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 .
Priority
The Examiner acknowledges the claim to priority for the provisional application 62/678,791 dated May 31st 2018. However, the provisional application does not provide support for the claims in the present application. For example, there is no recitation in the provisional application’s drawings, specification, or claims of a “menu” for selecting parameters as claimed in claim 1 of the present application. However, all of the claimed elements appear in the continuation application 17/103,414, filed May 31st 2019. Therefore, the priority date for the claims of the present application is understood to be May 31st 2019.
Claim Objections
Claim 3 objected to because of the following informalities:
Claim 3 recites: “The computing device of claim, wherein”. The claim number indicating which claim Claim 3 depends on is missing, and should be added to the claim. For purposes of §103, the Examiner interpreted Claim 3 to depend on claim 1.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 3, 4, 6, 7, 8, 12, 13, 16, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1).
Regarding claim 1:
Du teaches:
A computing device for characterizing and selecting micro-objects in a microfluidic device (Du: microfluidic device 100 [0074]; Du: The method further comprises selecting, for one or more micro-objects of the set of micro-objects [0023]), the computing device comprising a display screen (Du: system 150 can include an imaging device 194. In some embodiments, the imaging device 194 comprises a light modulating subsystem 404. […] the light modulating subsystem 404 can include a device that produces its own light (and thus dispenses with the need for a light source 402), such as […] a transmissive liquid crystal display (LCD). [0157]), the computing device being configured to
Du fails to explicitly teach:
display on the screen a menu for selecting a first parameter, selected from a provided parameter list, for characterizing a set of detected micro-objects, and the computing device being configured to
display on the screen a plot of the detected micro-object set based on the selected first parameter,
wherein the provided parameter list is a limited list of parameters offered within the menu, each of the parameters in the list being selectable to characterize the set of detected micro-objects based on the associated parameter, and wherein the display screen
enables selection of a sub-population of the set of detected micro-objects based on at least one selected threshold value for the selected first parameter, and
enables display of the detected micro-object set by visually differentiating the sub-population meeting the at least one selected threshold from the remaining micro-objects of the detected set.
Joanidopoulos teaches:
display on the screen a menu for selecting a first parameter, selected from a provided parameter list, for characterizing a set of detected objects (Joanidopoulos: FIG. 14 shows the user interface for defining the parameters (features) which are to be measured on the mask. For example, features from a list containing a total of approximately 100 different features may be selected. [0256]), and the computing device being configured to
display on the screen a plot of the detected object (Joanidopoulos: If necessary, mask detection is adjusted (FIG. 13, which illustrates object detection and segmentation processes, for example by applying thresholds). Sub-objects which are found on or in the masks of the main objects may optionally be defined. [0251]) set based on the selected first parameter (Joanidopoulos: Conventionally, data on parameters and features obtained from single objects are plotted in one-dimensional or two-dimensional histograms. It is then possible to select sub-populations of the detected objects with particular properties in histograms of this type. [0002]),
wherein the provided parameter list is a limited list of parameters offered within the menu, each of the parameters in the list being selectable to characterize the set of detected objects based on the associated parameter (Joanidopoulos: FIG. 14 shows the user interface for defining the parameters (features) which are to be measured on the mask. For example, features from a list containing a total of approximately 100 different features may be selected. [0256]; see Note 1A), and wherein the display screen
enables selection of a sub-group of the set of detected objects (Joanidopoulos: It is then possible to select sub-populations of the detected objects with particular properties in histograms of this type [0002]) based on at least one selected threshold value for the selected first parameter (Joanidopoulos: FIG. 13 shows screenshots of object detection and segmentation, for example by applying thresholds. [0268]), and
enables display of the detected object set by visually differentiating the sub-group meeting the at least one selected threshold from the remaining objects of the detected set (see Note 1B).
Note 1A: Joanidopoulos showcases “an example of a mask in sub-FIG. 6c produced by binarisation, for measurements in the original image used as a basis for said mask.” [0113]. In [0256] cited above, Joanidopoulos teaches that “FIG. 14 shows the user interface for defining the parameters (features) which are to be measured on the mask.” In other words, the mask image characterizing the cells in Fig. 6C may change based on the selected parameter from the list in Fig. 14.
Note 1B: Joanidopoulos teaches that “FIG. 13 shows screenshots of object detection and segmentation, for example by applying thresholds” [0268] and that “There are still nucleii present which are not well segmented” [0268]. Joanidopoulos showcases rectangular boundaries around some of the cells. The Examiner submits that the rectangular boundaries visually differentiate the selected subgroup from the remaining objects based on a threshold.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Joanidopoulos with Du. Using a menu to determine parameters for plotting a visual representation, as in Joanidopoulos, would benefit the Du teachings by enabling “kinetic or dynamic data, which can be represented in curves and are taken directly or indirectly from time-lapse images, to be analysed, specifically also for the case in which kinetic data of this type are available simultaneously for a large number of individual objects and are to be evaluated as a group.” [0010].
Regarding claim 2:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the provided parameter list provides parameters selected from the group (see Note 2A) consisting of Circularity (Joanidopoulos: Circularity Factor, Fig. 14; see Note 2B), CentroidXPixels, CentroidYPixels, CentroidXMicrons, CentroidYMicrons, CentroidXMicronsPenRelative, CentroidYMicronsPenRelative, NearestNeighborMicrons, DiameterMicrons, VolumeFemtoliters, BackgroundAreaMicrons, MeanBrightness, MinBrightness, MaxBrightness, MedianBrightness, BackgroundMedianBrightness, DeltaMedianBrightness, DeltaMaxBrightness, LogMeanBrightness, LogMaxBrightness, LogMedianBrightness, LogDeltaMaxBrightness, LogDeltaMedianBrightnessCV, BackgroundCV, LogDeltaBrightnessMaxToBackgroundRatio, LogDeltaBrightnessSum, FluidChannelNumber, FieldOfView, Cell Count, CellsPerPe, and the change with respect to time of any of the foregoing parameters.
Note 2A: Claim 2 is a Markush claim (see MPEP 2117). “Treatment of claims reciting alternatives is not governed by the particular format used (e.g., alternatives may be set forth as “a material selected from the group consisting of A, B, and C” or “wherein the material is A, B, or C”).” MPEP 2117(I). Accordingly, the parameters listed in claim 2 are treated as alternatives instead of a requirement that every parameter is taught in the prior art.
Note 2B: Joanidopoulos showcases in Fig. 14 a dropdown that includes various parameters. Among these parameters is the Circularity Factor, which the Examiner submits is analogous to the claimed Circularity parameter.
Regarding claim 3:
Du in view of Joanidopoulos teaches:
The computing device of claim, wherein the display screen is a graphic user interface (Joanidopoulos: The method according to the invention may advantageously be performed by using a piece of software with a graphical user interface [0039]).
Regarding claim 4:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the threshold comprises one or more members selected from the group consisting of: an upper threshold value and a lower threshold value (Joanidopoulos: Regions linked by Boolean operators are used for classification. The regions may be defined by quadrants, ranges, polygons or other one-dimensional or two-dimensional classifiers. [0258-0259]; see Note 4A).
Note 4A: The Examiner submits that a “range” as taught by Joanidopoulos would include an upper and lower value used to define the region.
Regarding claim 6:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the visual differentiation is represented by different colors between the sub-population meeting the threshold from the remaining micro-objects of the detected set (Joanidopoulos: FIG. 31 shows a gallery of passive cells and FIG. 32 shows a gallery of active cells and each of the two figures shows one of two point clouds which can already be differentiated in a colour representation of the histogram in FIG. 34 despite relatively small differences in intensity [0307]; see Note 6A).
Note 6A: In [0307], Joanidopoulos teaches that one group of cells may be differentiated by another based on color. Therefore, the Examiner submits that it would be obvious to one of ordinary skill in the art to differentiate the selected sub-population from the remaining micro-objects based on color.
Regarding claim 7:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the menu displayed on the screen is further configured for selecting a second parameter (Joanidopoulos: detecting second features, manifesting themselves directly or indirectly as differences between a plurality of the digital images of the series, of individual objects or sub-objects identified in the digital images [0013]), selected from the provided parameter list (Joanidopoulos: features from a list containing a total of approximately 100 different features may be selected. [0254]), for characterizing the set of detected micro-objects also characterized by the first parameter (Joanidopoulos: A list of the features to be measured in each image of the time series (static/stationary features or temporary features) is shown in FIG. 30 [0304]; see Note 7A).
Note 7A: In Fig. 30, Joanidopoulos showcases that multiple parameters may be added to a list: “A list of the features to be measured in each image of the time series (static/stationary features or temporary features) is shown in FIG. 30” [0304]. It follows that the teachings that apply to a first feature in Joanidopoulos may apply to any further (second, third, etc.) features added to the list.
Regarding claim 8:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the menu displayed on the screen is further configured for selecting a second parameter (Joanidopoulos: detecting second features, manifesting themselves directly or indirectly as differences between a plurality of the digital images of the series, of individual objects or sub-objects identified in the digital images [0013]), selected from the provided parameter list (Joanidopoulos: features from a list containing a total of approximately 100 different features may be selected. [0254]), for characterizing the sub-population of detected micro-objects meeting the at least one threshold value for the first parameter (Joanidopoulos: at least one second classifier relating to at least one first feature and at least one second feature and being able to be applied to first feature data of the first feature data set and second feature data of the second feature data set or to feature data combined from first feature data and second feature data. [0071]).
Note 8A: In [0071], Joanidopoulos teaches that a second classifier may classify (“be applied to”) feature data combined from a first feature and second feature. Therefore, the Examiner submits that it would be obvious to characterize first features based on a second feature.
Regarding claim 12:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the computing device is further configured to display on the screen an imaging menu for selecting an imaging parameter, selected from a provided imaging parameter list, for imaging at least a portion of the microfluidic device (see Note 12A).
Note 12A: The specification of the present application teaches: “In accordance with various embodiments, the imaging parameter can comprise a filter or "cube" type. […] Cube types can include brightfield (e.g., non-filtered, sampling the entire visible spectrum; identified as "OEP' in Figure 18, various fluorescent filters configured to detect specific fluorescent fluorophores (e.g., DAPI, Texas Red, Cy5, FITC, and the like). infrared filters, ultraviolet filters. etc.” [0372]. Therefore, the Examiner understands filters such as DAPI and Texas Red to be imaging parameters.
As discussed in the rejection of claim 1, Fig. 14 showcases a dropdown for each feature shown in the “Measurement” column on the left of Figure 14. In Fig. 30, Joanidopoulos showcases that the Color Channel corresponding to each parameter is also a dropdown element. The dropdown reads GFP, which corresponds to the Channel column in the left window.
In Fig. 14, a different set of parameters is shown, and most notably, the Channel is shown to have other options such as “dapi”, “bright”, and “TxRed”. Therefore, the Examiner submits a PHOSITA would understand that the Color Channel dropdown corresponds to a list where the user can select an imaging parameter.
Regarding claim 13:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above), wherein the computing device is further configured to display on the screen an imaging menu for selecting a plurality of imaging parameters, selected from a provided imaging parameter list, for imaging at least a portion of the microfluidic device (see Note 13A).
Note 13A: In Note 12A, it was discussed that similar to the feature selection dropdown showcased in Fig. 14 of Joanidopoulos, the Color Channel element (seen in Fig. 30 on the right) is also a dropdown. The Examiner submits that it would be obvious to one of ordinary skill in the art to display the available color channels as a list such that one can be selected.
Regarding claim 16:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above), wherein the imaging parameter comprises a fluorescent cube type (Joanidopoulos: it is now possible to display the cell nucleii selected in this way in a further projection of the feature space, specifying in this case the intensities in a first channel (dapi channel, blue), [0262] (emphasis added); see also Note 16A).
Note 16A: The specification of the present application teaches: “In accordance with various embodiments, the imaging parameter can comprise a filter or "cube" type. […] Cube types can include brightfield (e.g., non-filtered, sampling the entire visible spectrum; identified as "OEP' in Figure 18, various fluorescent filters configured to detect specific fluorescent fluorophores (e.g., DAPI, Texas Red, Cy5, FITC, and the like). infrared filters, ultraviolet filters. etc.” [0372].
Notably, Joanidopoulos teaches that DAPI can be used to display selected cell nuclei based on a DAPI channel: “it is now possible to display the cell nucleii selected in this way in a further projection of the feature space, specifying in this case the intensities in a first channel (dapi channel, blue) and in a channel (repair marker channel, red)” [0262]. In Fig. 15 of Joanidopoulos, it is visible that the DAPI parameter is selectable within a dropdown element:
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Cropped variant of Figure 15 of Joanidopoulos.
The specification also recites Texas Red as a cube type, which Joanidopoulos also discloses: “The objects are segmented in a first step. This is carried out in the more powerful channel (TxRed) (see FIG. 29). [0302].
Therefore, the Examiner submits that Joanidopoulos teaches at least one fluorescent cube type as an imaging parameter.
Regarding claim 17:
Du in view of Joanidopoulos teaches:
The computing device of claim 16, wherein the fluorescent cube type is configured to detect FITC, DAPI (Joanidopoulos: it is now possible to display the cell nucleii selected in this way in a further projection of the feature space, specifying in this case the intensities in a first channel (dapi channel, blue) and in a channel (repair marker channel, red) [0262]), CYS, or Texas Red fluorophores (Joanidopoulos: The objects are segmented in a first step. This is carried out in the more powerful channel (TxRed) (see FIG. 29). [0302]), or the like.
Regarding claim 19:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above), wherein the displayed imaging menu is further configured to provide a time lapse selector, wherein the time lapse selector enables selection of time lapse values (Joanidopoulos: it is further proposed that, in relation to at least one chronological development of at least one first feature, at least one time period of interest, corresponding to a sub-series of the series of images, is selected semi-automatically or fully automatically or interactively [0078]) for imaging at least a portion of the microfluidic device (Joanidopoulos: It may expediently be provided that at least one time period of interest is determined or selected in relation to a plurality or all of the individual objects or sub-objects identified in the digital images [0079]) over a selected time period.
Regarding claim 20:
Du in view of Joanidopoulos teaches:
The computing device of claim 19 (as shown above), wherein the time lapse values is selected from a group (see Note 20B) consisting of time interval (Joanidopoulos: in relation to at least one chronological development of at least one first feature, at least one time period of interest, corresponding to a sub-series of the series of images, is selected [0078]; see Note 20A), time delay, total number of cycles, and combinations thereof.
Note 20A: The Examiner submits that determining a time period requires at least one time lapse value (a start or end time) and duration. The Examiner submits that one of ordinary skill in the art would recognize that a time interval may be described by a start or end time and duration. Therefore, because Joanidopoulos teaches selecting “at least one time period of interest”, it would be obvious to one of ordinary skill in the art to determine time lapse values based on a time interval.
Note 20B: Claim 20 is a Markush claim (see MPEP 2117). “Treatment of claims reciting alternatives is not governed by the particular format used (e.g., alternatives may be set forth as “a material selected from the group consisting of A, B, and C” or “wherein the material is A, B, or C”).” MPEP 2117(I). Accordingly, the parameters listed in claim 2 are treated as alternatives instead of a requirement that every parameter is taught in the prior art.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Goto (US 20150235085 A1).
Regarding claim 5:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above), wherein the display screen enables one or more members selected from: a user entered value for threshold value selection (Joanidopoulos: it very expediently being possible to define classifiers relating to different sub-spaces in graphical diagrams of the sub-space in question, for example by inputting region boundaries [0031]).
Du in view of Joanidopoulos teaches:
wherein the display screen enables one or more members selected from: a slidable selector for threshold value selection, a point selector for threshold value selection,
Goto teaches:
wherein the display screen enables one or more members selected from: a slidable selector for threshold value selection (Goto: Additionally, when manually setting a threshold, an operator […] operates the slider 23 for threshold setting to set a threshold accordingly [0095]), a point selector for threshold value selection (Goto: Additionally, when manually setting a threshold, an operator selects the threshold manual setting radio button 232 in the threshold decision field[0095])
Note 5A: The Examiner interpreted the radio button for threshold selection taught by Goto as a point selector, because the specification does not disclose any details about the point selector except for paragraph [0686] which states: “The computing device of any one of embodiments 272 to 278, wherein the display screen enables a point selector for threshold value selection.”
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Goto with Du in view of Joanidopoulos. Inputting a threshold via a point selector or slidable selector, as in Goto, would benefit the Du in view of Joanidopoulos teachings by enabling the user to quickly and precisely fine-tune parameters.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Almarode (US 20180165414 A1).
Regarding claim 9:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above),
Du in view of Joanidopoulos fails to explicitly teach:
wherein the display screen further enables display of the sub-population of detected micro-objects meeting the at least one threshold value for the first parameter and characterized by the second parameter.
Almarode teaches:
wherein the display screen further enables display of the sub-population of detected micro-objects (Almarode: a corresponding scatterplot 300 of gene expressions for the child cell population can then be presented (where the cell population includes one or more cells 308). [0042]) meeting the at least one threshold value for the first parameter and characterized by the second parameter (Almarode: with respect to a rectangle shape as shown by gate 600, the boundary conditions can be all cells with (1) X-axis values between 1 and 10, and (2) Y-axis values between 1 and 8. The cell list data structure (e.g., the set of vectors {Cell ID 1, Gene 1 Count, Gene 2 Count}) can be traversed to find all Cell IDs meeting these criteria, and the data for these cell IDs can populate a new child cell population data structure in the workspace. [0042]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Almarode with Du in view of Joanidopoulos. Displaying a selection of a sub-population of micro-objects, as in Almarode, would benefit the Du in view of Joanidopoulos teachings because highlighting the sub-population based on the first and second parameter “can allow a user to focus on a biologically-interesting cluster of cells 308 in the scatterplot 300.” (Almarode, [0042]).
Regarding claim 10:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above),
Du in view of Joanidopoulos fails to teach:
wherein the display screen further enables selection of a subset of the sub-population of detected micro-objects based on at least one selected threshold value for the selected second parameter.
Almarode teaches:
wherein the display screen further enables selection of a subset of the sub-population of detected micro-objects based on at least one selected threshold value for the selected second parameter (Almarode: with respect to a rectangle shape as shown by gate 600, the boundary conditions can be all cells with (1) X-axis values between 1 and 10, and (2) Y-axis values between 1 and 8. The cell list data structure (e.g., the set of vectors {Cell ID 1, Gene 1 Count, Gene 2 Count}) can be traversed to find all Cell IDs meeting these criteria [0042]; see Note 10A).
Note 10A: In [0042], Almarode teaches that the cell list may be traversed to select “find” cells within the first X range and second Y range. Therefore, the Examiner submits that Almarode teaches “selection of a subset of the sub-population of detected micro-objects based on at least one selected threshold value for the selected second parameter”.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Almarode with Du in view of Joanidopoulos. Selecting a subset of micro objects based on the second parameter, as in Almarode, would benefit the Du in view of Joanidopoulos teachings because highlighting the sub-population based on the first and second parameter “can allow a user to focus on a biologically-interesting cluster of cells 308 in the scatterplot 300.” (Almarode, [0042]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Aragaki (US 20180025211 A1).
Regarding claim 11:
Du in view of Joanidopoulos teaches:
The computing device of claim 1 (as shown above),
Du in view of Joanidopoulos fails to explicitly teach:
wherein the computing device is further configured to accept screen instructions for repositioning one of the set of detected micro-objects, sub-population of the set of detected micro-objects, a first subset of the sub-population, or a second subset of the first subset.
Aragaki teaches:
wherein the computing device is further configured to accept screen instructions for repositioning one of the set of detected micro-objects (Aragaki: The user interface function causes the PC to accept an input of a correction amount for correcting the position of the tracking target cell 230T with respect to one of the plural cropping area images 281 displayed on the display device 50. The position shift correction function causes the PC to correct a position shift of the tracking target cell 230T in accordance with the correction amount which was input from the user interface, [0037]; see Note 11A), sub-population of the set of detected micro-objects, a first subset of the sub-population, or a second subset of the first subset.
Note 11A: The limitations in claim 11 are listed in the disjunctive, so the Examiner opted to map the limitations found in the most immediate search of the prior art.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Aragaki with Du in view of Joanidopoulos. Repositioning one of the set of detected micro-objects based on screen instructions, as in Aragaki, would benefit the Du in view of Joanidopoulos teachings because “it is necessary to exactly specify the position of the cell by following a subtle movement of the cell. However, it is a very tiresome work for a human (a researcher, etc.) to visually confirm and specify the position of the cell from each cell image of the time-lapse cell image group” (Aragaki, [0005]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Schmidt (US 20120147010 A1).
Regarding claim 14:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above),
Du in view of Joanidopoulos fails to teach:
wherein the computing device is further configured to display on the screen an algorithm selector for selecting an algorithm, selected from a provided algorithm list, for analyzing images acquired through each selected imaging parameter, and detecting the set of micro-objects.
Schmidt teaches:
wherein the computing device is further configured to display on the screen an algorithm selector for selecting an algorithm, selected from a provided algorithm list (Schmidt: A list of selectable statistical methods is displayed in a second pane of the graphical user interface. [0007]), for analyzing images acquired through each selected imaging parameter, and detecting the set of micro-objects (Schmidt: The objects whose staining is determined are segmented based on applying a statistical method to the first numerical value. [0046]).
By ensuring that the user can quickly process and draw conclusions from data that may otherwise require complicated statistical methods to parse: “An improved graphical user interface is sought that displays the large amount of information generated by image analysis of bio-medical images in an easily accessible, intuitive and cross-referenced manner.” (Schmidt, [0004]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Larson (WO 2006041453 A1).
Regarding claim 15:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above),
Du in view of Joanidopoulos fails to teach:
wherein the computing device is further configured to display on the screen at least one of image of each individual detected micro-object, wherein the number of images displayed for each detected micro-object is equal to the number of imaging parameters selected.
Larson teaches:
wherein the computing device is further configured to display on the screen at least one of image of each individual detected micro-object (Larson: Fig. 2; see Note 15A), wherein the number of images displayed for each detected micro-object is equal to the number of imaging parameters selected (Larson: The columns of thumbnails correspond to the images from corresponding fields of the nucleus (DAPI), cytokeratin (CK-PE), control, leukocyte (CD45 APC) and false color overlay image of cytokeratin (green) and the nucleus (magenta), Pg. 5, par. 8, (emphasis added); see Note 15B).
Note 15A: Fig. 2 of Larson showcases that each micro-object is displayed in a gallery: “Figure 2 depicts a typical characterization of CTC by fluorescent microscopy of a blood sample from a patient with metastatic prostate cancer. Regions that potentially contain CTC are displayed in rows of thumbnails.” (Pg. 5, par. 8).
Note 15B: Because the placement of each image corresponds to a “cube type” such as DAPI (see also Note 16A), the Examiner submits that the number of images is equal to the number of imaging parameters selected.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Larson with Du in view of Joanidopoulos. Displaying images of each micro-object associated with an imaging parameter, as in Larson, would benefit the Du in view of Joanidopoulos teachings by sorting the detected micro-objects into categories easily perceptible by a user.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20160171686 A1) in view of Joanidopoulos (US 20100135566 A1) and Ozcan (US 20190333199 A1).
Regarding claim 18:
Du in view of Joanidopoulos teaches:
The computing device of claim 12 (as shown above),
Du in view of Joanidopoulos fails to teach:
wherein the imaging parameter comprises subparameters selected from the group consisting of illumination percentage, exposure time (ms), z-axis offset (microns), and combinations thereof.
Ozcan teaches:
wherein the imaging parameter comprises subparameters selected from the group (see Note 18B) consisting of illumination percentage, exposure time (ms) (Ozcan: the exposure time for each channel was set to: Texas Red 350 ms (10×) and 150 ms (20×), FITC 800 ms (10×) and 400 ms (20×), DAPI 60 ms (10×) and 50 ms (20×). [0156]; see Note 18A), z-axis offset (microns), and combinations thereof.
Note 18A: Ozcan teaches that imaging parameters such as DAPI, FITC, and Texas Red (for further discussion of the imaging parameter, see Note 16A) have associated exposure times, defined in milliseconds. The Examiner submits that this teaching indicates that one of ordinary skill in the art would find it obvious to include a sub-parameter such as exposure time (in milliseconds) for the imaging parameter.
Note 18B: Claim 18 is a Markush claim (see MPEP 2117). “Treatment of claims reciting alternatives is not governed by the particular format used (e.g., alternatives may be set forth as “a material selected from the group consisting of A, B, and C” or “wherein the material is A, B, or C”).” MPEP 2117(I). Accordingly, the parameters listed in claim 2 are treated as alternatives instead of a requirement that every parameter is taught in the prior art.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Ozcan with Du in view of Joanidopoulos. Determining a exposure time sub-parameter for each imaging parameter, as in Ozcan, would benefit the Du in view of Joanidopoulos teachings by ensuring that each imaging parameter corresponds to an optimal exposure time.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT ALEXANDER PROVIDENCE whose telephone number is (571)270-5765. The examiner can normally be reached Monday-Thursday 8:30-5:00.
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
