. Because Jimenez relies on accurate displacement estimation during PIV processing, a person of ordinary skill in the art would have been motivated to apply the normalized residual validation method of Duncan in order to improve reliability and remove spurious vectors. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jimenez in view of Kara et. al (“ Generalized Knudsen Number for Unsteady Fluid Flow ”) . Regarding Claim 9, Jimenez teaches t he method according to claim 1, but fails to teach that the correction factor for the time between frames (t’) is given according to equation 5, t’ = t/T -1 , where T is the frequency, t is the time between pulses and t’ is the corrected time. While Jimenez teaches a correction factor for time, a mathematical expression defining corrected/dimensionless time, and time between pulses as the temporal parameter, he does not explicitly express the correction factor using frequency. However, Kara teaches nondimensional time defined using frequency, stating that “ After non- dimensionalization with ˆu = u / c, tˆ = ω0t and ∇ ˆ = l ∇ … ” Kara also teaches frequency-based temporal scaling, stating that “t he scaling parameter here is the Weissenberg number, Wi = ω0τ …” These disclosures explicitly show time multiplied by frequency, dimensionless/corrected time using frequency, and an equation equivalent to equation 5 in claim 9. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to express Jimenez’s correction factor using frequency as taught by Kara’s nondimensionalization because both references address temporal normalization of unsteady flow measurements. Kara explains that unsteady flow behavior is governed by scaling using frequency-based dimensionless parameters and that experimental data collapse when variables are nondimensionalized using frequency. Because Kara teaches expressing time in dimensionless form using frequency, a person of ordinary skill in the art would have been motivated to express Jimenez’s corrected/dimensionless time using frequency as an equivalent temporal normalization parameter in order to standardize temporal scaling across acquisition rates, express corrected time using commonly interchangeable parameters (time interval vs frequency), and improve comparison between experiments performed at different frame rates. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jimenez in view of Scarano (“ Iterative image deformation methods in PIV ”) . Regarding Claim 13, Jimenez teaches t he method according to claim 1, but fails to teach that calculating and applying the correction factor (t’) to obtain another vector map comprises multiplying each velocity vector obtained by the new time (t’) between frames, obtaining another vector map with the corrected velocity. While Jimenez teaches velocity vectors, vector maps, and corrected/dimensionless time associated with velocity fields, he does not explicitly disclose multiplying each velocity vector by the corrected time to generate another vector map. However, Scarano teaches that velocity vectors are obtained from displacement over time, stating that “t he image processing returns the displacement (i.e. velocity) vector distribution from the particle image of the recordings ” (Background). Scarano further teaches the mathematical relationship between displacement, velocity, and time, stating that “f or an image pair the velocity within the interrogation region is then approximated by the expression : u ≈ d/(t1-t2)” (Cross-correlation image matching). Scarano also explains that iterative processing uses previous velocity measurements to correct subsequent evaluations, stating that “i f an estimate of the displacement field is available, for instance by conventional cross-correlation analysis, then such a result can be used to apply the convection equation to the scalar intensity field …t hen the subsequent interrogation is still performed via cross-correlation …” (Cross-correlation image matching). These teachings show that velocity vectors depend directly on the time between frames and that velocity fields are corrected using updated displacement/time information. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Jimenez to multiply velocity vectors by a corrected time as shown in Claim 13. Because Scarano teaches that velocity vectors are computed from displacement over a time interval and that iterative methods use previous velocity estimates to correct subsequent vector evaluations , Scarano provides explicit motivation to apply a corrected time value to existing velocity vectors to obtain a corrected velocity vector map. Applying Jimenez’s corrected time (t’) to velocity vectors therefore represents applying known PIV velocity-time relationships, correcting vector fields using updated time information, and a predictable refinement step in iterative PIV processing. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hain and Kahler (“ Fundamentals of multiframe particle image velocimetry (PIV) ”) teach a strategy for the evaluation of time-resolved PIV image sequences , where t he primary aim of the method is to increase the accuracy and dynamic range by locally adopting the particle image displacement for each interrogation window to overcome the largest drawback of PIV. It would also render a 102 rejection for claims 1, 2, 4, 10, and 12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT WILLIAM ADU-JAMFI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571) 272-9298 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-T 8:00-6:00 . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Andrew Bee can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 270-5183 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WILLIAM ADU-JAMFI/ Examiner, Art Unit 2677 /ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677