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 .
Status of Claims
This communication is in response to the remarks and amendments filed 03/17/2026. Claims 1, 3-14 and 16-17 are pending.
Response to Remarks
Applicant’s arguments with respect to independent claims 1, 16 and 17 have been carefully and respectfully considered in light of the instant amendment, but are not persuasive. Accordingly, this action has been made FINAL.
Specification
The objections to the disclosure are removed in accordance with the remarks and amendments filed.
Claim Objections
The objections to the claims are removed in accordance with the remarks and amendments filed.
Claim Rejections - 35 USC § 101
The 101 claim rejection to claim 16 is removed in accordance with the remarks and amendments filed.
Claim Rejections - 35 USC § 102 and/or 103
On page 10 of the remarks, Applicant argues Fischbacher’s “mere description of a colony count of greater than (1) does not anticipate ‘determining a spatial distribution’”.
The Examiner respectfully disagrees. It is noted that the claims require “a spatial distribution of cells”, in which, under the broadest reasonable interpretation (BRI), describe an arrangement, spacing, or spread of cells within the image(s) and without stating what type of arrangement, relative position, grouping, pattern, etc. is needed to describe the “spatial distribution” of cells. This is conveyed by Fischbacher, for instance, paragraph [0101] “Aside from counting individual starting cells, polyclonality can often be inferred if two or more clearly distinct cell masses are observed, which are assumed to have originated from two or more cells from the same FACS sort. If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal”. The indication of a colony count greater than 1 emphasizes a spatial distribution within the images to specify polyclonality or monoclonality. That is, Fischbacher specifically points out that when a spatial distribution (i.e., two or more distinct cell masses) of cells is observed, then a determination can be made if the formation is polyclonal or monoclonal.
Additionally, on page 10 of the remarks, Applicant argues “Floto’s description of a single probability would not lead one to the recited arrangement of claim 1, which includes both a cell count based probability value (based on the number of cells) and a cell distribution based probability value (based on a spatial distribution)”.
The Examiner respectfully disagrees. Specifically, Floto’s disclosure at paragraph [0031] states:
“Each of the candidate colonies is subjected to a secondary analysis in which Day Zero images are examined and a probability that the colony is derived from a single cell is estimated. In other words, the identification of colonies as monoclonal is made by correlating the position of the colony in a Pick Day image with the position of a single originator cell in a Day Zero image”.
In other words, Floto discloses a probability that the colony is derived from a single cell, which includes more than one probability in the form of cell count and cell distribution for monoclonality. That is, the identification of colonies is also analyzed based on its position (spatial distribution) as stated in [0031] above. As evidence of this, Floto at [0109] discloses:
“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing that no more than one cell was found in the first volumetric image stack within a selected radius of the candidate colony”
and [0110]:
“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing how many cell location probability cones intersect a hemispherical volume defined by the selected radius of the candidate colony”
Wherein at [0109] the likelihood (probability) that no more than one cell was found is that of a cell count and at [0110] the likelihood (probability) is that of a cell distribution in relation to cell locations.
As shown above, the combination of Fischbacher and Floto disclose the limitations of “wherein evaluating compliance with predetermined evaluation conditions comprises: evaluating at least one cell count based probability value that represents the probability that the cell culture is monoclonal based on the determined number of cells; and evaluating at least one cell distribution based probability value that represents the probability that the cell culture is monoclonal based on the determined spatial distribution of cells, wherein the monoclonal quality indicator is assessed based at least on a combination of the cell count based probability value and the cell distribution based probability value” as presented in independent claim(s) 1, 16 and 17.
Therefore, the argued limitations were written broad such that they read upon the cited references or are shown explicitly by the references. As a result, the claims stand rejected as follows.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-6, 13-14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Fischbacher et al. (US 20240054761 A1) in view of Floto et al. (US 20200131465 A1).
Regarding claim 1, Fischbacher discloses a computer-implemented method for automated monitoring of monoclonal quality of cell growth (“The method includes identifying and optionally analyzing a target object of an image using the system of the invention... In some aspects, the target object is a cell or cell colony and the physical attribute is a cell morphology feature, such as size and/or shape.” Fischbacher, [0014]; Figs. 1 and 2 automated process using CNNs to determine state of monoclonal cells), comprising: acquiring a sequence of images of a cell culture taken at different times during cell growth (“generate a plurality of chronological images of an image area via the imaging device” Fischbacher, [0049]; Fig. 2 illustrates daily imaging of cells/colonies to document the growth; Additionally see [0063] “In some aspects, the instructions provide for generating a set of images via the imaging system of cells being cultured over a duration of time, the set having a plurality of individual images. In some aspects, the individual images are taken in a chronological manner and assigned a chronological timestamp”); processing each image in the sequence of images to identify cell locations of cells in the cell culture (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”” Fischbacher, [0059]; Fig. 3 identifies cell locations in the image(s)); determining for at least some of the images in the sequence of images the number of cells from the identified cell locations (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”. Third, the task of enumerating individual target objects in a fully magnified, cropped image was termed “single-cell detection”” Fischbacher, [0059]; i.e., the global detection indicates images, or sequence of images, that contain cells/colonies and crops the images containing them with the number of cells/colonies, as shown in Fig. 3); determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations (“Aside from counting individual starting cells, polyclonality can often be inferred if two or more clearly distinct cell masses are observed, which are assumed to have originated from two or more cells from the same FACS sort. If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well.” Fischbacher, [0101]; i.e., the selected image(s) (at least one image) which contains the colonies/cells can be observed to determine if there is more than 1 count in the image thereby indicating a spatial distribution of cells; when a spatial distribution (i.e., two or more distinct cell masses) of cells is observed, then a determination can be made if the formation is polyclonal or monoclonal); evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth (“If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well. Alternatively, if the workflow continues to detect exactly one colony until reaching the day-zero scan, the resulting image will be magnified and cropped exactly around the ancestral cell or cells. This image can then be passed to the single-cell detection model, providing a count of the number of starting cells.” Fischbacher, [0101]; i.e., using the colony/cell count of greater than 1, an evaluation can be determined based on the spatial distribution of colony/cells in the image to be characteristic of monoclonal growth (e.g., a single-cell population)); and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions (“On this basis, the well may then finally be declared either monoclonal or polyclonal.” Fischbacher, [0101]; i.e., on the basis of colony count greater than 1, as mentioned above which indicates the spatial distribution, and the counting individual cells can determine the quality of monoclonal cells; see Fig. 3).
Fischbacher discloses all of the subject matter as described above except for specifically teaching wherein evaluating compliance with predetermined evaluation conditions comprises: evaluating at least one cell count based probability value that represents the probability that the cell culture is monoclonal based on the determined number of cells; and evaluating at least one cell distribution based probability value that represents the probability that the cell culture is monoclonal based on the determined spatial distribution of cells, wherein the monoclonal quality indicator is assessed based at least on a combination of the cell count based probability value and the cell distribution based probability value. However, Floto in the same field of endeavor teaches wherein evaluating compliance with predetermined evaluation conditions comprises (“Each of the candidate colonies is subjected to a secondary analysis in which Day Zero images are examined and a probability that the colony is derived from a single cell is estimated. In other words, the identification of colonies as monoclonal is made by correlating the position of the colony in a Pick Day image with the position of a single originator cell in a Day Zero image” Floto, [0031]): evaluating at least one cell count based probability value that represents the probability that the cell culture is monoclonal based on the determined number of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing that no more than one cell was found in the first volumetric image stack within a selected radius of the candidate colony” Floto, [0109]); and evaluating at least one cell distribution based probability value that represents the probability that the cell culture is monoclonal based on the determined spatial distribution of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing how many cell location probability cones intersect a hemispherical volume defined by the selected radius of the candidate colony” Floto, [0110]), wherein the monoclonal quality indicator is assessed based at least on a combination of the cell count based probability value and the cell distribution based probability value (See Floto, [0109]-[0110] above).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Fischbacher and Floto before the effective filing date of the claimed invention. The motivation for this combination of references would have been to obtain a likelihood that the candidate colonies are monoclonal based on image data (Floto, [0004]). This motivation for the combination of Fischbacher and Floto is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 3, Fischbacher and Floto disclose the method of claim 1, however, Floto further teaches wherein determining a spatial distribution of cells comprises determining for at least one image a distance between two cell locations identified for that at least one image (“To ascertain initial individual cell positions, volumetric imaging is used to capture Z stacks of images at suitable vertical intervals over the six wells. Capture performance, including the number of images and the distance between them, will depend on the depth of the medium, the exposure time, and any experiment-specific criteria” Floto, [0039]); and wherein evaluating compliance of the determined spatial distribution with predetermined evaluation conditions comprises comparing the determined distance with a threshold distance (“Each of the candidate colonies is subjected to a secondary analysis in which Day Zero images are examined and a probability that the colony is derived from a single cell is estimated. In other words, the identification of colonies as monoclonal is made by correlating the position of the colony in a Pick Day image with the position of a single originator cell in a Day Zero image” Floto, [0031]; i.e., the position, or location, is at a distance such that a threshold is used to determine if the candidate colony is monoclonal, see Floto [0100] “measure of likelihood (e.g., a probability and/or a distance between the cell colony and the location of one or more cells in the set of images captured at the first time) that the candidate colony is monoclonal”).
Therefore, combining Fischbacher and Floto would meet the claim limitations for the same reasons as previously discussed in claim 1.
Regarding claim 4, Fischbacher and Floto disclose the method of claim 1, however, Floto further teaches wherein determining (ST30) a spatial distribution of cells comprises determining for at least two images in the sequence of images a displacement between cell locations of a first one of the two images and cell locations of a second one of the two images (“correlation of colony positions with originator cell positions, making certainty of monoclonality high if a single cell is visible on Day Zero in proximity to a candidate colony, with no other cells in proximity.” Floto, [0070] wherein “identifying, based on one or more coplanar images of the well captured at a second time substantially later than the first time, a location of a candidate colony of cells” Floto, [0100]); and wherein evaluating compliance of the determined spatial distribution with predetermined evaluation conditions comprises comparing the determined displacement with a threshold displacement (“a measure of likelihood (e.g., a probability and/or a distance between the cell colony and the location of one or more cells in the set of images captured at the first time) that the candidate colony is monoclonal; and (e) in response to the measure of likelihood exceeding a selected threshold (e.g., a specific probability and/or a specified distance value, such as 50 μm)” Floto, [0100]).
Therefore, combining Fischbacher and Floto would meet the claim limitations for the same reasons as previously discussed in claim 1.
Regarding claim 5, Fischbacher and Floto disclose the method of claim 4, Fischbacher teaches further comprising automatically aligning at least two subsequent images in the sequence of images (“The algorithm then expands these coordinates until each dimension of the bounding box is twice that of the predicted colony, loads the next most recent image for the same well and crops the image to the resulting region. Due to the preservation of plate orientation and physical positioning between scans, the earlier instantiation of the same colony is therefore approximately centered within the newly cropped image. This image is then passed to the local detection model, which reports the bounding box of the earlier colony, indicating its position within the original, uncropped image when summed with the cropping coordinates.” Fischbacher, [0100]; i.e., the images are aligned with respect to the same coordinates in the sequence of images).
Regarding claim 6, Fischbacher and Floto disclose the method of claim 1, Fischbacher teaches further comprising determining for at least one image in the sequence of images a spatial coherence characteristic from the identified cell locations (“analyzing a target object includes classifying the target object based on an attribute of the target object. Such attributes may include a physical feature of the target object, such as size, shape and/or color... In some aspects, the target object is a cell or cell colony and the attribute is a physical attribute including a cell morphology feature, such as size and/or shape. In some aspects, the attribute is a characteristic of the cell or cell colony” Fischbacher, [0055]-[0056]); and evaluating compliance of the determined spatial coherence characteristic with predetermined coherence conditions being characteristic of monoclonal growth, wherein the monoclonal quality indicator is additionally assessed based on the evaluated compliance with the predetermined coherence conditions (“model was desired to categorize images cropped around colony regions into specific classes based on shape and/or size, such as morphological classes for cells” Fischbacher, [0059]; i.e., the classes of cells are classified based on indicators such as shape and size coherence conditions to evaluate if the “clonality of a cell or cell populations, for example [is] a monoclonal or plyclonal cell or cell population” Fischbacher, [0060]).
Regarding claim 13, Fischbacher and Floto disclose the method of claim 1, Fischbacher teaches wherein the images in the sequence of images comprise bright-field microscopy images (“machine learning being applied to the identification of monoclonal cell lines from brightfield microscopy” Fischbacher, [0047]).
Regarding claim 14, Fischbacher and Floto disclose the method of claim 1, Fischbacher teaches wherein processing each image in the sequence of images to identify cell locations of cells in the cell culture is performed by means of a trained deep neural network, preferably a convolutional neural network (“By using the classification network in conjunction with colony detection models, the inventors automate the segmentation step, enabling fully autonomous deployment in laboratory automation scenario” Fischbacher, [0111] and [0015] “using one or more CNNs to process images”).
Regarding claim 16, Fischbacher and Floto disclose one or more non-transitory computer-readable media comprising computer-executable instructions that (Fischbacher, [0066]), when executed by a computing system, cause the computing system to perform a method for automated monitoring of monoclonal quality of cell growth (“The method includes identifying and optionally analyzing a target object of an image using the system of the invention... In some aspects, the target object is a cell or cell colony and the physical attribute is a cell morphology feature, such as size and/or shape.” Fischbacher, [0014]; Figs. 1 and 2 automated process using CNNs to determine state of monoclonal cells), comprising: acquiring a sequence of images of a cell culture taken at different times during cell growth (“generate a plurality of chronological images of an image area via the imaging device” Fischbacher, [0049]; Fig. 2 illustrates daily imaging of cells/colonies to document the growth; Additionally see [0063] “In some aspects, the instructions provide for generating a set of images via the imaging system of cells being cultured over a duration of time, the set having a plurality of individual images. In some aspects, the individual images are taken in a chronological manner and assigned a chronological timestamp”); processing each image in the sequence of images to identify cell locations of cells in the cell culture (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”” Fischbacher, [0059]; Fig. 3 identifies cell locations in the image(s)); determining for at least some of the images in the sequence of images the number of cells from the identified cell locations (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”. Third, the task of enumerating individual target objects in a fully magnified, cropped image was termed “single-cell detection”” Fischbacher, [0059]; i.e., the global detection indicates images, or sequence of images, that contain cells/colonies and crops the images containing them with the number of cells/colonies, as shown in Fig. 3); determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations (“Aside from counting individual starting cells, polyclonality can often be inferred if two or more clearly distinct cell masses are observed, which are assumed to have originated from two or more cells from the same FACS sort. If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well.” Fischbacher, [0101]; i.e., the selected image(s) (at least one image) which contains the colonies/cells can be observed to determine if there is more than 1 count in the image thereby indicating a spatial distribution of cells; wherein a spatial distribution (i.e., two or more distinct cell masses) of cells is observed, then a determination can be made if the formation is polyclonal or monoclonal); evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth (“If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well. Alternatively, if the workflow continues to detect exactly one colony until reaching the day-zero scan, the resulting image will be magnified and cropped exactly around the ancestral cell or cells. This image can then be passed to the single-cell detection model, providing a count of the number of starting cells.” Fischbacher, [0101]; i.e., using the colony/cell count of greater than 1, an evaluation can be determined based on the spatial distribution of colony/cells in the image to be characteristic of monoclonal growth (e.g., a single-cell population)); and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions (“On this basis, the well may then finally be declared either monoclonal or polyclonal.” Fischbacher, [0101]; i.e., on the basis of colony count greater than 1, as mentioned above which indicates the spatial distribution, and the counting individual cells can determine the quality of monoclonal cells; see Fig. 3).
Fischbacher discloses all of the subject matter as described above. However, Floto further teaches wherein evaluating compliance with predetermined evaluation conditions comprises (“Each of the candidate colonies is subjected to a secondary analysis in which Day Zero images are examined and a probability that the colony is derived from a single cell is estimated. In other words, the identification of colonies as monoclonal is made by correlating the position of the colony in a Pick Day image with the position of a single originator cell in a Day Zero image” Floto, [0031]): evaluating at least one cell count based probability value that represents the probability that the cell culture is monoclonal based on the determined number of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing that no more than one cell was found in the first volumetric image stack within a selected radius of the candidate colony” Floto, [0109]); and evaluating at least one cell distribution based probability value that represents the probability that the cell culture is monoclonal based on the determined spatial distribution of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing how many cell location probability cones intersect a hemispherical volume defined by the selected radius of the candidate colony” Floto, [0110]), wherein the monoclonal quality indicator is assessed based at least on a combination of the cell count based probability value and the cell distribution based probability value (See Floto, [0109]-[0110] above).
Therefore, combining Fischbacher and Floto would meet the claim limitations for the same reasons as previously discussed in claim 1.
Regarding claim 17, Fischbacher and Floto disclose a computing system, comprising: memory; at least one processor in communication with the memory (Fischbacher, [0081]]; and a non-transitory computer-readable medium storing instructions that (Fischbacher, [0062]), when executed by the at least one processor (Fischbacher, [0080]), cause the computing system to perform operations comprising: acquiring a sequence of images of a cell culture taken at different times during cell growth (“generate a plurality of chronological images of an image area via the imaging device” Fischbacher, [0049]; Fig. 2 illustrates daily imaging of cells/colonies to document the growth; Additionally see [0063] “In some aspects, the instructions provide for generating a set of images via the imaging system of cells being cultured over a duration of time, the set having a plurality of individual images. In some aspects, the individual images are taken in a chronological manner and assigned a chronological timestamp”); processing each image in the sequence of images to identify cell locations of cells in the cell culture (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”” Fischbacher, [0059]; Fig. 3 identifies cell locations in the image(s)); determining for at least some of the images in the sequence of images the number of cells from the identified cell locations (“First, the term “global detection” is assigned to the task of detecting the presence or absence of a target object in an image area. Second, the task of detecting a target object in cropped image of various image areas at a variety of zoom magnifications is referred to as “local detection”. Third, the task of enumerating individual target objects in a fully magnified, cropped image was termed “single-cell detection”” Fischbacher, [0059]; i.e., the global detection indicates images, or sequence of images, that contain cells/colonies and crops the images containing them with the number of cells/colonies, as shown in Fig. 3); determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations (“Aside from counting individual starting cells, polyclonality can often be inferred if two or more clearly distinct cell masses are observed, which are assumed to have originated from two or more cells from the same FACS sort. If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well.” Fischbacher, [0101]; i.e., the selected image(s) (at least one image) which contains the colonies/cells can be observed to determine if there is more than 1 count in the image thereby indicating a spatial distribution of cells; wherein a spatial distribution (i.e., two or more distinct cell masses) of cells is observed, then a determination can be made if the formation is polyclonal or monoclonal); evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth (“If either the global or local detection models reports a colony count of >1 at any point during the process of iterating backwards chronologically, the algorithm accordingly declares the well to be polyclonal and ceases processing any further images for that well. Alternatively, if the workflow continues to detect exactly one colony until reaching the day-zero scan, the resulting image will be magnified and cropped exactly around the ancestral cell or cells. This image can then be passed to the single-cell detection model, providing a count of the number of starting cells.” Fischbacher, [0101]; i.e., using the colony/cell count of greater than 1, an evaluation can be determined based on the spatial distribution of colony/cells in the image to be characteristic of monoclonal growth (e.g., a single-cell population)); and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions (“On this basis, the well may then finally be declared either monoclonal or polyclonal.” Fischbacher, [0101]; i.e., on the basis of colony count greater than 1, as mentioned above which indicates the spatial distribution, and the counting individual cells can determine the quality of monoclonal cells; see Fig. 3).
Fischbacher discloses all of the subject matter as described above. However, Floto further teaches wherein evaluating compliance with predetermined evaluation conditions comprises (“Each of the candidate colonies is subjected to a secondary analysis in which Day Zero images are examined and a probability that the colony is derived from a single cell is estimated. In other words, the identification of colonies as monoclonal is made by correlating the position of the colony in a Pick Day image with the position of a single originator cell in a Day Zero image” Floto, [0031]): evaluating at least one cell count based probability value that represents the probability that the cell culture is monoclonal based on the determined number of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing that no more than one cell was found in the first volumetric image stack within a selected radius of the candidate colony” Floto, [0109]); and evaluating at least one cell distribution based probability value that represents the probability that the cell culture is monoclonal based on the determined spatial distribution of cells (“wherein determining the measure of likelihood that the candidate colony is monoclonal comprises observing how many cell location probability cones intersect a hemispherical volume defined by the selected radius of the candidate colony” Floto, [0110]), wherein the monoclonal quality indicator is assessed based at least on a combination of the cell count based probability value and the cell distribution based probability value (See Floto, [0109]-[0110] above).
Therefore, combining Fischbacher and Floto would meet the claim limitations for the same reasons as previously discussed in claim 1.
Claim(s) 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Fischbacher et al. in view of Floto et al. and in further view of Yuan et al. (US 20230086042 A1).
Regarding claim 7, Fischbacher and Floto disclose the method of claim 1, the combination of Fischbacher and Floto as a whole does not expressly teach wherein the predetermined evaluation conditions define a threshold value for a monoclonal number growth rate of the cells; and wherein evaluating compliance of the determined number of cells with said predetermined evaluation conditions comprises: determining a cell number growth rate as the ratio of a difference of the determined numbers of cells for two images in the sequence of images to a time interval between the times when said two images are taken; and comparing the determined cell number growth rate with the threshold value for the monoclonal number growth rate. However, Yuan in the same field of endeavor teaches wherein the predetermined evaluation conditions define a threshold value for a monoclonal number growth rate of the cells (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.)” Yuan, [0047]; wherein the growth is determined to “dictate whether the clone should be rejected or advanced to the next cell line development stage” in a monoclonal antibodies, see Yuan [0050]); and wherein evaluating compliance of the determined number of cells with said predetermined evaluation conditions comprises: determining a cell number growth rate as the ratio of a difference of the determined numbers of cells for two images in the sequence of images to a time interval between the times when said two images are taken (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.), and/or may compare pixel counts generated by the colony size algorithm 320 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases in pixel counts between stages 304 to thresholds, etc.)” Yuan, [0047]); and comparing the determined cell number growth rate with the threshold value for the monoclonal number growth rate (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.” Yuan, [0047]; wherein the growth is determined to “dictate whether the clone should be rejected or advanced to the next cell line development stage” in a monoclonal antibodies, see Yuan [0050]).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Fischbacher, Floto and Yuan before the effective filing date of the claimed invention. The motivation for this combination of references would have been to compare various pixel sizes/numbers and/or cell counts (or changes in those quantities over time) to respective thresholds to determine cell growth of monoclonal antibodies (Yuan, [0008]). This motivation for the combination of Fischbacher, Floto and Yuan is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 8, Fischbacher and Floto disclose the method of claim 1, however, Yuan further teaches wherein the predetermined evaluation conditions define a threshold value for an area growth rate of the cells (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.), and/or may compare pixel counts generated by the colony size algorithm 320 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases in pixel counts between stages 304 to thresholds, etc.)” Yuan, [0047]; wherein “the “pixel count” is a count of all pixels in the map that have been classified as being within a cell colony by the FCN (e.g., a “pixel area” of the cell colony). Alternatively, the “pixel count” may be a count of how many pixels span the largest dimension (e.g., width or length) of a cell colony depicted in the map, or another suitable type of pixel count that is indicative of colony size.” Yuan, [0059]); wherein determining the spatial distribution of cells for at least one image in the sequence of images comprises determining for at least two images in the sequence of images a cell growth area as an area covered by the cell culture within the respective image (“Also in the example process 300, the application 118 implements a cell growth assessment algorithm 330 that operates on the outputs of the algorithms 310, 320 to make an “overall” growth assessment for the clone under consideration... For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.), and/or may compare pixel counts generated by the colony size algorithm 320 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases in pixel counts between stages 304 to thresholds, etc.)” Yuan, [0047]); and wherein evaluating compliance of the determined spatial distribution of cells with said predetermined evaluation conditions comprises: determining a cell area growth rate based on a change of the cell growth area determined for two images in the sequence of images and the time interval between the times when said two images are taken (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.), and/or may compare pixel counts generated by the colony size algorithm 320 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases in pixel counts between stages 304 to thresholds, etc.)” Yuan, [0047]); and comparing the determined cell area growth rate with the threshold value for the monoclonal area growth rate (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.” Yuan, [0047]; wherein the growth is determined to “dictate whether the clone should be rejected or advanced to the next cell line development stage” in a monoclonal antibodies, see Yuan [0050]).
Therefore, combining Fischbacher, Floto and Yuan would meet the claim limitations for the same reasons as previously discussed in claim 7.
Regarding claim 9, Fischbacher, Floto and Yuan discloses the method of claim 8, however, Floto further teaches determining a minimum radius for a circle enclosing the identified cell locations in the cell culture as the area covered by cell culture within the respective image (“identifying, from the plurality of cell locations, single cells that are spaced from all other cells by at least a selected first radius” Floto, [0120]).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Fischbacher, Floto and Yuan before the effective filing date of the claimed invention. The motivation for this combination of references would have been to indicate a high likelihood of monoclonality based on radius of the cell colony (Floto, [0031]). This motivation for the combination of Fischbacher, Floto and Yuan is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim 10, Fischbacher, Floto and Yuan disclose the method of claim 8, wherein determining the cell growth area comprises: performing a cell segmentation process on each image to determine a cell area around each identified cell location of a cell as an area covered by said cell (“By using the classification network in conjunction with colony detection models, the inventors automate the segmentation step, enabling fully autonomous deployment in laboratory automation scenario” Fischbacher, [0111]); and determining the cell growth area as the area covered by the determined cell areas in the cell culture for each image (“The algorithm processes images of an image area, typically including a target object” Fischbacher, [0057]; wherein the target object is a colony and/or cell, see [0014]).
Regarding claim 11, Fischbacher and Floto disclose the method of claim 1, however, Yuan further teaches wherein the method comprises: determining a density distribution of identified cell locations of cells in at least one image in the sequence of images (“the algorithm 320 may process the well image 430 to generate a down-sampled segmentation map that indicates the approximate shape/area/boundaries of the colony 430 (e.g., excluding stray cells that are not in contact with other cells in the colony 430” Yuan, [0046]); evaluating the appearance of cluster points of cells from the determined density distribution and deciding on the monoclonal quality depending on the evaluation of the appearance of cluster points of cells (“the application 118 implements a cell growth assessment algorithm 330 that operates on the outputs of the algorithms 310, 320 to make an “overall” growth assessment for the clone under consideration. Algorithm 330 may take any suitable form. For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.), and/or may compare pixel counts generated by the colony size algorithm 320 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases in pixel counts between stages 304 to thresholds, etc.). The application 118 may then output an indication of growth (e.g., a score, or a classification such as “good,” “moderate,” “poor” or “none”) based on the comparison(s)” Yuan, [0047]).
Therefore, combining Fischbacher, Floto and Yuan would meet the claim limitations for the same reasons as previously discussed in claim 7.
Regarding claim 12, Fischbacher, Floto and Yuan discloses the method of claim 11, however, Floto further teaches wherein the predetermined evaluation conditions define a threshold cluster distance value; and wherein the cell culture is decided to be not monoclonal (“determining, based on the first volumetric image stack, a measure of likelihood (e.g., a probability and/or a distance between the cell colony and the location of one or more cells in the set of images captured at the first time) that the candidate colony is monoclonal” Floto, [0100]), if two or more cluster points are determined to appear in the density distribution at a distance from each other exceeding the predetermined threshold cluster distance value (“For a given well 206, for example, the algorithm 330 may compare the cell counts generated by the cell counting algorithm 310 to day/stage-specific thresholds (and/or compare absolute, percentage or ratio increases between stages 304 to thresholds, etc.)” Yuan, [0047]; see additionally Floto [0100]).
Therefore, combining Fischbacher, Yuan and Floto would meet the claim limitations for the same reasons as previously discussed in claim 9.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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