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 .
Specification
The clean copy of the specification along with the marked-up copy of the specification filed on 02/14/2025 has been entered and made of record.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “wherein a resolution of a high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images” in claim 1 is a relative term which renders the claim indefinite. The term “a resolution of a high-resolution digital color image is relatively higher than a resolution of at least one digital image” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As claimed the metes and the bounds of “resolution” i.e levels/numbers how high? etc. cannot be determined and also scope of “relatively higher” is indefinite as the term of the degree renders the claim indefinite. Amendments/clarification are required. Claims 2-8 and 16 depend on claim 1, therefore they are rejected.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “wherein a resolution of a high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images” in claim 9 is a relative term which renders the claim indefinite. The term “a resolution of a high-resolution digital color image is relatively higher than a resolution of at least one digital image” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As claimed the metes and the bounds of “resolution” i.e levels/numbers how high? etc. cannot be determined and also scope of “relatively higher” is indefinite as the term of the degree renders the claim indefinite. Amendments/clarification are required.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “wherein a resolution of the constructed digital color image is relatively higher than a resolution of at least one digital image of the input set of digital images” in claim 10 is a relative term which renders the claim indefinite. The term “a resolution of the constructed digital color image is relatively higher than a resolution of at least one digital image” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As claimed the metes and the bounds of “resolution” i.e levels/numbers how high? etc. cannot be determined and also scope of “relatively higher” is indefinite as the term of the degree renders the claim indefinite. Amendments/clarification are required. Claims 11-15 depend on claim 10, therefore they are rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over NPL1 (Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy, Kevin D Haan et al., IEEE, 2020, Pages 30-50) hereafter NPL1 in view of HAYUT Itai et al., (WO2018078448A1) hereafter HAYUT.
1. Regarding claim 1 as best understood by the examiner, NPL1 discloses a method (page 31 fig 1 shows a method of Deep learning network) for training a machine learning model to construct a digital color image depicting a sample, the method comprising:
acquiring a training set of digital images of a training sample by: illuminating, by a (page 31, fig 1 (b)
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shows the “Network inputs” images using “bright-field microscopy image” and page 32 section II discloses “bright-field microscopy” which uses white light illumination meeting the claim limitations of acquiring a training set of digital images of a training sample by: illuminating, by a ;
receiving a ground truth comprising a high-resolution digital color image of the training sample (pages 31, 37 section VI, fig 1 shows receiving a ground truth comprising a high-resolution digital color image of the training sample (i.e input) as seen by the process flow arrow in fig 1), wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images (fig 1 and page 37 section VI shows and discloses wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images, examiner notes that the specifics of “a resolution” is not required by the current claim); and
training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth
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(pages 31, 37 section VI, fig 1 shows training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth as seen by the process flow arrow in fig 1). NPL1 discloses on page 32 section II discloses “bright-field microscopy” which uses white light illumination. NPL1 is silent and however fails to disclose illuminating, by a plurality of white light emitting diodes, illuminating the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample.
HAYUT shows and discloses illuminating, by a plurality of white light emitting diodes, illuminating the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). Before the effective filing date of the invention was made, HAYUT and NPL1 are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be a higher performance i.e greater accuracy, sensitivity and specificity system/method at para 0034. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of HAYUT in the method of NPL1 to obtain the invention as specified in claim 1.
2. Regarding claim 2, NPL1 and HAYUT disclose the method according to claim 1. NPL1 shows and discloses further wherein at least one digital image of the training set of digital images is captured using a first microscope objective, and wherein the act of receiving the ground truth comprises: illuminating the training sample with a bright-field illumination pattern; and capturing, using a second microscope objective, the high-resolution digital color image of the training sample while the training sample is illuminated with the bright-field illumination pattern, wherein a numerical aperture of the second microscope objective is higher than a numerical aperture of the first microscope objective (fig 1 and pages 31-32 shows and discloses wherein at least one digital image of the training set of digital images is captured using a first microscope objective, and wherein the act of receiving the ground truth comprises: illuminating the training sample with a bright-field illumination pattern; and capturing, using a second microscope objective, the high-resolution digital color image of the training sample while the training sample is illuminated with the bright-field illumination pattern, wherein a numerical aperture of the second microscope objective is higher than a numerical aperture of the first microscope objective). HAYUT also disclose capturing the training images with multiple microscope objective lens and higher numerical apertures in paras 0036-0040. NPL1 and HAYUT in combination would therefore meet the limitations of claim 2.
3. Regarding claim 3, NPL1 and HAYUT disclose the method according to claim 1. NPL1 disclose the white light illumination on page 32 and HAYUT disclose wherein each white light emitting diode of the plurality of white light emitting diodes is configured to illuminate the training sample from one direction of a plurality of directions (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). NPL1 and HAYUT in combination would therefore meet the limitations of claim 3.
4. Regarding claim 4, NPL1 and HAYUT, disclose the method according to claim 3. NPL1 shows and discloses wherein at least one digital image of the training set of digital images is captured using a first microscope objective (fig 1, pages 31-32) and HAYUT disclose wherein at least one direction of the plurality of directions corresponds to an angle larger than a numerical aperture of the first microscope objective (paras 0048 and 0036-0040 disclose wherein at least one direction of the plurality of directions corresponds to an angle larger than a numerical aperture of the first microscope objective). NPL1 and HAYUT in combination would therefore meet the limitations of claim 4.
5. Regarding claim 5, NPL1 discloses a method (page 31 fig 1 shows a method for constructing a digital color image depicting a sample using Deep learning network) for constructing a digital color image depicting a sample, the method comprising:
receiving an input set of digital images of the sample, wherein the input set of digital images is acquired by illuminating, by a (page 31, fig 1 (b)
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shows the “Network inputs” images using “bright-field microscopy image” and page 32 section II discloses “bright-field microscopy” which uses white light illumination meeting the claim limitations receiving an input set of digital images of the sample, wherein the input set of digital images is acquired by illuminating, by a ;
constructing a digital color image depicting the sample by: inputting the input set of digital images into a machine learning model being trained according to the method of claim 1
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fig 1, pages 31-32 shows and discloses constructing a digital color image (i.e network outputs) depicting the sample by: inputting the input set of digital images (network inputs) into a machine learning model being trained (i.e Deep neural network being trained) according to the method of claim 1 meeting the claim limitations, pages 31, 37 section VI, fig 1 shows training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth as seen by the process flow arrow in fig 1)), and
receiving, from the machine learning model, an output comprising the constructed digital color image depicting a sample (fig 1 shows the network outputs comprising the constructed digital color image depicting a sample
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, wherein a resolution of the constructed digital color image is relatively higher than a resolution of at least one digital image of the input set of digital images (fig 1 and page 37 section VI shows and discloses wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images, examiner notes that the specifics of “a resolution” is not required by the current claim). NPL1 discloses on page 32 section II discloses “bright-field microscopy” which uses white light illumination. NPL1 is silent and however fails to disclose illuminating, by a plurality of white light emitting diodes, illuminating the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample.
HAYUT shows and discloses illuminating, by a plurality of white light emitting diodes, illuminating the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). Before the effective filing date of the invention was made, HAYUT and NPL1 are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be a higher performance i.e greater accuracy, sensitivity and specificity system/method at para 0034. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of HAYUT in the method of NPL1 to obtain the invention as specified in claim 5.
6. Regarding claim 6, NPL1 and HAYUT disclose the method according to claim 5. NPL1 disclose wherein the act of receiving the input set of digital images of the sample and HAYUT disclose further comprises: acquiring the input set of digital images of the sample by: illuminating, by a plurality of white light emitting diodes, the sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the sample(Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). NPL1 and HAYUT in combination would therefore meet the limitations of claim 6.
7. Regarding claim 7, NPL1 and HAYUT disclose the method according to claim 5. NPL1 discloses the white light illumination in pages 31-32 and HAYUT disclose wherein each white light emitting diode of the plurality of white light emitting diodes is configured to illuminate the sample from one direction of a plurality of directions (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). NPL1 and HAYUT in combination would therefore meet the limitations of claim 7.
8. Regarding claim 8, NPL1 and HAYUT disclose the method according to claim 5. NPL1 disclose capturing digital images using the microscope, wherein each digital image of the input set of digital images is captured using a microscope objective (fig 1 and disclosure in pages 31-32 “bright-field illumination microscopy” and microscope objective), and HAYUT disclose wherein at least one direction of the plurality of directions corresponds to an angle larger than a numerical aperture of the microscope objective (paras 0036-0040 and 0048). NPL1 and HAYUT in combination would therefore meet the limitations of claim 8.
9. Claim 9 as best understood by the examiner is a corresponding device claim of claim 1. See the explanation of claim 1. NPL1 in fig 1 and pages 30-32 shows and discloses a computational microscopy using deep neural networks meeting the claim limitations of device comprising circuitry.
10. Regarding claim 10, as best understood by the examiner, NPL1 discloses a microscope system (fig 1, pages 30-32 shows and discloses a microscopy system) comprising:
an illumination system comprising a (page 31, fig 1 (b)
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shows the “Network inputs” images using “bright-field microscopy image” and page 32 section II discloses “bright-field microscopy” which uses white light illumination meeting the claim limitations of acquiring a training set of digital images of a training sample by: an illumination system comprising a ;
an image sensor configured to capture digital images of the sample (fig 1 shows the microscopic images, page 32 col 1 disclose the “microscopic imaging system” and page 46 col 2 discloses cameras capturing the images meeting the above claim limitations);
a microscope objective configured to image the sample onto the image sensor (fig 1 shows the images captured by bright-field illumination by microscope and page 32 disclose the microscopic imaging system with the “lens” (i.e the objective) meeting the claim limitations);
and
circuitry configured to execute: an acquisition function configured to acquire an input set of digital images by being configured to: control (page 31, fig 1 (b)
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shows the “Network inputs” images using “bright-field microscopy image” and page 32 section II discloses “bright-field microscopy” which uses white light illumination meeting the claim limitations of acquiring a training set of digital images of a training sample by: illuminating, by a and wherein the circuitry is further configured to execute:
an image construction function configured to: input the input set of digital images into a machine learning model being trained according to the method of claim 1(see the explanation of the method of claim 1 and fig 1 shows the “Network inputs” images input to the machine learning model meeting the claim limitations), and receive, from the machine learning model, an output comprising a constructed digital color image depicting the sample (fig 1 shows the “network outputs” from the network meeting the above claim limitations); wherein a resolution of the constructed digital color image is relatively higher than a resolution of at least one digital image of the input set of digital images (fig 1 and page 37 section VI shows and discloses wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images, examiner notes that the specifics of “a resolution” is not required by the current claim). NPL1 discloses on page 32 section II discloses “bright-field microscopy” which uses white light illumination. NPL1 is silent and however fails to disclose control the plurality of white light emitting diodes of the illumination system to illuminate the sample with each illumination pattern of the plurality of illumination patterns, and control the image sensor to capture a digital image of the sample for each illumination pattern of the plurality of illumination patterns.
HAYUT shows and discloses control the plurality of white light emitting diodes of the illumination system to illuminate the sample with each illumination pattern of the plurality of illumination patterns, and control the image sensor to capture a digital image of the sample for each illumination pattern of the plurality of illumination patterns (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). Before the effective filing date of the invention was made, HAYUT and NPL1 are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be a higher performance i.e greater accuracy, sensitivity and specificity system/method at para 0034. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of HAYUT in the system of NPL1 to obtain the invention as specified in claim 10.
11. Regarding claim 11, NPL1 and HAYUT disclose the microscope system according to claim 10, wherein each of the plurality of white light emitting diodes is configured to illuminate the sample from one direction of a plurality of directions (Para 0048 discloses “Microscope 100 may include illumination assembly 110. The term "illumination assembly" refers to any device or system capable of projecting light to illuminate sample 114. Illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), lasers, and lamps configured to emit light. In one embodiment, illumination assembly 110 may include only a single light source. Alternatively, illumination assembly 110 may include four, sixteen, or even more than a hundred light sources organized in an array or a matrix. In some embodiments, illumination assembly 110 may use one or more light sources located at a surface parallel to illuminate sample 114. In other embodiments, illumination assembly 110 may use one or more light sources located at a surface perpendicular or at an angle to sample 114. In addition, illumination assembly 110 may be configured to illuminate sample 114 in a series of different illumination conditions. In one example, illumination assembly 110 may include a plurality of light sources arranged in different illumination angles, such as a two- dimensional arrangement of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle al, and a beam 120 projected from a second illumination angle a2. As another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns. Accordingly and consistent with the present disclosure, the different illumination conditions may be selected from a group including: different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof meeting the above claim limitations). NPL1 and HAYUT in combination would therefore meet the limitations of claim 11.
12. Regarding claim 12, NPL1 and HAYUT disclose the microscope system according to claim 11. NPL1 shows and discloses further wherein at least one digital image of the training set of digital images is captured using a first microscope objective (fig 1, pages 31-32) and HAYUT disclose further wherein at least one direction of the plurality of directions corresponds to an angle larger than a numerical aperture of the first microscope objective (paras 0048 and 0036-0040 disclose wherein at least one direction of the plurality of directions corresponds to an angle larger than a numerical aperture of the first microscope objective). NPL1 and HAYUT in combination would therefore meet the limitations of claim 12.
13. Regarding claim 15, NPL1 and HAYUT disclose the microscope system according to claim 10. HAYUT discloses further wherein a numerical aperture of the microscope objective is 0.4 or lower (para 0051 discloses wherein a numerical aperture of the microscope objective is 0.4 or lower).
14. Claim 16 is a corresponding non-transitory computer readable medium claim of claim 5. See the corresponding explanation of claim 5. NPL1 discloses a non-transitory computer-readable storage medium comprising program code portions which, when executed on a device having processing capabilities, performs the method according to claim 5 (NPL1 in fig 1 and pages 30-32 shows and discloses a computational (i.e a computer with instructions stored in the memory to perform the functions) microscopy using deep neural networks meeting the claim limitations).
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAYESH PATEL whose telephone number is (571)270-1227. The examiner can normally be reached IFW Mon-FRI.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached at 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAYESH PATEL/
Primary Examiner
Art Unit 2677
/JAYESH A PATEL/Primary Examiner, Art Unit 2677
Prosecution Insights