DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 11th, 2026, has been entered.
Claim Status
Claims 1-8, 11-12, 20-23, and 27-32 in the claim set filed February 26th, 2026, were pending for examination in the Application No. 17/984,931 filed November 10th, 2022. In the remarks and amendments received on May 11th, 2026, claim 1 is amended and claims 9-10, 13-19, 24-26, and 33-120 remain canceled. Accordingly, claims 1-8, 11-12, 20-23, and 27-32 are currently pending for examination in the application.
Response to Amendment
Applicant’s amendments filed May 11th, 2026, to the Claims have been entered.
Response to Arguments
Applicant’s arguments filed February 26th, 2026, regarding the rejections of the claims have been fully considered but are moot because the arguments do not apply to the new combination of the references being used in the current rejection below.
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.
Claims 1, 6-8, 11-12, 21, 27, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Fujii et al. (Fujii; US 2007/0273878 A1, cited in Applicant's IDS filed April 5th, 2023) in view of Song et al. (Song; CN 102663390 B).
Regarding claim 1, Fujii discloses a particle analyzer comprising:
a light detection system comprising an imaging photodetector, wherein the light detection system is configured to detect light from particles of a sample in a flow stream irradiated with a light source (para. [0081], recite(s)
[0081] “The particles are then imaged. …As described above, the image of the particle is imaged by the imaging section through the objective lens unit 60 in the imaging optical system 5 by irradiating the light from the irradiation unit 30 of the illumination optical system 4 on the flow of the particle suspension liquid narrowed to a flat shape in the flow cell 8 of the fluid mechanism unit 3.”
, where the “irradiation unit” is a light source); and
a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to (para. [0079] further recites
[0079] “The image data analyzing device 2 is configured by a personal computer (PC) including the image display unit 2a, the image data processing unit 2b serving as the device main body equipped with the CPU, ROM, RAM, hard disc and the like, and the input device 2c such as keyboard, as shown in FIGS. 1 and 2. An application program for performing analyzing process and statistical process of the image data based on the processing result in the particle image processing apparatus 1 by communicating with the particle image processing apparatus 1 is installed in the hard disc of the image data processing unit 2b. The application program is configured to be executed by the CPU of the image data processing unit 2b. In the present embodiment, automatic adjustment of stroboscopic light emitting intensity of the lamp 31 and automatic focal adjustment of the flow cell 8 to be hereinafter described can be carried out by the control of the image data analyzing device 2.”
):
generate an image of each particle based on the detected light (para. [0081]—see citation above—, where the images of particles obtained through the “illumination optical system” are images generated of each particle based on the detected light);
generate an image(para. [0091-0092], recite(s)
[0091] “In step S5, the image processing processor 94 performs a binarization process on the image of after the Laplacian process at the threshold level (binary threshold value) set in the binary threshold value setting process. That is, the collection of pixels having the luminance value smaller than the value calculated in equation (2) is extracted as the particle image for the imaged image by the bright field illumination. The collection of pixels having the luminance value larger than the value calculated in equation (3) is extracted as the particle image for the imaged image by the dark field illumination.”
[0092] “…The pixel values of P1 to P3 are 0 and the pixel values of P0 and P4 to P8 are 1 for the nine pixels of P0 to P9 of the binarization processed image data, as shown in FIG. 21. If the luminance value corresponding to each of the nine pixels P0 to P8 is greater than or equal to the binary threshold value, the pixel values of P0 to P8 are 1, whereas if the luminance value corresponding to each of the nine pixels P0 to P8 is smaller than the binary threshold value, the pixel values of P0 to P8 are 0. …”
, where the binarized image (i.e., the image generated by a “binarization process on the image”) to extract “particle image[s]” of each particle is generating an image based on the image (i.e., the images obtained through the illumination optical system, e.g. the “imaged image by the bright field illumination” and/or the “imaged image by the dark field illumination”) of each particle by comparing pixels of an image (i.e., “pixel values”) against a determined threshold (i.e., “binary threshold value”) and converting each pixel to a binary pixel value (e.g., either “1” or “0” as detailed in the example outlined in para. [0092] above));
modulate a visualization parameter for the image(para. [0091]—see previous citation immediately above—, where paras. [0089-0090] further recite(s):
[0089] “In step S4, the image processing processor 94 sets a binary threshold level (binary threshold value) based on the data after the edge enhancement process is performed. That is, a luminance histogram section for executing the binary threshold value setting process is provided in the Laplacian circuit of the image processing processor 94. First, the image processing processor 94 creates a luminance histogram (see FIGS. 18 and 19) from the image data of after the Laplacian process. FIG. 18 shows the luminance histogram of the imaged image by the bright field illumination, and FIG. 19 shows the luminance histogram of the imaged image by the dark field illumination. The image processing processor 94 performs a predetermined smoothing process on the luminance histogram. After obtaining the most frequent luminance value from the luminance histogram of after the smoothing process, the binary threshold value is calculated with the following equation (2) or (3) using the most frequent luminance value.”
[0090] “Equation (2) is applied to the imaged image by the bright field illumination, and equation (3) is applied to the imaged image by the dark field illumination. In equations (2) and (3), [alpha], [beta] and [gamma] are parameters that can be set by the user, and the user is able to change the values of [alpha], [beta] and [gamma] according to the measuring object. The default values (default) of [alpha] and [beta] are "90" and "0", respectively. The value of [gamma] is set between 10 and 70.”
, where the setting of “binary threshold [luminance] value[s]” based on user set parameters (e.g., alpha, beta, and gamma) for an image generated by the binarization process is modulating a visualization parameter (e.g., modulating luminance thresholds) to adjust visualization of the generated binarized image (e.g., adjusting the “luminance” in the generated image)); and
automatically adjust a data acquisition parameter of the particle analyzer based on the (paras. [0120] and [0141-0142], recite(s)
[0120] “FIG. 30 is a flow chart describing the automatic adjustment operation of the stroboscopic light emitting intensity in the dark field illumination, and FIG. 31 is a view showing a specific example of the DA value. The automatic adjustment operation of the stroboscopic light emitting intensity in the dark field illumination will now be described with reference to FIGS. 30 and 31.”
[0141] “In the present embodiment, the particle image is extracted based on the threshold value smaller than the most frequent luminance value and the extracted particle image is analyzed to obtain the morphological feature information indicating the morphological feature of the particle when processing the imaged image by the bright field illumination, whereas the particle image is extracted based on the threshold value greater than the most frequent luminance value and the extracted particle image is analyzed to obtain the morphological feature information indicating the morphological feature of the particle when processing the imaged image by the dark field illumination, whereby the particle image can be extracted from the imaged image by either one of the illuminations of the bright field illumination or the dark field illumination, and the morphological feature information of the particle can be obtained using one particle image analyzing apparatus, as described above.”
[0142] “In the present embodiment, the image data analyzing device 2 is configured to adjust the stroboscopic light emitting intensity of the lamp 31 based on the luminance value of the particle image, thereby setting the stroboscopic light emitting intensity of the lamp 31 so as to be the luminance value of the particle image of when the particle image clearly appears, as described above.”
, where “automatic focusing adjustment” is performed on extracted “particle image(s)” (i.e., binary images) based on threshold luminance values is automatically adjusting a data acquisition parameter (e.g., illumination or “light emitting intensity”) in response to the visualization parameter (e.g., luminance threshold) of the binarized images (i.e., “particle image(s)”)).
Where Fujii does not specifically disclose automatically adjust a data acquisition parameter of the particle analyzer in response to the modulated visualization parameter of the image and the adjustment to the visualization of generated image, Fujii teaches the modulated visualization parameter …to adjust visualization of the generated image… as user modulated “threshold [luminance] value[s]” to adjust the “luminance” of the generated image as detailed in paras. [0089-0091] above (see the rejection of claim limitation “modulate a visualization parameter…” above).
Since Fujii discloses that the extracted particle images are used in automatically adjusting the data acquisition parameter (e.g., stroboscopic light emitting intensity) of the particle analyzer based on the visualization parameter (e.g., “threshold [luminance] value[s]”) as detailed in para. [0141] above, a person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the visualization parameter and the adjustment to the generated image used to automatically adjust the data acquisition parameter is the modulated visualization parameter of user-set binary thresholds and the “luminance” adjusted in the generated image caused by the modulation of the visualization parameter as taught by Fujii since Fujii teaches that the extracted particle images are obtained using the modulated visualization parameter which adjusts the luminance of the extracted particle images (paras. [0089-0091]—see citations in claim limitation “modulate…” above).
Where Fujii does not specifically disclose
generate an image mask based on the image of each particle by comparing pixels of a greyscale image of the particle against a determined threshold value and converting each pixel to a binary pixel value;
Song teaches in the same field of endeavor of image binarization for particle analysis of a particle analyzer
generate an image mask based on the image of each particle by comparing pixels of a greyscale image of the particle against a determined threshold value and converting each pixel to a binary pixel value (Fig. 5 and description, para(s). [0002], [0004], [0020], and [0038], depict(s)/recite(s):
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[0002] “This invention belongs to the field of image processing technology for microscope systems, and in particular refers to a method for binarizing flow cytometry cell microscopy images.”
[0004] “Image binarization is a basic operation and important step in image processing. Its function is to set the gray value of pixels in an image to 0 or 255, that is, to make the entire image present a visual effect that is only black and white.
Binarization methods are mainly divided into global binarization and local binarization.
The basic idea of global binarization is to find or set a threshold T, set pixels in the source image with values greater than T to white (or black), and set pixels with values less than T to black (or white). Its advantages are fast processing speed and insensitivity to noise. Its disadvantage is that the binarization effect is not ideal when the image grayscale distribution is uneven. The representative method is the Otsu method.
Local binarization is a method that divides the source image into N parts, calculates the threshold for each part, and then binarizes it. Its advantage is that it is good at processing images with uneven gray levels, but its disadvantages are that it requires a lot of computation and is sensitive to noise. A representative method is the Niblack method.”
[0020] “Figure 1 is a grayscale image of the cells to be processed;”
[0038] “Figure 5 shows the binarization result of the source image using the method of the present invention, where the white part is the cell region and the black part is the background region; …”
, where an image undergone “Image binarization” is at least generating an ‘image mask’ as depicted in Fig. 5 above, wherein the ‘image mask’ is obtained by comparing pixels of a greyscale image (e.g., “gray value of pixels in an image” and/or a “grayscale image”) of a particle against a determined threshold value (e.g., “set a threshold T”) and converting each pixel to a binary pixel value (e.g., “white (or black)”)).
Since Fujii also discloses generating a binary image to extract particle images by a binarization process comprising of comparing pixels of an image of a particle against a determined binary threshold value and converting each pixel to a binary pixel value (see para. [0091-0092] of Fujii in the claim limitation “generate an image mask…” above), a person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the image generated by a binarization process in the system of Fujii above would result in an image mask of each particle generated by comparing pixels of a greyscale image of the particle against a determined threshold value to highlight each particle in each particle image as taught by Song above.
Therefore, it would also have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention that the claimed “image” and “generated image” of Fujii (i.e., a binary image) recited in the claimed processes of “modulat[ing] the visualization parameter for the image” and “automatically adjust[ing] a data acquisition parameter of the particle analyzer based on the modulated visualization parameter of the imagethe visualization of the generated image” are an “image mask” and “generated image mask”, respectively, as each of the “image mask” and “generated image mask” are also images obtained by performing the same image binarization process as disclosed in Fujii above (see citations of Fujii in claim limitations “modulate…” and “automatically adjust…” above).
Regarding claim 6, Fujii in view of Song further discloses the particle analyzer according to claim 1, wherein Fujii further discloses the memory comprises instructions to modulate the visualization parameter for a region of analysis of the image mask of each particle (para. [0091]—see citation in claim 1 limitation “generate an image mask…” above—, where using the “binary threshold value setting process” to extract a “collection pixels” as a “particle image” is modulating the visualization parameter (e.g., modulating luminance thresholds) for a region of analysis of the image mask (e.g., the binarized image)).
Regarding claim 7, Fujii in view of Song further discloses the particle analyzer according to claim 6, wherein Fujii further discloses the memory comprises instructions to modulate the visualization parameter to exceed a threshold visualization of each particle in the region of analysis (para. [0091]—see citation in claim 1 limitation “generate an image mask…” above—, where setting the “binary threshold value” to “the value calculated in equation (3)” upon which extraction of the particle image is based on when the “luminance value [is] larger than the value calculated in equation (3)” is modulating the visualization parameter to exceed a threshold visualization of the particle in the region of analysis).
Regarding claim 8, Fujii in view of Song further discloses the particle analyzer according to claim 7, wherein Fujii further discloses the memory comprises instructions to modulate the visualization parameter to visualize a border of the particle in the image mask (para. [0093], recite(s)
[0093] “If the region configured by the pixel of interest and the eight pixels in the vicinity thereof is one part of the boundary of the particle image, that is, if the prime code is 00000000 in binary notation, the multiple point information is obtained. The multiple point is a code indicating the possibility of number of times the relevant pixel is passed in edge trace, to be hereinafter described, where the multiple point information corresponding to all the patterns are stored in the look up table (not shown) in advance. The number of multiple points is obtained by referencing the look up table. With reference to FIG. 22, if the pixel values of P2 and the four pixels of P5 to P8 are 1, and the pixel values of four pixels of P0, P1, P3 and P4 are 0, there is a possibility the pixel of interest P8 is passed twice in edge trace, as shown with arrows C and D in FIG. 22. Therefore, the pixel of interest P8 has two points, and the number of multiple points is 2. The number of multiple points is stored in the multiple point number storing region 98b.”
, where performing “edge trac[ing]” or determining the “boundary of the particle image” after binarization based on the modulated binary threshold luminance setting process is modulating the visualization parameter sufficient to visualize a border of the particle in the image mask (e.g., the binarized image)).
Regarding claim 11, Fujii in view of Song further discloses the particle analyzer according to claim 1, wherein Fujii further discloses the visualization parameter is a pixel intensity threshold (para. [0091]—see citation in claim 1 limitation “modulate…” above—, where threshold pixel “luminance value” is a pixel intensity threshold).
Regarding claim 12, Fujii in view of Song further discloses the particle analyzer according to claim 11, wherein Fujii further discloses the memory comprises instructions to modulate the pixel intensity threshold for one or more locations in a region of analysis of the image mask of each particle (para. [0091]—see citation in claim 1 limitation “generate an image mask…” above—, where modulating the pixel luminance intensity threshold for the “imaged image” to extract a “collection of pixels” as a “particle image” is modulating the pixel intensity threshold for one or more location in the region of analysis (i.e., the “imaged image”)).
Regarding claim 21, Fujii in view of Song further discloses the particle analyzer according to claim 11, wherein Fujii further discloses the pixel intensity threshold comprises an image mask threshold (para. [0091]—see citation in claim 1 limitation “generate an image mask…” above—, where a “binary threshold” is an image mask threshold).
Regarding claim 27, Fujii in view of Song further discloses the particle analyzer according to claim 1, wherein Fujii further discloses the memory comprises instructions to automatically adjust a light intensity detection threshold for one or more of detector channels of the particle analyzer in response to a change in the visualization parameter for the image mask of each particle (paras. [0089-0091]—see citations in claim 1 limitation “generate an image mask…” above—, where adjusting binary threshold luminance values for an “imaged image” by either bright field illumination or by dark field illumination in response to changing the binary threshold luminance values in the “binary threshold value setting process” is adjusting a light intensity detection threshold for one or more detector channels (i.e., detector channels for bright field and/or dark field illumination) of the particle analyzer in response to a change in the visualization parameter (e.g., binary threshold luminance values) for the particle image mask (e.g., the binarized image)).
Regarding claim 32, Fujii in view of Song further discloses the particle analyzer according to claim 1, wherein Fujii further discloses the particle analyzer according to claim 1 further comprising a light source for irradiating particles of the sample in the flow stream (para. [0081]—see similar limitation in claim 1 above).
Claims 2-5 and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Fujii in view of Song as applied to claim 1 above, and further in view of Asada et al. (Asada; US 2023/0314300 A1).
Regarding claim 2, Fujii in view of Song discloses the particle analyzer according to claim 1, wherein Asada further teaches in the same field of endeavor of particle analysis the memory comprises instructions to generate the image of each particle from data signals from a side-scattered light detector channel of the light detection system (paras. [0089], [0092], [0243], and [0300], recite(s)
[0089] “FIG. 1 shows an example of using, as the signal obtained from each cell, a forward scattered light signal, a side scattered light signal, and a side fluorescence signal, which are optical signals obtained by applying light to the cell flowing in a flow cell. However, the signal is not limited in particular as long as the signal indicates a feature of each cell and allows classification of cells for each type.”
[0092] “The signal based on light scattering can include a scattered light signal caused by light application and a light loss signal caused by light application. The scattered light signal serves as a parameter that indicates a feature of a cell and that is different in accordance with the light reception angle of scattered light with respect to the advancing direction of application light. The forward scattered light signal is used as a parameter that indicates the size of the cell. The side scattered light signal is used as a parameter that indicates complexity of the nucleus of the cell.”
[0243] “As shown in FIG. 38, the measurement unit 400a or the measurement unit 500a includes the flow cell 4113, 551. In the measurement unit 400a or the measurement unit 500a, a biological sample is sent to the flow cell 4113, 551. The biological sample supplied to the flow cell 4113, 551 is irradiated with light from the light source 4112, 553, and forward scattered light, side scattered light, and side fluorescence emitted from each cell in the biological sample are detected by, the light detectors (4116, 4121, 4122, 555, 558, 559). The measurement unit 400a or the measurement unit 500a generates waveform data from a forward scattered light signal, a side scattered light signal, and a side fluorescence signal obtained through detection of light by the light detectors (4116, 4121, 4122, 555, 558, 559), and transmits the waveform data to the vendor-side apparatus 100.”
[0300] “The imaging part 760 is implemented by a TDI (Time Delay Integration) camera. The imaging part 760 can capture images of the fluorescences and the transmitted light and output, to the processing unit 800, a fluorescence image corresponding to the fluorescences and a bright field image corresponding to the transmitted light, as imaging signals.”
, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least a “side scattered light signal” detector channel).
Since Fujii also discloses generating bright field images (see paras. [0089-0091] in claim 1 limitations “generate an image mask…” and “modulate…” above), it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle from data signals including data signals from at least a side-scattered light detector to improve cell classification in flow cytometry as taught by Asada (para. [0099], recites
[0099] “The signal obtained from a cell may be a combination of at least two kinds of signals out of the above-described signals obtained from a cell. Through combination of a plurality of signals, the features of a cell can be pleiotropically analyzed, and thus, cell classification with a higher accuracy is enabled. As for the combination, for example, at least two out of a plurality of optical signals, e.g., a forward scattered light signal, a side scattered light signal, and a fluorescence signal, may be combined. Alternatively, scattered light signals having different angles, e.g., a low angle scattered light signal and a high angle scattered light signal, may be combined. Still alternatively, an optical signal and an electrical signal may be combined. The kind and number of signals to be combined are not limited in particular.”
).
Regarding claim 3, Fujii in view of Song further discloses the particle analyzer according to claim 1, wherein Asada further teaches in the same field of endeavor of particle analysis the memory comprises instructions to generate the image of each particle from data signals from one or more fluorescence detector channels of the light detection system (paras. [0089], [0092], [0243], and [0300]—see citations in claim 2 above—, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least one “fluorescence signal” detector channel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle from data signals including data signals from at least one or more fluorescence detector channels to improve cell classification in flow cytometry as taught by Asada (para. [0099]—see citation in claim 2 above).
Regarding claim 4, Fujii in view of Song discloses the particle analyzer according to claim 1, wherein Asada further teaches in the same field of endeavor of particle analysis the memory comprises instructions to generate the image of each particle from data signals from a forward-scattered light detector channel of the light detection system (paras. [0089], [0092], [0243], and [0300]—see citations in claim 2 above—, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least a “forward scattered light signal” detector channel).
Since Fujii also discloses generating bright field images (see paras. [0089-0091] in claim 1 limitations “generate an image mask…” and “modulate…” above), it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle from data signals including data signals from at least a forward-scattered light detector channel to improve cell classification in flow cytometry as taught by Asada (para. [0099]—see citation in claim 2 above).
Regarding claim 5, Fujii in view of Song discloses the particle analyzer according to claim 1, wherein Asada further teaches in the same field of endeavor of particle analysis the memory comprises instructions to generate the image of each particle from data signals from a light loss detector channel of the light detection system (paras. [0089], [0092], [0243], and [0300]—see citations in claim 2 above—, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least a “light loss signal” detector channel).
Since Fujii discloses generating dark field images (see paras. [0089-0091] in claim 1 limitations “generate an image mask…” and “modulate…” above), it would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle from data signals including data signals from at least a light loss detector channel to improve cell classification in flow cytometry as taught by Asada (para. [0099]—see citation in claim 2 above).
Regarding claim 28, Fujii in view of Song discloses the particle analyzer according to claim 27, wherein Fujii further discloses the memory comprises instructions to generate an image of each particle when light detected by a (paras. [0089-0091]—see citations in claim 1 limitation “modulate…” above—, where extracting the “particle image” for an “imaged image by the dark field illumination” when the “luminance value larger than the value calculated in equation (3)” is generating an image of the particle when light detected by the light detection channel (e.g., “dark field illumination”) exceeds the adjusted light intensity detection threshold (e.g., “the value calculated in equation (3)”)).
Where Fujii does not specifically disclose the light detection channel as a side scattered light detection channel, Asada further teaches in the same field of endeavor of particle analysis that an image of the particle can be generated based on light detected by a side scattered light detection channel (paras. [0089], [0092], [0243], and [0300]—see citations in claim 2 above—, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least a “side scattered light signal” detector channel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle when light is detected by a side-scattered light detection channel exceeds the adjusted light intensity detection threshold to improve cell classification in flow cytometry by incorporating features better captured by side scattered light signals in the extracted particle image as taught by Asada (para. [0099]—see citation in claim 2 above).
Regarding claim 29, Fujii in view of Song discloses the particle analyzer according to claim 27, wherein Fujii further discloses the memory comprises instructions to not generate an image of each particle when light detected by a (paras. [0089-0091]—see citations in claim 1 limitations “generate an image mask…” and “modulate…” above—, where extracting the “particle image” for an “imaged image by the dark field illumination” when the “luminance value larger than the value calculated in equation (3)” is not generating an image of the particle when light detected by the light detection channel (e.g., “dark field illumination”) does not exceed the adjusted light intensity detection threshold (e.g., “the value calculated in equation (3)”)).
Where Fujii does not specifically disclose the light detection channel as a side scattered light detection channel, Asada further teaches in the same field of endeavor of particle analysis that an image of the particle can be generated based on light detected by a side scattered light detection channel (paras. [0089], [0092], [0243], and [0300]—see citations in claim 2 above—, where the “light detectors (4116, 4121, 4122, 555, 558, 559)” include at least a “side scattered light signal” detector channel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate generating the image of the particle when light is detected by a side-scattered light detection channel exceeds the adjusted light intensity detection threshold to improve cell classification in flow cytometry by incorporating features better captured by side scattered light signals in the extracted particle image as taught by Asada (para. [0099]—see citation in claim 2 above).
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Fujii in view of Song as applied to claim 11 above, and further in view of Wolf (US 2019/0333264 A1).
Regarding claim 22, Fujii in view of Song discloses the particle analyzer according to claim 11, wherein Wolf further teaches in the same field of endeavor of particle analysis the particle analyzer further comprises a display comprising a graphical user interface (GUI) for modulating the visualization parameter of the image mask of each particle (paras. [0037], [0043], and [0053], recite(s)
[0037] “…More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia presentations, for example. In some embodiments, the display device presents the cell images (for example, rendered by the CPU 205 ) and provides a user interface for making adjustments to the displayed cell images, as described in more detail with respect to FIG. 3…”
[0043] “The control panel 301 allows the user to specify modifications to how the cell images are displayed. Such modifications may be computationally intensive and may generally be performed using a robust workstation. However, even on such a high powered computing device, the modifications and/or adjustments to each cell image may take minutes to complete per image in the wall of images since each cell image is subjected to intensive image processing. These adjustments include, but are not limited to:…
Thresholding—used to mask out noise at the lower intensities…”
[0053] “In the popup UI 300 , a subset of the adjustments listed herein (such as in the preceding paragraph and/or bullet list) are made available via the control panel 301 . As shown, available adjustments include channel enable/disable channel toggle 305 a for a Brightfield channel, a Brightfield channel filter slider adjustment bar 306 a , a Brightfield channel color adjustment selector 307 a , and a Brightfield channel adjustment selection panel 308 a . The enable/disable channel toggle 305 a for a Brightfield channel may enable or disable the display of the Brightfield channel. The Brightfield channel filter slider adjustment bar 306 a may allow for an adjustment of the channel filter applied to the Brightfield channel, thereby allowing selection or application of a number of smoothing filters to the Brightfield channel image. The Brightfield channel color adjustment selector 307 a may toggle display of the Brightfield channel color adjustment panel 308 a and may display the selected color from the Brightfield channel color adjustment panel 308 a . The Brightfield channel color adjustment panel 308 a shows the available colors that can be selected for the Brightfield channel. Similarly, the available adjustments include channel enable/disable channel toggle 305 b for a FITC channel, an FITC channel filter slider adjustment bar 306 b , an FITC channel color adjustment selector 307 b , and an FITC channel histogram 309 a . The enable/disable channel toggle 305 b for the FITC channel may enable or disable the display of the FITC channel. The FITC channel filter slider adjustment bar 306 b may allow for an adjustment of the channel filter applied to the FITC channel, thereby allowing selection or application of a number of smoothing filters to the FITC channel image. The FITC channel color adjustment selector 307 b may toggle display of the FITC channel color adjustment panel (not shown in this figure) or the FITC channel histogram 309 a and may display the selected color from the FITC channel color adjustment panel. The FITC channel histogram 309 a may show a representation of FITC channel image samples collected and may enable the user to see the distribution on intensities of the FITC channel image samples and to adjust the image settings accordingly, for example using one or more threshold bars 310 a or a gamma selection 311 a.”
, where the user interface comprising of “threshold bars” including “slider adjustment bar[s]” for adjusting visualization parameters for at least a “Brightfield channel” is a display comprising a graphical user interface (GUI) for modulating the visualization parameter (e.g., “Thresholding—used to mask out noise at the lower intensities”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate a display comprising a GUI for modulating the visualization parameter to more easily allow a user modulate the visualization parameter of pixel intensity thresholding as taught by Wolf (para. [0055], recites
[0055] “In some embodiments, the control panel 301 may be presented as the popup UI 300 . In some embodiments, the control panel 301 may be small and efficiently present the adjustment options available to the user for modifying the image settings. In some embodiments, the control panel 301 is interactive and provides visual cues regarding the behaviors or effects of the available adjustments. In some implementations, the control panel 301 may be accessed in a network or Internet browser, a custom application for viewing, or some other light weight client application. Accordingly, the control panel 301 may operate on a wide range of computing devices, even those with constrained resources (for example, low power processing systems) rather than requiring the robust workstation identified herein for viewing, analyzing, and modifying the cell images.”
).
Regarding claim 23, Fujii, as modified by Song and Wolf, discloses the particle analyzer according to claim 22, wherein Wolf further teaches the GUI comprises a slide bar for adjusting the pixel intensity threshold (paras. [0037], [0043], and [0053]—see citations in claim 22 above—, where the “filter slider adjustment bars” include adjustments to particle images including “Thresholding” is the GUI comprising at least a slide bar for adjusting the pixel intensity threshold).
Claims 30-31 are rejected under 35 U.S.C. 103 as being unpatentable over Fujii in view of Song as applied to claim 1 above, and further in view of Sharpe et al. (Sharpe; US 2018/0356846 A1).
Regarding claim 30, Fujii in view of Song discloses the particle analyzer according to claim 1, wherein Sharpe further teaches in the same field of endeavor of particle analysis the particle analyzer according to claim 1 further comprises a particle sorter (paras. [0207] and [0222-0223], recite(s)
[0207] “In exemplary embodiments, an optical detector assembly 226 ( FIG. 7) for use with microfluidic assembly 218 is provided. At least a portion of optical detector assembly 226 may be implemented in particle inspection region assembly 222 to interrogate the particles in this region. At least a portion of optical detector assembly 226 may monitor flow through a plurality of channels 203 simultaneously. In exemplary embodiments, assembly 226 can inspect individual particles for one or more particular characteristics, such as size, form, fluorescence, optical scattering, as well as other characteristics. It is noted that assembly 226 is not limited for use in particle or cell sorting systems and may be implemented in any suitable system having a substance, such as particles, to be monitored flowing through one or more channels.”
[0222] “In general, processor 214 is configured to change (e.g., automatically change) one or more parameters, features, characteristics and/or components of system 200 based on the one or more operational characteristics sensed by the one or more sensor members 216 . As such, processor 214 is generally programmed, configured, and/or adapted to enable or facilitate system 200 to process particles in an operatorless fashion.”
[0223] “In certain embodiments, other characteristics/aspects of the components of system 200 that may be monitored or sensed (e.g., via sensor assemblies 216 ) and/or operated in an operatorless fashion or manner (e.g., via processor 214 and/or sensors 216 ) may include, without limitation, the following:
… (vii) adjusting optical measurement apparatus (e.g., through positioning various mechanical or optical components, or by effecting the direction or position of one or more optical paths or particle paths to enable reliable and consistent measurement and/or sorting of particles flowing within system 200 (e.g., within the cytometer apparatus); monitor and control functions (e.g., system leaks (gas or liquid); out of bounds (power, safe shut-down, instrument safety and control network, universal power supply, etc.); trending (e.g., sample quality, sort rate, sort fraction, assessment of live to dead ratio within a sample, scheduling of samples, alarm conditions and alarms); intelligent error handling such as self-fixing, self-regulation or other act such as by reacting to system 200 parameters (e.g., parameter change such as temperature, pressure, vacuum, alignment movement, etc.) that may affect system/instrument operation);…”
, where “particle or cell sorting systems” include particle sorters).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii in view of Song to incorporate a particle sorter to automatically adjust one or more parameters, features, characteristics and/or components of the particle sorter to ensure reliable and consistent sorting of particles as taught by Sharpe (paras. [0222-0223]—see citations above).
Regarding claim 31, Fujii, as modified by Song and Sharpe, discloses the particle analyzer according to claim 30, wherein Sharpe further teaches in the same field of endeavor of particle analysis the memory comprises instructions to automatically generate a sorting gate for particles of the sample in response to an adjusted visualization parameter (paras. [0156] and [0056]—see citations in claim 30 above—, where recite(s)
[0144] “According to certain aspects, and referring to FIG. 4B, gating regions may be automatically defined around the sub-populations and/or sub-groups of cells. Predetermined gating selection criteria may be used to draw a gating region around the combined sub-population. For example, a gating region may be defined to include 100% of the combined sub-population. Optionally, a gating region may be defined to include that portion of the combined sub-population residing within two standard deviations of a mean of a measured, sensed, or determined characteristic of the sub-population. As a non-limiting example, a gating region may be drawn around that portion of a combined sub-population that falls with a range of fluorescence signal intensities centered on the mean fluorescence signal intensity and/or that falls with 2.5 standard deviations of the scatter signal. Gating regions may be automatically defined around sub-populations or sub-groups of cells and may be adapted in real-time and/or may be updated periodically at regular intervals and/or when a gating update criteria is triggered.”
[0156] “In exemplary embodiments, system 100 (e.g., via sensors 116 and processor 114 ) may be configured and adapted to track a cell population or populations for operatorless operation, and/or for sorting particles to account for varying operating conditions of system 100 (e.g., instruments and/or instrument component variations, varying environment around system 100 , and/or variations between samples, as non-limiting examples). In general, particle populations (e.g., a grouping of cells that are considered similar), and/or cluster positions based on certain data representations, may be monitored, and the data and/or sort gate and/or region conditions or boundaries may then be adjusted (or “tracked”) to account for minor fluctuations in measured signal levels so that sorting (particle processing) may continue with minimal impact on sort purity and recovery.”
, where “gating regions” or “sort gate[s]” are sorting gates and automatically adjusting the sorting gates based on “gating update criteria” of “signal intensities” and/or “scatter signal[s]” is automatically generate a sorting gate for particles of the sample in response to the adjusted visualization parameter (e.g., “gating update criteria” including “signal intensities” and/or “scatter signal[s]”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the presently filed invention to modify the system of Fujii, as modified by Song and Sharpe, to further incorporate automatically generate a sorting gate for particles of the sample in response to the adjusted visualization parameter to improve sorting purity and/or yield for a desired to-be-sorted population as taught by Sharpe (para. [0145], recites
[0145] “In general, the predetermined gating selection criteria may be any selection criteria that assists in satisfying the purity and/or yield desired for the to-be-sorted population. The gating selection criteria may be set based on absolute signal values, based on relative signal values, based on statistical parameters, etc. for any individually identified sub-populations and/or for any combination of the individually identified sub-populations. The gating region selection criteria may be based on any measured, sensed, and/or determined characteristic(s) as may be reflected on characteristic versus time graphs, single variable histograms, bivariate plots, set thresholds (absolute and/or relative), statistical analyses thereof, and the like, and/or any combination thereof.”
).
Allowable Subject Matter
Claim 20 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Izuka (US 2009/0226031 A1) discloses in the abstract and para(s). [0069] and [0074]:
[abstract] “A particle analyzer capable of extracting particle image for each particle at high accuracy, if a plurality of images of a particle are imaged. Concretely, a particle analyzer comprising a controller, including a memory under control of a processor, the memory storing instructions enabling the processor to carry out operations, comprising: acquiring extraction parameters for each particle based on each image of a particles; extracting particle images from each image of a particles based on the extraction parameters obtained for each particle; and analyzing particles based on the extracted particle image.”
[0069] “The still image imaged by the CCD camera 82 is output to the image processing substrate 6 (see FIG. 8) as a video signal via the video signal cable 103 . In the image input interface 101 of the image processing substrate 6 , the digitized image data is generated from the imaged image by performing A/D conversion on the video signal from the CCD camera 82 (see FIG. 8). The image data is a gray scale image. …”
[0074] “In step S 4 , the image processing processor 94 sets a binarization threshold value based on the data after the edge enhancement process has been performed. In other words, the Laplacian filter circuit of the image processing processor 94 is arranged with a luminance histogram portion executing the binarization threshold value setting processing. First, the image processing processor 94 creates a luminance histogram (see FIGS. 12 and 13) from the image data after the Laplacian filter processing. FIG. 12 shows the luminance histogram of the still image by the bright-field illumination, and FIG. 13 shows the luminance histogram of the still image by the dark-field illumination. The image processing processor 94 performs a predetermined smoothing processing on the luminance histogram. With respect to the still image by the bright-field illumination, the most frequent luminance value of the still image is obtained from the luminance histogram after the smoothing processing, and thereafter, the binarization threshold value is calculated by the following equation (2) by using the most frequent luminance value.”
Sawa et al. (“On-Chip Multiple Particle Velocity and Size Measurement Using Single-Shot Two-Wavelength Differential Image Analysis,” 2020) discloses in the abstract and the para. between pgs. 6-7:
[abstract] “Precise and quick measurement of samples’ flow velocities is essential for cell sorting timing control and reconstruction of acquired image-analyzed data. We developed a simple technique for the single-shot measurement of flow velocities of particles simultaneously in a microfluidic pathway. The speed was calculated from the difference in the particles’ elongation in an acquired image that appeared when two wavelengths of light with different irradiation times were applied. We ran microparticles through an imaging flow cytometer and irradiated two wavelengths of light with different irradiation times simultaneously to those particles. The mixture of the two wavelength transmitted lights was divided into two wavelengths, and the images of the same microparticles for each wavelength were acquired in a single shot. We estimated the velocity from the difference of its elongation divided by the difference of irradiation time by comparing these two images. The distribution of polystyrene beads’ velocity was parabolic and highest at the center of the flow channel, consistent with the expected velocity distribution of the laminar flow. Applying the calculated velocity, we also restored the accurate shapes and cross-sectional areas of particles in the images, indicating this simple method for improving of imaging flow cytometry and cell sorter for diagnostic screening of circulating tumor cells.”
[para. between pgs. 6-7] “For the velocity measurement, the images of samples were cut around the particle at 80 pixels (px) × 80 px from the full size of images of 530 and 590 nm wavelengths, and those images of the particles at two wavelengths with different irradiation times were saved as PNG files into the analysis computer. After the subtraction of the background with no prerecorded sample images, the 8-bit (256-step) grayscale values of obtained particle images were transformed into binary images using threshold values based on the average intensities of the acquired images of each wavelength. Using binarized images, the lengths of the X-axis, Y-axis, and the areas of particles were determined by counting the white pixels.”
Ma (US 2010/0061633 A1) discloses in the abstract and para(s). [0068]:
[abstract] “A method, device and computer readable storage media for enhancing an image for optical character recognition by detecting the edges of the image to create an edge detected image, binarizing the edge detected image to create a binary edge image for processing, dilating the binary edge image to create a dilated binary edge image, taking the XOR difference between the binary edge image and the dilated binary edge image to obtain a text boundary, superimposing the text boundary on the image and determining the pixels of the image that are covered by the text boundary, calculating the average grayscale value of the pixels of the image that are covered by the text boundary, and setting background pixels of the image to the calculated average grayscale value of the pixels of the image that are covered by the text boundary. …”
[0068] “There are two major ways to find the optimal binarization threshold T for a given image in accordance with exemplary embodiments. A first way is a single image-based approach in which a graphical user interface was designed to import one single edge image every time and to display the binarization result. As shown in FIG. 11, a graphical user interface input means such as slider 1102 with range [Tmin , Tmax] was set to change the threshold T continuously, where Tmin and Tmax are the minimum and maximum possible thresholds that can be used to binarize the edge image. Once a satisfying binarization result is achieved (a best tradeoff of noise reduction and keeping the target object), the slider value (threshold T) is recorded for that image. As shown in FIG. 10, this process is implemented at 1001 by displaying one image, 1101 , from C at a time and to letting the user adjust the binarization threshold T using the slider 1102 illustrated in FIG. 11. The user visually inspects the image at various threshold values. When the user finds the optimal binarization threshold T for the given image, the user presses the “submit” button 1104 . The current threshold value is displayed in the text area 1103 .”
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/J.Z.Y./Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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