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 .
Claim Status
Claims 1 and 7-9, and 14-20 are currently pending and under examination herein.
Claims 2-6 and 21-61 were previously canceled.
Claims 10-13 are canceled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/12/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/05/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the list of cited references was considered in full by the examiner. A signed copy of the corresponding 1449 form has been included with this Office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 7-9, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention is directed to a method of characterizing one or more populations in a second set of flow cytometer data. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 1 and 7-9, and 14-20 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 1 recites defining a training gate by a set of vertices; the limitation defining a training gate by a set of vertices can be practically performed in human mind (mental process) because human mind is able to define a training gate, for example drawing a geometric shape with vertices using pen and paper to select specific data points.
Claim 1 further recites generating an image for each of the obtained first and second sets of flow cytometer data by organizing each of the data sets into two-dimensional bins; the limitation generating an image by organizing datasets into bins/grids/pixels can be particularly performed in human mind (mental process) because human mind is able to organize data into bins and assign shades to them and thereby generate an image (See specification, page 3-4).
Claim 1 further recites creating a histogram based on average values of the two-dimensional binned data and a parameter; the limitation creating a histogram is considered a mathematical process since it involves mathematical steps of defining bins, counting/averaging, and calculating bar width and height, as such said limitation falls within mathematical concepts groupings of abstract ideas.
Claim 1 further recites calculating a cumulative distribution function based on the histogram; the limitations calculating a function is considered a mathematical calculation based on a mathematical formula, and as such falls within mathematical concepts groupings of abstract ideas.
Claim 1 further recites determining an image generation value associated with a shade for each two-dimensional bin based on the cumulative distribution function; the limitation determining a value based on a mathematical function fall into mathematical concepts groupings of abstract ideas.
Claim 1 further recites adjusting the training gate with an image registration algorithm; the limitation adjusting a training gate with an algorithm for example mathematical deformation model comprising B-spline warping and computing a coefficient to maximize similarity, are considered formulas and equations and fall into mathematical concept grouping of abstract idea (also a mental process of performing the calculations and adjusting based on the result of the calculation).
Claim 1 further recites extracting the set of vertices defining the training gate from an adjusted generated image of the set of flow cytometry data to accommodate the second set of data; the limitations extracting vertices can be practically performed in human mind (mental process) by using a pen and paper, since humans are capable of extract vertices by overlaying two sets of data (see specification pg. 4).
Claim 1 further recites computing B-spline coefficients that define a function for warping the generated image; the limitation computing coefficients of a function is considered a mathematical calculation, and as such, fall into mathematical concepts groupings of abstract ideas.
Claim 1 further recites applying the function defined by the B-spline coefficients; the limitation applying the function defined by the B-spline coefficients is considered a mathematical calculation, and as such falls within mathematical concepts groupings of abstract ideas.
Claim 1 further recites characterizing one or more populations in the set of flow cytometry data based on the adjusted training gate; the limitation characterizing populations/data points, given the plain meaning of “characterizing”, can be practically performed in human mind (mental process), since human mind is capable of characterizing/defining one or more data points in a dataset based on a result of an analysis.
Claim 7 recites adjusting the training gate by imposing the training gate onto a blank image. The limitation adjusting a training gate can be particularly performed in human mind (mental process) because human mind is able to depict/ draw a geometric shape on a blank image. See specification, page 20; FIG. 3, 301a.
Claim 8 recites using an algorithm to adjust the training gate imposed onto the blank image. The limitation using an algorithm is considered a formula or equation, and thus, falls into mathematical concepts groupings of abstract ideas (also a mental process).
Claim 9 recites overlaying the adjusted training gate to the second set of flow cytometer data. The limitation overlaying the gate can be particularly performed in human mind (mental process) because human mind is able to overlay/superimpose a gate/geometric shape on a set of data points.
Claim 14 recites assigning shades to bins. The limitation assigning shades can be particularly performed in human mind because human mind is able to assign shades to bins (a mental process).
Claim 15 recites assigning black to a bin associated with a value under a threshold. The limitation assigning black can be particularly performed in human mind because human mind is able to assign color to bins (a mental process).
Claims 16-20 provide additional information.
The identified claims recite a law of nature, a natural phenomenon (product of nature) or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. Therefore, the claims are directed to a judicial exception and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims 1 and 7-9, and 14-20 include the following.
Claim 1 recites obtaining a first and a second set of flow cytometer data. Claim 1 further recites displaying a generated image for the first and second sets of data on a graphical user interface, and a processor implemented algorithm/ processor.
The additional elements of a processor implemented algorithm/processor and a display device are generic computer components and/or processes. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the additional element of obtaining flow cytometry data serves to collect the information for use by the abstract idea. Additionally, the step of displaying generated image of data amounts to necessary data gathering and outputting, and as such, considered insignificant extra-solution activity.
Therefore, the additionally recited element amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1 and 7-9, and 14-20 are directed to an abstract idea. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claims 1 and 7-9, and 14-20 include the following.
Claim 1 recites obtaining a first and a second set of flow cytometer data. Claim 1 further recites displaying a generated image for the first and second sets of data on a graphical user interface, and a processor implemented algorithm.
The additional elements of a processor implemented algorithm and a display device are conventional computer components and/or processes and also well-understood, routine, and conventional. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TU Communications LLC v. AV Auto, LLC, 823 F.3d 607,613,118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Furthermore, the additional elements obtaining a first and a second set of flow cytometer data amount to necessary data gathering and outputting. See MPEP 2106.05(g). Additionally, displaying cytometry data using a graphical user interface amount to conventional methods and systems for displaying cytometry data. This position is supported by Verschoor et al. (An introduction to automated flow cytometry gating tools and their implementation, Frontier in Immunology, Volume 6 - 26 July 2015; previously cited). Verschoor reviews automated flow cytometry analysis methods and discloses displaying image of flow cytometry data and characterized populations after gate adjustments (pg. 2-3, Figures 1 & 2).
Therefore, these additional elements are not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea (and/or natural correlation) without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Applicant’s Remarks
Applicant's arguments filed 06/24/2025 have been fully considered but they are not persuasive. Applicant states:
Claim 1 recites the combination of elements of displaying the generated images on a GUI, extracting the vertices defining the training gate from an adjusted generated image of the first set of flow cytometer data, a processor implemented image registration algorithm for performing the warping of the generated image into the adjusted generated image, and characterizing one or more populations based on the adjusted training gate. Similar to Example 37, these additional elements integrate the judicial exception into a practical application (Step 2A, Prong 2: Yes). In particular, the specific manner of characterizing one or more populations in a second set of flow cytometer data based on the adjusted training gate, results in improved characterization of flow cytometer data.
It is the combination of first generating an image of flow cytometer data, displaying the generated image wherein each two-dimensional bin is represented by a pixel of the associated shade, and then adjusting the training gate with a processor implemented algorithm by employing said generated image comprising pixels of various shades that integrates the abstract idea into a practical application. This combination characterizes populations in the flow cytometer data in a way that cannot be achieved when the elements are considered separately. These steps add significantly more to the abstract idea than mere computer implementation.
It is respectfully submitted that the above statement is not persuasive. The Applicant remarks are directed to Step 2A Prong Two of 101 analysis, specifically whether the additional elements integrate the recited judicial exception into a practical application of the exception.
Claim 1 of Example 37, as noted by the Applicant, recites additional elements of receiving a user selection, a graphical user interface, and a processer, where the step of automatically moving most used icons to a position on the GUI closest to the start icon of a computer system by determining amount of use, integrates the judicial exception of ‘determining step’ into a practical application of ‘moving icons automatically’, which provides a specific improvement to the prior GUI systems/computer technology (emphasis added).
As stated above, instant claims, recite the additional elements of obtaining data, displaying data on a graphical user interface, and a processor. These elements amount to necessary data gathering and are generic computer components that do not integrate the judicial exception into a practical application. it is noted that the mere recitation of a generic computer/computer components does not take the claim limitation out of the mathematical and/or mental processes.
Taken as a whole, the instant claims are directed to judicial exception of characterizing populations. It is important to note, the judicial exception alone cannot provide the improvement (See MPEP 2106.04(d) III). The improvement must be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)).
As such, the rejection of instant claims under U.S.C. 101 is maintained.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 7-9, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fainshtein (US20170061657A1; cited previously), in view of Baumgrass (US20220207895A1; newly cited), in view of Jung (Two-dimensional histogram specification using two-dimensional cumulative distribution function, 01 June 2014, Electronics Letters, Volume 50, Pages 842-900; newly cited), and further in view of Jorge-Peñas (Free Form Deformation–Based Image Registration Improves Accuracy of Traction Force Microscopy, PLoS ONE 10(12): e0144184. doi:10.1371/journal. pone.0144184, August 19, 2015; previously cited).
Regarding claim 1, Fainshtein discloses a method of characterizing one or more populations in a second set of flow cytometer data (par. [0125]- [0130] and figure 12, blocks 1202-1210, par. [0018] and [0125]: "a method of controlling graphic display of cytometric events'') of adjusting a training gate (fig. 12, block 1204, par. [0127]: ''At block 1204, a gate is rendered around a population of cytometric events shown by the graphic display.'') prepared from a first set of flow cytometer data (fig. 12, block 1204, par. [0127]: "In rendering the gate, the gate is overlaid upon the first plot.'') to accommodate a second set of flow cytometer data (fig. 12, block 1210, par. [0130]: "the first plot is replaced with a second plot'').
Fainshtein further discloses obtaining a first (fig. 12, block 1202, par. [0126]) and a second set of flow cytometer data (fig. 12, block 1210, par. [0 130]), wherein the first set of flow cytometer data comprises a training gate defined by a set of vertices (par. [0046]);
Fainshtein further discloses generating an image for each of the obtained first and second sets of flow cytometer data by organizing the flow cytometer data into two-dimensional bins; creating a histogram based on average values of the two-dimensional binned data and a parameter (par. [0 126] :"a graphic display including a first plot", and par. [0130]: implicit: "the first plot is replaced with a second plot''; “ a 2D density plot diagram that shows the density of data using two parameters plotted against each other. As with FIG. 3, an x-axis shows the parameter values while a y-axis illustrates the side scatter level… the 2D density plot diagram includes a cluster of data. The cluster of data may include several regions, each region associated with a common density [0074-0076]”; see also, contour plot diagram [0077]; the contour plot diagram uses a two-dimensional array of values that contain the event counts for the histogram… Each level may then be assigned a color or other visual discriminator. The resulting display is a collection of polygons that may be colored according to the level of the data. As with the dot-plot diagram, the contour plot diagram may undergo gating and/or include gates to identify populations of interest [0079] FIG. 3-6).
Further regarding claim 1, Fainshtein does not expressly disclose that values of the two-dimensional binned data is an average value.
Baumgrass discloses a method for classifying selected marker signals from cytometric measurements comprising a first measurement and a second measurement, wherein the first measurement comprises a cytometric measurement acquired from a first sample of particles, and the second measurement comprises a cytometric measurement acquired from a second sample (abstract). Baumgrass further discloses that a pre-processing step can comprise a pre-gating on specific population/cell types [0129] and that pre-processed data can be binned [0133].
Baumgrass further discloses that the detected intensities pN1 lj of a marker lN1 j can be binned in a serial manner, such that a 2-dimensional bin is generated, i.e. the intensities of a first marker can be binned, and in a subsequent step, the intensities of a second marker can be binned. The combination of two binned marker intensities defines a two-dimensional bin [0036]
Baumgrass further discloses that a 2-dimensional binning is performed in one step providing a plurality of two-dimensional bins. Baumgrass further discloses that the one-step 2-dimensional binning is performed by arranging the intensities related to a first and a second marker to each other and putting a grid comprising a plurality of equal sectors on those sorted intensities, wherein each sector of the grid represents each one two-dimensional bin [0037].
Baumgrass further discloses that at least one associated marker function can be determined for each bin, in particular for each two-dimensional bin where the marker function is a statistical value with respect to a marker intensity of a bin, in particular one of a mean value, a median, a minimum value, a maximum value, a standard deviation, a variance, an inter quartile range, a distance, a range, a correlation, and coefficient of variation. A range can be an absolute range, which can be a difference between a maximum value and a minimum value per sample, per area and/or per quadrant. In an embodiment, the range is a relative range which is related to a maximum value and a minimum value of a total of all considered samples, in particular of the first and the second sample. The third marker intensity can particularly be a third binned marker intensity [0040-0045].
Further regarding claim 1, Fainshtein discloses distinguishing populations from one another visually by using a dot-plot diagram [0072]. Baumgrass further discloses that the number of cells in the bins are displayed in grey scale: low values are represented by grey, median values by darker grey and high values by black and that the grid of bins can be extended with a statistical information such as cell density, standard deviation and relative standard error of the means and manual inspection [0179-0180].
Baumgrass further discloses that the graphical representation of each feature set at least one associated marker function and/or the third marker intensity is represented by a colour according to a predefined colour scale or a grey level according to a predefined grey scale (for example, assigning shades to bins) [0100].
Fainshtein and Baumgrass do not expressly disclose calculating a cumulative distribution function.
Jung discloses a method of 2D histogram specification (HS), where given the 2D input and target histograms, the proposed method derives the pairwise pixel-value mapping using the 2D CDF (abstract).
Fainshtein further discloses displaying a generated image of each of the first and second sets of flow cytometer data on a graphical user interface (par. [0012] “the graphics control device includes a memory for storing a display setting for graphically plotting events upon receipt of a message including a triggering event. The display setting may indicate at least one of: a plot display size, a plot type, event display coloring, or a quantity of events to display. In such implementations, the plot generator may be further configured to retrieve the display setting for the triggering event and generate the computer displayable graphic representation using the display setting”. “a graphic display including a first plot of flow cytometry events that may include representation of the plot including instructions to paint pixels, polygons, or other visual indicators at specific locations on a display device [0126]”).
Fainshtein further discloses adjusting with a processor implemented algorithm by extracting the set of vertices defining the training gate from an adjusted generated image of the first set of flow cytometer data to accommodate the generated image of the second set of flow cytometer data (par. [0074]: "The gating can be performed by software algorithms; par [0046]: when a user draws a gate around a set of events, the user uses a mouse to place vertices around a set of data points (by entering a “draw gate” mode and clicking on a series of points within the plot for example), which vertices are connected with lines by the display system as illustrated for the gate 902 in FIG. 9. The system may also have a “modify gate” mode where the user can move the mouse cursor to a vertex or line of a previously drawn gate, click and hold a mouse button, and then with the mouse button down move the mouse to drag the vertex or line to a new location to modify the gate).
Fainshtein further discloses receiving a gate selection signal identifying the gate from a first input device and receiving a triggering event replacing the first plot with a second plot while modifying the visual indicator. In one innovative aspect, a method for generating a contour, a tangible machine-readable storage device having computer-executable instructions stored thereon for generating a contour, and a system for generating a contour are described with reference to the accompanying drawings [0026] (for example, adjusting a training gate using an algorithm).
Baumgrass further discloses a support vector machine approach is used to determine the largest variation between the first and the second feature set. In particular, a distance of the first and the second feature set to a hyperplane can be determined. The accumulated distance can be a measure characterizing the similarity (or the dissimilarity) between the first and the second feature set. Baumgrass further discloses that alternatively, a statistical value, such as a mean value, a variance, a range, a standard deviation and/or a cumulant can be determined for the first and the second feature set for each pair of feature sets, wherein the largest variation can be determined e.g. by a difference or ratio between the statistical value. Baumgrass further discloses that an alternative embodiment is characterized in that an artificial neuronal network is used to determine the variation between the first and the second feature set [0047-0053].
Further regarding claim 1, Fainshtein and Baumgrass do not expressly disclose that the algorithm is an image registration algorithm that computes B- spline coefficients that define a function for warping.
Jorge-Peñas discloses a B-spline-based Free Form Deformation (FFD) technique to model a wide range of local deformations (pg. 3, para. 1).
Jorge-Peñas further discloses computing B- spline coefficients that define a function for warping the generated image and applying the function defined by b-spline coefficient. Jorge-Peñas discloses that the transformation model that warps the reference image during the optimization by a multivariate B-spline function whose control points (coefficients) are the tuning parameters. Jorge-Peñas further discloses (pg. 3, last para.) computing B-spline coefficients/ solving for control point positions via optimization, as evidenced by Loeckx (Loeckx: pg. 641, subsection 2.1, last para.; “To register a floating image F to a reference image R we need to determine the optimal set of parameters φι for the transformation T(Φ) …The parameters φι of the transformation T are the coordinates of the control points φi,j,k “). Jorge-Peñas further discloses that the algorithm overlays a regular mesh over the fixed image and defines the mesh nodes as the control points of B-splines curves. Then, the position of each of these control points is tuned iteratively during the optimization process deforming until it matches (pg. 3 last para., Figure 1, Formula (4)).
Jorge-Peñas further discloses applying the B-spline function defined by the B-spline coefficient/ control points (Jorge-Peñas, pg.4, first para.; in the case of using cubic B-splines as warping functions, the transformation that represents the local deformations and maps the voxel coordinates x = (x,y,z) of Id to the voxel coordinates of Ir).
Fainshtein further discloses characterizing the one or more populations in the second set of flow cytometer data based on the adjusted training gate (par. [0047]: In embodiments, user interaction with the system is improved by allowing automated or semi-automated simultaneous event data display format modification activities and gate drawing/modification activities). Baumgrass discloses characterizing a sample [0051].
Fainshtein further discloses displaying the characterized populations (par [0047]: In some implementations, the system may automatically select a display format based on the characteristics of the event data (e.g., event density near the gate being drawn and/or modified) and/or the particular gate modification action being performed by the user; par [0049]: FIG. 1 shows a functional block diagram for one example of a graphics control system for analyzing and displaying cytometric events).
Further regarding claim 1, Fainshtein does not expressly disclose that the generated image of the first set of flow cytometer data is warped to maximize similarity relative to the generated image of the second set of flow cytometer data. However, Jorge-Peñas discloses calculation of a deformation as a non-rigid image registration process that warps the image of the unstressed material to match the image of the stressed one ((abstract); This warping process is a deformation applied to the relaxed image using the current estimate of the transformation model parameters. Once the distance metric has been evaluated, these parameters are updated as specified by the selected optimization strategy (pg. 3, Methods)).
Regarding claim 7, Fainshtein discloses imposing the training gate onto a blank image (fig. 12, block 1202, par. [0126]: The representation may include instructions to paint pixels, polygons, or other visual indicators at specific locations on a display device).
Regarding claim 8, Fainshtein discloses that the algorithm is configured to adjust the training gate imposed onto the blank image (par. [0074]: "The gating can be performed by software algorithms; par. [0130]: "the first plot is replaced with a second plot while maintaining and/or modifying the gate on the graphic display''). Additionally, Jorge-Peñas discloses the position of each of these control points is tuned iteratively during the optimization process deforming moving image until it matches fix image (pg. 3 last para.).
Regarding claim 9, Fainshtein discloses overlaying the adjusted training gate to the second set of flow cytometer data by applying the vertices of the adjusted gate from the blank image to the generated image of the second set of flow cytometer data (par. [0127]: In rendering the gate, the gate is overlaid upon the first plot; par. [0130] the first plot is replaced with a second plot while maintaining and/or modifying the gate on the graphic display; Fig. 12). Baumgrass discloses that The bins in grey are overlaid by plotting an additional marker function on the same plane characterized by the first and the second binned intensities. This provides an easy characterization of cells in certain bins [0151].
Regarding claims 14-18, Fainshtein and Jorge-Peñas do not expressly disclose calculating a cumulative distribution function based on the histogram, and determining an image generation value associated with each bin based on the cumulative distribution function and details about shading. Baumgrass discloses that the graphical representation of each feature set, particularly of the feature sets comprised in the at least one selected pair of feature sets, comprises a coordinate system with an abscissa and an ordinate, wherein the two binned marker intensities of the feature set are plotted along the abscissa and the ordinate, and the at least one associated marker function and/or the third marker intensity is represented by a colour according to a predefined colour scale or a grey level according to a predefined grey scale [0100]. Baumgrass further discloses that the marker intensity indicated by means of as a grey scale look-up table [ 0160]. further regarding claims 15 and 16, Baumgrass discloses that measurement data acquired from cells falling into a specific bin are captured and displayed if a minimum number of five cells is reached. This number can be user defined, and might be higher or lower for other instances. The number of cells in the bins are displayed in grey scale: low values are represented by grey, median values by darker grey and high values by black [0179].
Regarding claim 19, Fainshtein discloses that the training gate is drawn by a user (The gating can be performed automatically by software algorithms, or manually by a human; or it may be initially performed automatically and then adjusted manually. In manual gating, the operator draws or adjusts the gates by clicking the plot with computer mouse and dragging the mouse [0074]).
Regarding claim 20, Fainshtein discloses that the adjusted training gate possesses a different shape than the training gate (FIG. 9-10 different shapes of training gate before and after adjustment).
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Fainshtein, Buamgrass, Jung, and Jorge-Peñas, the examiner concludes that this combination represents applying a known techniques to a known method. Fainshtein only disclosed obtaining a first and a second set of flow cytometer data, generating an image for each of the obtained first and second sets of flow cytometer data; adjusting the training gate with a processor implemented algorithm by extracting the set of vertices defining the training gate from an adjusted generated image of the first set of flow cytometer data to accommodate the generated image of the second set of flow cytometer data. In the same field of research, Baumgrass and Jung provided the specifics of image generation, 2D-bin and histogram generation, and calculating a cumulative distribution function. Combining the flow cytometry data gating strategy of Fainshtein with known image generation strategies of Baumgrass and Jung would have allowed for quantifying multiple parameters simultaneously revealing trends in data by using average values, as discloses by Baumgrass, and would have allowed for precise comparison of datasets, as disclosed by Jung. Furthermore, the known Image registration algorithm and B-Spline warping analysis of Jorge-Peñas would have allowed for maximizing similarity between first and second flow cytometer data. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at combining these methods. This combination would have been expected to have provided a more accurate identification of data. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Response to Applicant’s Remarks
Applicant states:
Fainshtein and Mata-Fink fail to teach or suggest the elements of creating a histogram based on average values of the two-dimensional binned data and a parameter, calculating a cumulative distribution function based on the histogram, and determining an image generation value associated with a shade for each two-dimensional bin based on the cumulative distribution function.
As Jorge-Penas was cited merely for the element of computing B-spline coefficients, Jorge- Penas fails to make up for the deficiencies in Fainshtein and Mata-Fink. Accordingly, Claim 1 and all claims that depend therefrom are not obvious over Fainshtein and Jorge-Penas in view of Mata-Fink, and this rejection may be withdrawn.
It is respectfully submitted that this is not persuasive. The amendments to claims necessitated a new round of art rejection. As such, the combination of Fainshtein, Baumgrass, Jung, and Jorge-Peñas discloses all limitations of instant claims.
Conclusion
No claims are allowed.
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/G.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686