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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/13/2024, 03/26/2025, and 06/12/2026 have been considered by the examiner.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or
composition of matter, or any new and useful improvement thereof, may obtain a patent
therefor, subject to the conditions and requirements of this title.
Claims 13 along with its dependent claims 14-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 13 recites a processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform operations as defined in the specification in paragraph [0119], “The point-of-care apparatus includes an electronic storage 1810, a processor 1820, a network interface 1840, and a memory 1850. The memory 1850 may be a volatile type of memory, e.g., RAM, or a non-volatile type of memory, e.g., NAND flash memory. The memory 1850 includes processor-readable instructions that are executable by the processor 1820 to cause the apparatus to perform various operations, including those mentioned herein, such as the operations shown and described in connection with FIGS. 8–17, and/or applying machine learning models or image analytics, among others.” Thus, the disclosure encompasses both transitory and non-transitory mediums (volatile & non-volatile), can be a signal or carrier wave etc; therefore, fails to fall within at least one of the four categories of patent eligible subject matter. It has been understood by the office that the processor-readable medium storing instructions is the same as the “memory 1850” and that these are forms of memory which are statutory and also transitory propagating signals, per se (a volatile type of memory, e.g., RAM, or a non-volatile type of memory, e.g., NAND flash memory), thus includes both transitory, and non-transitory subject matter; since the specification explicitly states memory 1850 may be a volatile type of memory, e.g., RAM, or a non-volatile type of memory, e.g., NAND flash memory. Therefore, claims 13-18 do not fit within the recognized categories of statutory subject matter. See MPEP 2106.
The office respectfully recommends the applicant amends claim 13 limitation “A processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform operations” to reflect the limitation “A non-transitory processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform operations”.
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 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 of this title, if the
differences between the claimed invention and the prior art are such that the claimed
invention as a whole would have been obvious before the effective filing date of the
claimed invention to a person having ordinary skill in the art to which the claimed
invention pertains. Patentability shall not be negated by the manner in which the
invention was made.
Claims 1, 2, 7, 8, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over YAFIN (US 20230003622 A1), hereinafter referenced as YAFIN in view of WENUS (US 20210216039 A1), hereinafter referenced as WENUS further in view of JACKSON (US 20210327080 A1), hereinafter referenced as JACKSON.
Regarding claim 1, YAFIN teaches an apparatus for analyzing a biological sample (Fig. 1, #20 called biological sample analysis system, Paragraph [0094] - YAFIN discloses FIG. 1, which is block diagram showing components of a biological sample analysis system 20, in accordance with some applications of the present invention.),
the apparatus comprising: at least one processor (Fig. 1, #28 called computer processor, Paragraph [0095] - YAFIN discloses a computer processor 28 typically receives and processes optical measurements that are performed by the optical measurement device.);
and at least one memory storing instructions (Fig. 1, #30 called memory, Paragraph [0095] - YAFIN discloses the computer processor communicates with a memory 30.) which,
when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
determining that a sample chamber contains a first type of biological sample (Fig. 9, Paragraph [0125] - YAFIN discloses a cell suspension is placed within a sample chamber (step 70) and allowed to settle to form a monolayer (step 71). Subsequently, at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74).),
the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber (Figs. 3A-3B, Paragraph [0099] - YAFIN discloses the sample carrier includes at least three components; a molded component 42, a glass layer 44 (e.g., a glass sheet), and an adhesive layer 46 configured to adhere the glass layer to an underside of the molded component. Paragraph [0110] - YAFIN discloses the sample is viewed by the optical measurement devices via the glass layer, glass being transparent at least to wavelengths that are typically used by the optical measurement device. See also Paragraph [0094].);
providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).):
focus on the respective depth (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
Although YAFIN further teaches and capture one or more images of one or more fields of view containing the respective depth in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).);
YAFIN fails to explicitly teach counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest;
However, WENUS explicitly teaches counting one or more types of constituents in the plurality of images (Fig. 4, Paragraph [0031] - WENUS discloses such a method of micro object detection may be used with DHM imaging techniques for particle detection, for counting and sizing in a volume of fluid. This technique includes capturing a holographic image of the volume followed by parallel holographic reconstruction at multiple depths, thus covering the whole volume simultaneously. WENUS further discloses due to the extra 3D information obtained through the multi-depth reconstruction, the technique will result in more accurate particle counting and sizing when compared to existing approaches. The 3D information may also help improve the ability to determine the particle type or particle characteristics; for instance, by having a 3D representation of a bacteria it will potentially be easier to identify the bacteria type.)
to determine one or more depth distributions of the one or more types of constituents across the plurality of depths (Fig. 3A-3B, Paragraph [0039] - WENUS discloses the hologram image of FIG. 3A, depicting polymer microbeads suspended in water, is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.),
the one or more types of constituents comprising a constituent of interest (Fig. 3A-3B, Paragraph [0039] - WENUS discloses FIG. 3A depicts a hologram image of microbeads, obtained using a digital holographic microscope, providing an example of such a multi-depth reconstruction, and FIG. 3B depicts corresponding reconstructed images obtained from the hologram image of FIG. 3A. Specifically, the hologram image of FIG. 3A, depicting polymer microbeads suspended in water [wherein polymer microbeads are a constituent of interest], is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN of having an apparatus for analyzing a biological sample, the apparatus comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
Wherein YAFIN’s apparatus wherein having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
YAFIN in view of WENUS fail to explicitly teach and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
However, JACKSON explicitly teaches and quantifying the constituent of interest in the biological sample based on the one or more depth distributions (Fig. 2A-2B, Paragraph [0040] - JACKSON discloses a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample. JACKSON further discloses FIG. 2B shows an example of such a projection image 250 that has been generated from a set of images including the set of images 200 shows in FIG. 2A. Such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Such an image segmentation could then be used to automatically determine a number, size, identity, morphology, or other information about cells, organoids, tumor spheroids, or some other analysis of the contents of the sample.)
and based on one or more depth distribution curves (Fig. 3A-3C, Paragraph [0051] - JACKSON discloses FIG. 3C illustrates a plot of a set of texture values 320 determined for a particular pixel of an output depth map as a function of the depth of the input images used to generate the texture values. A peak 325 is present in the texture data; this peak could be detected and the depth (‘d.sub.1’ in FIG. 3C) determined therefrom. See also Paragraph [0046].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN in view of WENUS of having an apparatus for analyzing a biological sample, the apparatus comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest; with the teachings of JACKSON having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
Wherein YAFIN’s apparatus wherein having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves both the sample count estimation and the view of the distribution of sample contents, since both YAFIN and JACKSON relate to optical imaging/analysis techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and JACKSON relates to a method for generating a projection image of a three-dimensional sample; the image processing methods described herein includes generating a plurality of images of a ‘three-dimensional’ sample wherein such a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample; such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and JACKSON (US 20210327080 A1), Paragraph [0002, 0033, 0040].
Regarding claim 2, YAFIN and WENUS in view of JACKSON teach the apparatus of claim 1,
YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
Although YAFIN explicitly teaches the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
YAFIN fails to explicitly teach forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
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Annotated diagram of WENUS Fig. 6 illustrating multi-depth image synthesis
However, WENUS explicitly teaches forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths (Fig. 6, Paragraph [0054] - WENUS discloses in the 3D multi-depth image synthesis method 600 shown in FIG. 6, memory that is sufficient for a 3D array representing the scanned volume of the sample is allocated at operation 602. At operation 604, an incoming binarized image slice is placed in the 3D array. Similar to the 2D multi-depth image synthesis method, this may also be done whenever a binarized image slice is processed and arrives to be processed. The image slice is copied to its corresponding place in the combined result. Paragraph [0039] - WENUS further discloses a unique feature of DHM is the ability to reconstruct the hologram at multiple depths (z-slices).),
wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension (Fig. 6, Paragraph [0055] - WENUS discloses if all of the binarized image slices have been processed, at operation 608, the holes in the 3D array are identified and filled. The resulting array is shown at the right side of FIG. 6. [See annotated Fig. 6 above].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having an apparatus for analyzing a biological sample, the apparatus comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
Wherein YAFIN’s apparatus wherein having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
Regarding claim 7, YAFIN teaches a method for analyzing a biological sample (Figs. 8-9, Paragraph [0125] - YAFIN discloses FIG. 8 and FIG. 9, which are flowcharts showing step of methods that are performed with respect to platelets within a blood sample, in accordance with some applications of the present invention.),
the method comprising: determining that a sample chamber contains a first type of biological sample (Fig. 9, Paragraph [0125] - YAFIN discloses a cell suspension is placed within a sample chamber (step 70) and allowed to settle to form a monolayer (step 71). Subsequently, at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74).),
the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber (Figs. 3A-3B, Paragraph [0099] - YAFIN discloses the sample carrier includes at least three components; a molded component 42, a glass layer 44 (e.g., a glass sheet), and an adhesive layer 46 configured to adhere the glass layer to an underside of the molded component. Paragraph [0110] - YAFIN discloses the sample is viewed by the optical measurement devices via the glass layer, glass being transparent at least to wavelengths that are typically used by the optical measurement device. See also Paragraph [0094].);
providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).):
focus on the respective depth (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
Although YAFIN further teaches and capture one or more images of one or more fields of view containing the respective depth in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).);
YAFIN fails to explicitly teach counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest;
However, WENUS explicitly teaches counting one or more types of constituents in the plurality of images (Fig. 4, Paragraph [0031] - WENUS discloses such a method of micro object detection may be used with DHM imaging techniques for particle detection, for counting and sizing in a volume of fluid. This technique includes capturing a holographic image of the volume followed by parallel holographic reconstruction at multiple depths, thus covering the whole volume simultaneously. WENUS further discloses due to the extra 3D information obtained through the multi-depth reconstruction, the technique will result in more accurate particle counting and sizing when compared to existing approaches. The 3D information may also help improve the ability to determine the particle type or particle characteristics; for instance, by having a 3D representation of a bacteria it will potentially be easier to identify the bacteria type.)
to determine one or more depth distributions of the one or more types of constituents across the plurality of depths (Fig. 3A-3B, Paragraph [0039] - WENUS discloses the hologram image of FIG. 3A, depicting polymer microbeads suspended in water, is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.),
the one or more types of constituents comprising a constituent of interest (Fig. 3A-3B, Paragraph [0039] - WENUS discloses FIG. 3A depicts a hologram image of microbeads, obtained using a digital holographic microscope, providing an example of such a multi-depth reconstruction, and FIG. 3B depicts corresponding reconstructed images obtained from the hologram image of FIG. 3A. Specifically, the hologram image of FIG. 3A, depicting polymer microbeads suspended in water [wherein polymer microbeads are a constituent of interest], is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN of having a method for analyzing a biological sample, the method comprising: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
Wherein YAFIN’s method wherein having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
The motivation behind the modification would have been to obtain an enhanced method for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
YAFIN in view of WENUS fail to explicitly teach and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
However, JACKSON explicitly teaches and quantifying the constituent of interest in the biological sample based on the one or more depth distributions (Fig. 2A-2B, Paragraph [0040] - JACKSON discloses a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample. JACKSON further discloses FIG. 2B shows an example of such a projection image 250 that has been generated from a set of images including the set of images 200 shows in FIG. 2A. Such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Such an image segmentation could then be used to automatically determine a number, size, identity, morphology, or other information about cells, organoids, tumor spheroids, or some other analysis of the contents of the sample.)
and based on one or more depth distribution curves (Fig. 3A-3C, Paragraph [0051] - JACKSON discloses FIG. 3C illustrates a plot of a set of texture values 320 determined for a particular pixel of an output depth map as a function of the depth of the input images used to generate the texture values. A peak 325 is present in the texture data; this peak could be detected and the depth (‘d.sub.1’ in FIG. 3C) determined therefrom. See also Paragraph [0046].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN in view of WENUS of having a method for analyzing a biological sample, the method comprising: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest; with the teachings of JACKSON having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
Wherein YAFIN’s method wherein having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
The motivation behind the modification would have been to obtain an enhanced method for analyzing a sample that improves both the sample count estimation and the view of the distribution of sample contents, since both YAFIN and JACKSON relate to optical imaging/analysis techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and JACKSON relates to a method for generating a projection image of a three-dimensional sample; the image processing methods described herein includes generating a plurality of images of a ‘three-dimensional’ sample wherein such a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample; such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and JACKSON (US 20210327080 A1), Paragraph [0002, 0033, 0040].
Regarding claim 8, YAFIN and WENUS in view of JACKSON teach the method of claim 7,
YAFIN fails to explicitly teach further comprising: forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
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Annotated diagram of WENUS Fig. 6 illustrating multi-depth image synthesis
However, WENUS explicitly teaches forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths (Fig. 6, Paragraph [0054] - WENUS discloses in the 3D multi-depth image synthesis method 600 shown in FIG. 6, memory that is sufficient for a 3D array representing the scanned volume of the sample is allocated at operation 602. At operation 604, an incoming binarized image slice is placed in the 3D array. Similar to the 2D multi-depth image synthesis method, this may also be done whenever a binarized image slice is processed and arrives to be processed. The image slice is copied to its corresponding place in the combined result. Paragraph [0039] - WENUS further discloses a unique feature of DHM is the ability to reconstruct the hologram at multiple depths (z-slices).),
wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension (Fig. 6, Paragraph [0055] - WENUS discloses if all of the binarized image slices have been processed, at operation 608, the holes in the 3D array are identified and filled. The resulting array is shown at the right side of FIG. 6. [See annotated Fig. 6 above].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having a method for analyzing a biological sample, the method comprising: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
Wherein YAFIN’s method wherein having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
The motivation behind the modification would have been to obtain an enhanced method for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
Regarding claim 13, YAFIN teaches a processor-readable medium storing instructions (Fig. 1, Paragraph [0136] - YAFIN discloses computer program instructions may also he stored in a computer-readable medium (e.g., a non-transitory computer-readable medium) that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart blocks and algorithms.) which,
when executed by at least one processor of an apparatus (Fig. 1, Paragraph [0131] - YAFIN discloses applications of the invention described herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non-transitory computer-readable medium) providing program code for use by or in connection with a computer or any instruction execution system, such as computer processor 28. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.), causes the apparatus at least to perform:
determining that a sample chamber contains a first type of biological sample (Fig. 9, Paragraph [0125] - YAFIN discloses a cell suspension is placed within a sample chamber (step 70) and allowed to settle to form a monolayer (step 71). Subsequently, at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74).),
the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber (Figs. 3A-3B, Paragraph [0099] - YAFIN discloses the sample carrier includes at least three components; a molded component 42, a glass layer 44 (e.g., a glass sheet), and an adhesive layer 46 configured to adhere the glass layer to an underside of the molded component. Paragraph [0110] - YAFIN discloses the sample is viewed by the optical measurement devices via the glass layer, glass being transparent at least to wavelengths that are typically used by the optical measurement device. See also Paragraph [0094].);
providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).):
focus on the respective depth (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
Although YAFIN further teaches and capture one or more images of one or more fields of view containing the respective depth in the sample chamber (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).);
YAFIN fails to explicitly teach counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest;
However, WENUS explicitly teaches counting one or more types of constituents in the plurality of images (Fig. 4, Paragraph [0031] - WENUS discloses such a method of micro object detection may be used with DHM imaging techniques for particle detection, for counting and sizing in a volume of fluid. This technique includes capturing a holographic image of the volume followed by parallel holographic reconstruction at multiple depths, thus covering the whole volume simultaneously. WENUS further discloses due to the extra 3D information obtained through the multi-depth reconstruction, the technique will result in more accurate particle counting and sizing when compared to existing approaches. The 3D information may also help improve the ability to determine the particle type or particle characteristics; for instance, by having a 3D representation of a bacteria it will potentially be easier to identify the bacteria type.)
to determine one or more depth distributions of the one or more types of constituents across the plurality of depths (Fig. 3A-3B, Paragraph [0039] - WENUS discloses the hologram image of FIG. 3A, depicting polymer microbeads suspended in water, is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.),
the one or more types of constituents comprising a constituent of interest (Fig. 3A-3B, Paragraph [0039] - WENUS discloses FIG. 3A depicts a hologram image of microbeads, obtained using a digital holographic microscope, providing an example of such a multi-depth reconstruction, and FIG. 3B depicts corresponding reconstructed images obtained from the hologram image of FIG. 3A. Specifically, the hologram image of FIG. 3A, depicting polymer microbeads suspended in water [wherein polymer microbeads are a constituent of interest], is reconstructed at three different depths in the depictions of FIG. 3B. The markings in FIG. 3B indicate particles that are in-focus at certain depths and out-of-focus at different depths, thus highlighting the three-dimensional distribution of the particles within the imaged volume.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN of having a processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
Wherein YAFIN’s processor-readable medium storing instructions wherein having counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest.
The motivation behind the modification would have been to obtain an enhanced method for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
YAFIN in view of WENUS fail to explicitly teach and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
However, JACKSON explicitly teaches and quantifying the constituent of interest in the biological sample based on the one or more depth distributions (Fig. 2A-2B, Paragraph [0040] - JACKSON discloses a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample. JACKSON further discloses FIG. 2B shows an example of such a projection image 250 that has been generated from a set of images including the set of images 200 shows in FIG. 2A. Such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Such an image segmentation could then be used to automatically determine a number, size, identity, morphology, or other information about cells, organoids, tumor spheroids, or some other analysis of the contents of the sample.)
and based on one or more depth distribution curves (Fig. 3A-3C, Paragraph [0051] - JACKSON discloses FIG. 3C illustrates a plot of a set of texture values 320 determined for a particular pixel of an output depth map as a function of the depth of the input images used to generate the texture values. A peak 325 is present in the texture data; this peak could be detected and the depth (‘d.sub.1’ in FIG. 3C) determined therefrom. See also Paragraph [0046].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN in view of WENUS of having a a processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest; with the teachings of JACKSON having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
Wherein YAFIN’s processor-readable medium storing instructions wherein having and quantifying the constituent of interest in the biological sample based on the one or more depth distributions and based on one or more depth distribution curves.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves both the sample count estimation and the view of the distribution of sample contents, since both YAFIN and JACKSON relate to optical imaging/analysis techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and JACKSON relates to a method for generating a projection image of a three-dimensional sample; the image processing methods described herein includes generating a plurality of images of a ‘three-dimensional’ sample wherein such a set of images 200 can contain sufficient in-focus image information to generate a projection image of the sample that provides an improved view of the distribution of the contents within the sample; such a projection image could facilitate improved imaging and analysis of the contents of a sample. For example, a projection image could be segmented in order to identify discrete cells, organoids, tumor spheroids, or other three-dimensional contents of the projection image. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and JACKSON (US 20210327080 A1), Paragraph [0002, 0033, 0040].
Regarding claim 14, YAFIN and WENUS in view of JACKSON teach the processor-readable medium of claim 13,
YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
Although YAFIN explicitly teaches the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
YAFIN fails to explicitly teach forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
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Annotated diagram of WENUS Fig. 6 illustrating multi-depth image synthesis
However, WENUS explicitly teaches forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths (Fig. 6, Paragraph [0054] - WENUS discloses in the 3D multi-depth image synthesis method 600 shown in FIG. 6, memory that is sufficient for a 3D array representing the scanned volume of the sample is allocated at operation 602. At operation 604, an incoming binarized image slice is placed in the 3D array. Similar to the 2D multi-depth image synthesis method, this may also be done whenever a binarized image slice is processed and arrives to be processed. The image slice is copied to its corresponding place in the combined result. Paragraph [0039] - WENUS further discloses a unique feature of DHM is the ability to reconstruct the hologram at multiple depths (z-slices).),
wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension (Fig. 6, Paragraph [0055] - WENUS discloses if all of the binarized image slices have been processed, at operation 608, the holes in the 3D array are identified and filled. The resulting array is shown at the right side of FIG. 6. [See annotated Fig. 6 above].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having a processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of WENUS having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
Wherein YAFIN’s processor-readable medium storing instructions wherein having forming a three-dimensional pixel array based on at least some of the one or more images of the one or more fields of view containing the respective depths of the plurality of depths, wherein the three-dimensional pixel array comprises a first dimension, a second dimension, and a third dimension.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves sample count estimation and determination of sample constituent types, since both YAFIN and WENUS relate to optical investigation techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and WENUS generally relates to imaging and image processing techniques involving the use of digital holographic microscopy; the technique will result in more accurate particle counting and sizing when compared to existing approaches, and the 3D information may also help improve the ability to determine the particle type or particle characteristics. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and WENUS (US 20210216039 A1), Paragraph [0002, 0031].
Claims 3, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over YAFIN (US 20230003622 A1), hereinafter referenced as YAFIN in view of WENUS (US 20210216039 A1), hereinafter referenced as WENUS further in view of JACKSON (US 20210327080 A1), hereinafter referenced as JACKSON further in view of DE CORRAL (US 20130080073 A1), hereinafter referenced as DE CORRAL.
Regarding claim 3, YAFIN and WENUS in view of JACKSON teach the apparatus of claim 2,
Although YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
YAFIN and WENUS in view of JACKSON fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
However, DE CORRAL explicitly teaches applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
at least one one-dimensional convolution in the second dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
and at least one one-dimensional convolution in the third dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having an apparatus for analyzing a biological sample, the apparatus comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of DE CORRAL having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
Wherein YAFIN’s apparatus wherein having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves sample count estimation and reduces computation time, since both YAFIN and DE CORRAL relate to data processing techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and DE CORRAL generally relates to analysis of compounds, and, more particularly, to detection and quantification of ions collected by liquid chromatography, ion-mobility spectrometry and mass spectrometry wherein reducing the volume over which three-dimensional convolution elements are computed additionally reduces computation time. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and DE CORRAL (US 20130080073 A1), Paragraph [0003, 0410].
Regarding claim 9, YAFIN and WENUS in view of JACKSON teach the method of claim 8,
Although YAFIN further teaches wherein the providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).) comprises:
YAFIN and WENUS in view of JACKSON fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
However, DE CORRAL explicitly teaches applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
at least one one-dimensional convolution in the second dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
and at least one one-dimensional convolution in the third dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having a method for analyzing a biological sample, the method comprising: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of DE CORRAL having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
Wherein YAFIN’s method wherein having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
The motivation behind the modification would have been to obtain an enhanced method for analyzing a sample that improves sample count estimation and reduces computation time, since both YAFIN and DE CORRAL relate to data processing techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and DE CORRAL generally relates to analysis of compounds, and, more particularly, to detection and quantification of ions collected by liquid chromatography, ion-mobility spectrometry and mass spectrometry wherein reducing the volume over which three-dimensional convolution elements are computed additionally reduces computation time. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and DE CORRAL (US 20130080073 A1), Paragraph [0003, 0410].
Regarding claim 15, YAFIN and WENUS in view of JACKSON teach the processor-readable medium of claim 14,
Although YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
YAFIN and WENUS in view of JACKSON fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
However, DE CORRAL explicitly teaches applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
at least one one-dimensional convolution in the second dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.),
and at least one one-dimensional convolution in the third dimension (Fig. 24, Paragraph [0401] - DE CORRAL discloses one way to reduce the number of operations of a computation is to implement three-dimensional convolution filters as a series of one-dimensional convolution filters. In the case of a three-dimensional convolution, a 21.times.21.times.21 three-dimensional convolution filter is replaced with nine one-dimensional convolutions, three one-dimensional convolutions in each dimension. This approach reduces calculation time, for example, by a factor of 6 in the two-dimensional case and a factor of 48 in the three-dimensional case.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of YAFIN and WENUS in view of JACKSON of having a processor-readable medium storing instructions which, when executed by at least one processor of an apparatus, causes the apparatus at least to perform: determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; with the teachings of DE CORRAL having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
Wherein YAFIN’s processor-readable medium wherein having applying, for at least a portion of the three-dimensional pixel array, at least one one-dimensional convolution in the first dimension, at least one one-dimensional convolution in the second dimension, and at least one one-dimensional convolution in the third dimension.
The motivation behind the modification would have been to obtain an enhanced apparatus for analyzing a sample that improves sample count estimation and reduces computation time, since both YAFIN and DE CORRAL relate to data processing techniques, wherein YAFIN relates generally to analysis of bodily samples, and in particular, to optical density and microscopic measurements that are performed upon blood samples; in order to accurately estimate the number of platelets in the sample, platelets that are suspended within the cell solution are identified, in addition to identifying platelets within the monolayer focus field; and DE CORRAL generally relates to analysis of compounds, and, more particularly, to detection and quantification of ions collected by liquid chromatography, ion-mobility spectrometry and mass spectrometry wherein reducing the volume over which three-dimensional convolution elements are computed additionally reduces computation time. Please see YAFIN (US 20230003622 A1), Paragraph [0002, 0114], and DE CORRAL (US 20130080073 A1), Paragraph [0003, 0410].
Allowable Subject Matter
Claim 4, along with dependent claims 5-6, are therefrom objected to as being dependent upon rejected base claim 1, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
Claim 10, along with dependent claims 11-12, are therefrom objected to as being dependent upon rejected base claim 7, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims.
Claim 16, along with dependent claims 17-18, are therefrom objected to as being dependent upon rejected base claim 13, but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims, once 35 U.S.C. 101 rejections are overcome.
The following is a statement of reasons for the indication of allowable subject matter:
With regards to dependent claim 4, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 4, the prior arts fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, a narrower convolutional operation in the first dimension and the second dimension, a wider convolutional operation in the first dimension and the second dimension, and at least one one-dimensional convolution in the third dimension.
Regarding claim 4, YAFIN and WENUS in view of JACKSON teach the apparatus of claim 2,
YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
With regards to dependent claim 10, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 10, the prior arts fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, a narrower convolutional operation in the first dimension and the second dimension, a wider convolutional operation in the first dimension and the second dimension, and at least one one-dimensional convolution in the third dimension.
Regarding claim 10, YAFIN and WENUS in view of JACKSON teach the method of claim 8,
YAFIN further teaches wherein the providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).) comprises:
With regards to dependent claim 16, the cited prior arts fail to explicitly teach the following limitation in combination with all claim limitations:
Regarding claim 16, the prior arts fail to explicitly teach applying, for at least a portion of the three-dimensional pixel array, a narrower convolutional operation in the first dimension and the second dimension, a wider convolutional operation in the first dimension and the second dimension, and at least one one-dimensional convolution in the third dimension.
Regarding claim 16, YAFIN and WENUS in view of JACKSON teach the processor-readable medium of claim 14,
YAFIN further teaches wherein in providing the plurality of images (Fig. 9, Paragraph [0125] - YAFIN discloses at least one image is acquired while the microscope is focused at the monolayer-depth-level (step 72), and platelets that have settled within the monolayer are identified in the image (step 74). Additionally, at least one microscopic image of the sample is acquired, while the microscope is focused at a different depth level from the monolayer-depth-level (step 73). and platelets that have not settled within the monolayer are identified within the at least one microscopic image at the different depth level from the monolayer-depth-level (step 75).),
the instructions, when executed by the at least one processor (Fig. 1, Paragraph [0133] - YAFIN discloses data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus.), cause the apparatus at least to perform:
Examiner Remarks
Claims 1-12 were not rejected under 35 U.S.C. 101. Independent claims 1 and 7 were directed to a machine (apparatus) and process (method), respectively, which fall within the four statutory categories. Although the claims recite limitations that might fall under the mental processes grouping of abstract ideas (i.e., counting one or more types of constituents in the plurality of images to determine one or more depth distributions of the one or more types of constituents across the plurality of depths, the one or more types of constituents comprising a constituent of interest; and quantifying the constituent of interest in the biological sample based on the one or more depth distributions), those concepts are integrated into a practical application as they are applied in conjunction with controlling an imaging device to obtain images of a sample at various depths in a chamber along with subsequent processing (i.e., determining that a sample chamber contains a first type of biological sample, the sample chamber comprising at least one bottom wall through which an imaging device is configured to image the sample chamber; providing a plurality of images by controlling the imaging device to, for each depth of a plurality of depths in the sample chamber: focus on the respective depth, and capture one or more images of one or more fields of view containing the respective depth in the sample chamber; and quantifying… based on one or more depth distribution curves). The claimed operations are said to improve the process of sample analysis through specific recited image acquisition and processing techniques rather than simply using a computer as a tool to perform calculations. Thus, the claims are not directed to a judicial exception under Step 2A, Prong Two of the 35 U.S.C. 101 analysis.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
BEN-YAKAR et al. (US 20240094193 A1) - A microfluidic device capable of trapping contents in a manner suitable for high-throughput imaging is described herein. The microfluidic device may include one or more trapping devices, with each trapping device having a plurality of trapping channels. The trapping channels may be configured to receive contents via an inlet channel that connects a sample reservoir to the trapping channels via fluid communication. The trapping channels are shaped such that contents within the trapping channels are positioned for optimal imaging purposes. The trapping channels are also connect to at least one exit channel via fluid communication. The fluid, and contents within the fluid, maybe controlled via hydraulic pressure.… Fig. 1, Abstract.
WEISS et al. (US 20210096056 A1) - An imaging system (200) including a phase-modulating element (202) configured and arranged with optics (100) in an imaging path (300) of the imaging system, to modulate light emitted from an object (150), while the object is in motion with respect to the imaging system, to create a modified point-spread function (PSF); and a processor (700) configured and arranged to generate, on an image plane (500) of the imaging system, a three-dimensional image from the modulated light to provide depth-based characteristics of the object. Other applications are also described.… Fig. 1, Abstract.
LI et al. (US 20210069696 A1) - The invention provides novel sample chambers, sample analysis units, multi-well plates, and imaging and analytical systems as well as methods for accurate, efficient and high-throughput imaging, measurement and analysis of diverse types of biological cells to obtain information such as cell count, cell size, cell concentration, cell sub-population, cell morphology, cell viability, etc.… Fig. 1, Abstract.
EL-ZEHIRY et al. (US 20190087638 A1) - A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.… Fig. 1, Abstract.
JONES et al. (US 20170326549 A1) - This disclosure describes single-use test cartridges, cell analyzer apparatus, and methods for automatically performing microscopic cell analysis tasks, such as counting blood cells in biological samples. A small unmeasured quantity of a biological sample such as whole blood is placed in the disposable test cartridge which is then inserted into the cell analyzer. The analyzer isolates a precise volume of the biological sample, mixes it with self-contained reagents and transfers the entire volume to an imaging chamber. The geometry of the imaging chamber is chosen to maintain the uniformity of the mixture, and to prevent cells from crowding or clumping, when it is transferred into the imaging chamber. Images of essentially all of the cellular components within the imaging chamber are analyzed to obtain counts per unit volume. The devices, apparatus and methods described may be used to analyze a small quantity of whole blood to obtain counts per unit volume of red blood cells, white blood cells, including sub-groups of white cells, platelets and measurements related to these bodies..… Fig. 1, Abstract.
ZENG et al. (US 20110150305 A1) - Methods and systems are provided for correcting artifacts in iterative reconstruction processes. In certain embodiments, weighting schemes may be applied such that less than all of the available scan or projection data is utilized in the iterative reconstruction. In this manner, inconsistencies in the data undergoing reconstruction may be reduced...… Fig. 1, Abstract.
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/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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