DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/06/2025 and 04/14/2025 have been considered by the examiner.
Claim Objections
Claims 8 & 9 are objected to because of the following informalities:
In claim 8, line 3-4, “wherein the at least one processor, upon executing the program code, performs the following steps: obtaining…” should read “wherein the at least one processor, upon executing the program code, performs
In claim 9, lines 4-5, “wherein the processor is configured to load the multiple sets of imaging data from the optical imaging device” should read “wherein the processor is configured to load the multiple sets of imaging data from the optical imaging system .
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 8, and 9 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 1 & 8; recite the limitation, “fusing and processing the multiple sets of imaging data in a machine-learning logic…,” [Lines 4-5 & 7-8, respectively].
Claims 1 & 8; recite the limitation, “obtaining, from the machine-learning logic…,” [Lines 6 & 9, respectively].
Claim 9; recites the limitation, “an optical imaging system configured to acquire the multiple sets of imaging data…,” [Lines 2-3].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1, 8, & 9:
“machine-learning logic” (Fig. 1, Paragraph [0031] - “Various techniques described herein generally relate to virtual staining of a tissue sample by utilizing a trained machine-learning logic (MLL). The MLL can be implemented, e.g., by a support vector machine or a deep neural network which includes at least one encoder branch and at least one decoder branch.” See also Paragraph [0042]. Thus, “machine-learning logic” does have sufficient structure wherein it is a neural network.
“optical imaging system” (Fig. 4, Paragraph [0062] – “FIG. 4 schematically illustrates a system 70 for acquiring imaging data having a digital phase contrast. The system 70 includes a microscope 90 and a computer 80. The microscope 90 includes an illumination module 91, an optical system 92, and a detector module 93.” Thus, “optical imaging system” does have a sufficient structure associated with it wherein it is a microscope.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 10 and 11 fail to further limit the scope of independent claim 8 and dependent claim 9 of which they are dependent on.
Claim 8 states, “wherein the at least one processor, upon executing the program code, performs the following steps: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, fusing and processing the multiple sets of imaging data in a machine-learning logic, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain.” Claim 1 similarly states, “the method comprising: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, fusing and processing the multiple sets of imaging data in a machine-learning logic, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain.”
Independent claim 8 lists the same steps of performing the method as disclosed in claim 1, thus, claim 10 does not narrow the scope of claim 8. Similarly, claim 9 is dependent on claim 8, and thus, claim 11 fails to narrow the scope of claim 9.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 and 5-11 are rejected under 35 U.S.C. 103 as being unpatentable over SPRINGER (US 20230258917 A1), hereinafter referenced as SPRINGER in view of STEWART (US 20210192295 A1), hereinafter referenced as STEWART.
Regarding claim 1, SPRINGER teaches a method of virtually staining a tissue sample (Fig. 6, Paragraph [0081] – SPRINGER discloses FIG. 6 shows process steps for a virtual staining process operated on an unstained input object (tissue specimen).), the method comprising:
obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts (Fig. 6, Paragraph [0081] – SPRINGER discloses a number of M phase-shifted holograms (here for example M=3) of the specimen are taken per N different illumination wavelengths (here for example N=3), wherein the N different illumination wavelengths are applied time-sequentially (see also FIGS. 1-5). This results in a total number of M x N holograms.),
Although SPRINGER further teaches and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain (Fig. 10A, Paragraph [0107] – SPRINGER discloses in virtual staining of a tissue specimen, a digital representation that is equivalent to a chemically stained (labeled) version of the tissue specimen is created. The trained classifier receives as an input for each, pixel, either the QDI value and/or the QPI value (for each colour) and/or the colour image value, or all or a combination of these three, and outputs for each pixel a probability value that the pixel is stained with a certain staining chemical and thereby a virtually stained colour image is received. See also Paragraph [0110].).
SPRINGER fails to explicitly teach fusing and processing the multiple sets of imaging data in a machine-learning logic,
However, STEWART explicitly teaches fusing and processing the multiple sets of imaging data in a machine-learning logic (Fig. 3, Paragraph [0052] – STEWART discloses two or more images, which include at least first and second images that are generated by the interaction of the above photons with a sample, are fused by an image processor. Paragraph ]0053] – STEWART further discloses the mathematical algorithm that is used for image fusion is not limited, and the algorithm includes Image Weighted Bayesian Fusion (IWBF), Partial Least Squares Discriminant Analysis (PLS-DA), linear regression, logistic regression, Support Vector Machines (SVM), Relative Vector Machines (RVM), naïve Bayes, Linear Discriminant Analysis (LDA), and neural networks.),
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 SPRINGER of having a method of virtually staining a tissue sample, the method comprising: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain, with the teachings of STEWART having fusing and processing the multiple sets of imaging data in a machine-learning logic.
Wherein having SPRINGER’s method of virtually staining a tissue sample wherein having fusing and processing the multiple sets of imaging data in a machine-learning logic.
The motivation behind the modification would have been to obtain an enhanced method for virtual staining that reduces variability and provides improved visualization, since both SPRINGER and STEWART relate to computational analysis of tissue specimens, wherein SPRINGER generally pertains to the field of holographic microscopy, in particular to devices, methods and systems for time-sequential partially coherent illumination based holographic microscopy scanners; the variability of digitized objects (for example tissue specimens) is reduced because the application of a chemical dye with different laboratory protocols (e.g. leading to different application time of the chemical dye) is avoided, another advantage is the reduction of the deformation of object (for example a tissue specimen) structure due to the removal of staining, which leads to an improved diagnosis, and STEWART contemplates various embodiments of imaging techniques that combine two or more images generated from samples of interest; the processes 200, 250 described above are beneficial because they provide improved visualization and identification of a target within a sample by combining imaging modalities. Please see SPRINGER (US 20230258917 A1), Paragraph [0062], and STEWART (US 20210192295 A1), Paragraph [0035].
Regarding claim 5, SPRINGER in view of STEWART teach the method of claim 1,
SPRINGER further teaches wherein the multiple different phase contrasts comprise at least two different phase contrasts that have been acquired using the same phase-contrast imaging modality at different imaging settings (Fig. 6, Paragraph [0081] – SPRINGER discloses a number of M phase-shifted holograms (here for example M=3) of the specimen are taken per N different illumination wavelengths (here for example N=3), wherein the N different illumination wavelengths are applied time-sequentially (see also FIGS. 1-5). This results in a total number of M x N holograms.),).
Regarding claim 6, SPRINGER in view of STEWART teach the method of claim 5,
SPRINGER further teaches wherein the at least two different phase contrasts have been acquired using a digital phase-contrast imaging modality at multiple different wavelengths (Fig. 6, Paragraph [0081] – SPRINGER discloses a number of M phase-shifted holograms (here for example M=3) of the specimen are taken per N different illumination wavelengths (here for example N=3), wherein the N different illumination wavelengths are applied time-sequentially (see also FIGS. 1-5). This results in a total number of M x N holograms.),).
Regarding claim 7, SPRINGER in view of STEWART teach the method of claim 5,
SPRINGER further teaches wherein the at least two different phase contrasts have been acquired using a digital phase-contrast imaging modality at multiple different polarizations (Fig. 4, Paragraph [0073] – SPRINGER discloses FIG. 4 shows a digital-in-line holographic microscopy (DIHM) scanner in which a phase-shift between, interference images is realized with a switchable polarizer and a birefringent optical element. In this setup a phase shift between at least two acquisitions of an interference patter of an object is obtained by changing the polarization, direction of the light (for example from p to s direction or from s to p) that is allowed to pass the polarization filter, that means switching the polarizer.).
Regarding claim 8, SPRINGER teaches a computing device comprising at least one processor and a memory (Fig. 14, Paragraph [0125] – SPRINGER discloses electronic device 1500 comprises a CPU 1501 as processor. The electronic device 1500 further comprises a GPU 1506 that is connected to the processor 1501. The electronic device 1500 further comprises a data storage 1502 and a data memory 1503 (here a RAM).),
wherein the at least one processor is configured to load program code from the memory and to execute the program code (Fig. 14, Paragraph [0125] – SPRINGER discloses data memory 1503 is arranged to temporarily store or cache data or computer instructions for processing by the processor 1501. The data storage 1502 and the data memory 1503 may comprise computing instructions that implement the processes described above, e.g. a process of recording and storing a scanned hologram of an object. The computing instructions may further implement the process of training a classification algorithm and the performing virtual staining operations as described above. ),
wherein the at least one processor (Fig. 14, #1501 called processor, Paragraph [0125]), upon executing the program code, performs the following steps:
obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts (Fig. 6, Paragraph [0081] – SPRINGER discloses a number of M phase-shifted holograms (here for example M=3) of the specimen are taken per N different illumination wavelengths (here for example N=3), wherein the N different illumination wavelengths are applied time-sequentially (see also FIGS. 1-5). This results in a total number of M x N holograms.),
Although SPRINGER further teaches and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain (Fig. 10A, Paragraph [0107] – SPRINGER discloses in virtual staining of a tissue specimen, a digital representation that is equivalent to a chemically stained (labeled) version of the tissue specimen is created. The trained classifier receives as an input for each, pixel, either the QDI value and/or the QPI value (for each colour) and/or the colour image value, or all or a combination of these three, and outputs for each pixel a probability value that the pixel is stained with a certain staining chemical and thereby a virtually stained colour image is received. See also Paragraph [0110].).
SPRINGER fails to explicitly teach fusing and processing the multiple sets of imaging data in a machine-learning logic,
However, STEWART explicitly teaches fusing and processing the multiple sets of imaging data in a machine-learning logic (Fig. 3, Paragraph [0052] – STEWART discloses two or more images, which include at least first and second images that are generated by the interaction of the above photons with a sample, are fused by an image processor. Paragraph ]0053] – STEWART further discloses the mathematical algorithm that is used for image fusion is not limited, and the algorithm includes Image Weighted Bayesian Fusion (IWBF), Partial Least Squares Discriminant Analysis (PLS-DA), linear regression, logistic regression, Support Vector Machines (SVM), Relative Vector Machines (RVM), naïve Bayes, Linear Discriminant Analysis (LDA), and neural networks.),
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 SPRINGER of having a computing device comprising at least one processor and a memory, wherein the at least one processor is configured to load program code from the memory and to execute the program code, wherein the at least one processor, upon executing the program code, performs the following steps: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain, with the teachings of STEWART having fusing and processing the multiple sets of imaging data in a machine-learning logic.
Wherein having SPRINGER’s computing device wherein having fusing and processing the multiple sets of imaging data in a machine-learning logic.
The motivation behind the modification would have been to obtain an enhanced method for virtual staining that reduces variability and provides improved visualization, since both SPRINGER and STEWART relate to computational analysis of tissue specimens, wherein SPRINGER generally pertains to the field of holographic microscopy, in particular to devices, methods and systems for time-sequential partially coherent illumination based holographic microscopy scanners; the variability of digitized objects (for example tissue specimens) is reduced because the application of a chemical dye with different laboratory protocols (e.g. leading to different application time of the chemical dye) is avoided, another advantage is the reduction of the deformation of object (for example a tissue specimen) structure due to the removal of staining, which leads to an improved diagnosis, and STEWART contemplates various embodiments of imaging techniques that combine two or more images generated from samples of interest; the processes 200, 250 described above are beneficial because they provide improved visualization and identification of a target within a sample by combining imaging modalities. Please see SPRINGER (US 20230258917 A1), Paragraph [0062], and STEWART (US 20210192295 A1), Paragraph [0035].
Regarding claim 9, SPRINGER in view of STEWART teach the computing device of claim 8,
SPRINGER further teaches wherein the computing device is coupled with an optical imaging system configured to acquire the multiple sets of imaging data (Fig. 14, Paragraph [0125] – SPRINGER discloses electronic device 1500 further comprises a GPU 1506 that is connected to the processor 1501. The electronic system 1500 further comprises an Ethernet interface 1504 which acts as interface for data communication with external devices, as for example a DIHM scanner. The DHIM scanner can also be connected to the electronic device with other standard connection buses, like USB.),
and wherein the processor is configured to load the multiple sets of imaging data from the optical imaging device (Fig. 14, Paragraph [0148] – SPRINGER discloses an electronic device (1500) comprising circuitry configured to acquiring with at least three different illumination light wavelengths respectively one or more phase-shifted holograms of an object (12) at an image sensor (14).).
Regarding claim 10, SPRINGER in view of STEWART teach the computing device of claim 8,
SPRINGER further teaches wherein the at least one processor is configured to execute the method of claim 1 (Fig. 14, Paragraph [0125] – SPRINGER discloses electronic device 1500 comprises a CPU 1501 as processor. The computing instructions may further implement the functionality of calculating a colour image, QPIs and a QDI of the object. The computing instructions may further implement the process of training a classification algorithm and the performing virtual staining operations as described above. See also Paragraphs [0081, 0107, 0110].).
Regarding claim 11, SPRINGER in view of STEWART teach the computing device of claim 9,
SPRINGER further teaches wherein the at least one processor is configured to execute the method of claim 1 (Fig. 14, Paragraph [0148] – SPRINGER discloses an electronic device (1500) comprising circuitry configured to acquiring with at least three different illumination light wavelengths respectively one or more phase-shifted holograms of an object (12) at an image sensor (14). See also Paragraphs [0081, 0107, 0110].)
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over SPRINGER (US 20230258917 A1), hereinafter referenced as SPRINGER in view of STEWART (US 20210192295 A1), hereinafter referenced as STEWART in further view of CHEN (US 20230359010 A1), hereinafter referenced as CHEN.
Regarding claim 2, SPRINGER in view of STEWART teach the method of claim 1,
SPRINGER in view of STEWART fail to explicitly teach wherein the multiple different phase contrasts comprise at least two different phase contrasts that have been acquired using multiple different phase-contrast imaging modalities.
However, CHEN explicitly teach wherein the multiple different phase contrasts comprise at least two different phase contrasts that have been acquired using multiple different phase-contrast imaging modalities (Fig. 4, Paragraph [0003] – CHEN discloses a variety of microscopic imaging methods such as transport of intensity equation (TIE), differential phase contrast (DPC), and Fourier psychographic microscopy (FPM) can be implemented. See also Paragraph [0025].).
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 SPRINGER in view of STEWART of having a method of virtually staining a tissue sample, the method comprising: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, fusing and processing the multiple sets of imaging data in a machine-learning logic, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain, with the teachings of CHEN having wherein the multiple different phase contrasts comprise at least two different phase contrasts that have been acquired using multiple different phase-contrast imaging modalities.
Wherein having SPRINGER’s method of virtually staining a tissue sample wherein the multiple different phase contrasts comprise at least two different phase contrasts that have been acquired using multiple different phase-contrast imaging modalities.
The motivation behind the modification would have been to obtain an enhanced method for virtual staining that reduces variability and provides diverse multi-contrast observation, since both SPRINGER and CHEN relate to condensers that provide illumination for phase-contrast observation, wherein SPRINGER generally pertains to the field of holographic microscopy, in particular to devices, methods and systems for time-sequential partially coherent illumination based holographic microscopy scanners; the variability of digitized objects (for example tissue specimens) is reduced because the application of a chemical dye with different laboratory protocols (e.g. leading to different application time of the chemical dye) is avoided, another advantage is the reduction of the deformation of object (for example a tissue specimen) structure due to the removal of staining, which leads to an improved diagnosis, and CHEN relates in particular to a miniaturized, low-cost, multi-contrast label-free microscopic imaging system; the entire system uses low-cost hardware to obtain high-quality, diverse observations and enable quantitative phase measurements. Please see SPRINGER (US 20230258917 A1), Paragraph [0062], and CHEN (US 20230359010 A1), Paragraph [0008].
Regarding claim 3, SPRINGER and STEWART in view of CHEN teach the method of claim 2,
SPRINGER and STEWART fail to explicitly teach wherein the multiple different phase-contrast imaging modalities comprise a first digital phase-contrast imaging modality and a second digital phase-contrast imaging modality, wherein the first digital phase-contrast imaging modality is a transport of intensity equation digital phase-contrast imaging modality, and wherein the second digital phase-contrast imaging modality is a differential digital phase-contrast imaging modality.
However, CHEN explicitly teaches wherein the multiple different phase-contrast imaging modalities comprise a first digital phase-contrast imaging modality and a second digital phase-contrast imaging modality (Fig. 4, Paragraph [0003] – CHEN discloses a variety of microscopic imaging methods such as transport of intensity equation (TIE), differential phase contrast (DPC), and Fourier psychographic microscopy (FPM) can be implemented. See also Paragraph [0025].),
wherein the first digital phase-contrast imaging modality is a transport of intensity equation digital phase-contrast imaging modality (Fig. 4, Paragraph [0003] – CHEN discloses a variety of microscopic imaging methods such as transport of intensity equation (TIE), differential phase contrast (DPC), and Fourier psychographic microscopy (FPM) can be implemented. See also Paragraph [0025].),
and wherein the second digital phase-contrast imaging modality is a differential digital phase-contrast imaging modality (Fig. 4, Paragraph [0003] – CHEN discloses a variety of microscopic imaging methods such as transport of intensity equation (TIE), differential phase contrast (DPC), and Fourier psychographic microscopy (FPM) can be implemented. See also Paragraph [0025].).
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 SPRINGER in view of STEWART of having a method of virtually staining a tissue sample, the method comprising: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, fusing and processing the multiple sets of imaging data in a machine-learning logic, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain, with the teachings of CHEN having wherein the multiple different phase-contrast imaging modalities comprise a first digital phase-contrast imaging modality and a second digital phase-contrast imaging modality, wherein the first digital phase-contrast imaging modality is a transport of intensity equation digital phase-contrast imaging modality, and wherein the second digital phase-contrast imaging modality is a differential digital phase-contrast imaging modality.
Wherein having SPRINGER’s method of virtually staining a tissue sample wherein the multiple different phase-contrast imaging modalities comprise a first digital phase-contrast imaging modality and a second digital phase-contrast imaging modality, wherein the first digital phase-contrast imaging modality is a transport of intensity equation digital phase-contrast imaging modality, and wherein the second digital phase-contrast imaging modality is a differential digital phase-contrast imaging modality.
The motivation behind the modification would have been to obtain an enhanced method for virtual staining that reduces variability and provides diverse multi-contrast observation, since both SPRINGER and CHEN relate to condensers that provide illumination for phase-contrast observation, wherein SPRINGER generally pertains to the field of holographic microscopy, in particular to devices, methods and systems for time-sequential partially coherent illumination based holographic microscopy scanners; the variability of digitized objects (for example tissue specimens) is reduced because the application of a chemical dye with different laboratory protocols (e.g. leading to different application time of the chemical dye) is avoided, another advantage is the reduction of the deformation of object (for example a tissue specimen) structure due to the removal of staining, which leads to an improved diagnosis, and CHEN relates in particular to a miniaturized, low-cost, multi-contrast label-free microscopic imaging system; the entire system uses low-cost hardware to obtain high-quality, diverse observations and enable quantitative phase measurements. Please see SPRINGER (US 20230258917 A1), Paragraph [0062], and CHEN (US 20230359010 A1), Paragraph [0008].
Regarding claim 4, SPRINGER and STEWART in view of CHEN teach the method of claim 2,
Although SPRINGER further teaches wherein the multiple different phase-contrast imaging modalities comprise at least one of an inline holography phase contrast (Fig. 1, Paragraph [0064] – SPRINGER discloses FIG. 1 schematically shows a digital in-line holographic microscopy (DIHM) scanner in which a phase-shift between interference images is realized by changing the object-to-sensor distance.),
or a phase-shifting interferometry phase contrast (Fig. 2, Paragraph [0067] – SPRINGER discloses FIG. 2-schematically shows a digital in-line holographic microscopy (DIHM) scanner in which a phase-shift between interference images is realized with a tuneable phase-shifter.).
SPRINGER and STEWART fail to explicitly teach a shearing interferometry phase contrast,
However, CHEN explicitly teaches a shearing interferometry phase contrast (Fig. 4, Paragraph [0008] – CHEN discloses six microscopic imaging modes in a fixed system configuration, including bright field, dark field, rainbow dark field, DPC [wherein DPC is a shearing interferometry], Rheinberg optical staining, and QPI, which enables diverse multi-contrast observation.),
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 SPRINGER in view of STEWART of having a method of virtually staining a tissue sample, the method comprising: obtaining multiple sets of imaging data depicting a tissue sample, the multiple sets of imaging data having multiple different phase contrasts, fusing and processing the multiple sets of imaging data in a machine-learning logic, and obtaining, from the machine-learning logic, at least one output image, each one of the at least one output image depicting the tissue sample comprising a respective virtual stain, with the teachings of CHEN having a shearing interferometry phase contrast.
Wherein having SPRINGER’s method of virtually staining a tissue sample wherein the multiple different phase-contrast imaging modalities comprise at least one of a Zernike phase contrast, a Jamin-Lebedeff interference phase contrast, a shearing interferometry phase contrast, an inline holography phase contrast, an off- axis holography phase contrast, or a phase-shifting interferometry phase contrast.
The motivation behind the modification would have been to obtain an enhanced method for virtual staining that reduces variability and provides diverse multi-contrast observation, since both SPRINGER and CHEN relate to condensers that provide illumination for phase-contrast observation, wherein SPRINGER generally pertains to the field of holographic microscopy, in particular to devices, methods and systems for time-sequential partially coherent illumination based holographic microscopy scanners; the variability of digitized objects (for example tissue specimens) is reduced because the application of a chemical dye with different laboratory protocols (e.g. leading to different application time of the chemical dye) is avoided, another advantage is the reduction of the deformation of object (for example a tissue specimen) structure due to the removal of staining, which leads to an improved diagnosis, and CHEN relates in particular to a miniaturized, low-cost, multi-contrast label-free microscopic imaging system; the entire system uses low-cost hardware to obtain high-quality, diverse observations and enable quantitative phase measurements. Please see SPRINGER (US 20230258917 A1), Paragraph [0062], and CHEN (US 20230359010 A1), Paragraph [0008].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
OZCAN et al. (US 20250259462 A1) - A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.… Fig. 1, Abstract.
ZUO et al. (US 20220011563 A1) - The invention claims a microscopic imaging method of phase contrast (PC) and differential interference contrast (DIC) based on the transport of intensity equation (TIE). Firstly, three intensity images are captured along the optical axis; secondly, TIE is solved by deconvolution to obtain the quantitative phase; then, the intensity image under the DIC imaging mode is obtained according to the DIC imaging principle; finally, the corresponding phase image of PC imaging mode is obtained according to the PC imaging principle. The proposed approach can endow the bright-field microscope with the ability to realize PC and DIC imaging without complex modification of the traditional bright-field microscope. In other words, this method only needs to use the traditional bright-field microscope without adding any complex hardware.… Fig. 1, Abstract.
SHI et al. (US 20160063694 A1) - A method of tomographic imaging the real part of the refractive index of the internal structure of an image volume and a corresponding apparatus for performing the method. The method includes a two-step process of first retrieving phase projections of an image volume from projective intensity measurements through the image volume. The second step of the method includes iterative image reconstruction of an image of the image volume using phase projections taken at multiple projection angles. The first step of intensity measurements and subsequent phase retrieval can use one of many possible phase retrieval methods including the Bronnikov phase retrieval method. … Fig. 1, Abstract.
DAS et al. (US 20150049860 A1) - A system and method for single step and motionless x-ray phase contrast imaging. In one embodiment, a system for single step x-ray imaging includes a clinical x-ray source, a first coded aperture mask, a second coded aperture mask, and a photon counting spectral detector. The first coded aperture mask is disposed between the x-ray source and an object to be imaged. The photon counting spectral detector is disposed to detect x-rays passing through the object. The second coded aperture mask is disposed between the object to be imaged and the photon counting spectral detector. The system can provide, from a single acquisition step, an absorption image, a phase image, and differential phase image.… Fig. 1, Abstract.
LARKIN et al. (US 20140153692 A1) - A method forms a representative image from a crossed grating fringe pattern interferogram of an object from an interferometer. The method determines a plurality of spectral lobes from the interferogram and selects from the plurality of determined lobes, two substantially orthogonal sidelobes. The selected sidelobes represent spatial differential phase information of the interferogram. The method applies an inverse Riesz transform to the spatial differential phase information of the selected sidelobes to form a transformed differential phase image, and forms from the transformed differential phase image, a representative image emphasising high frequency detail information of the object..… Fig. 1, Abstract.
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/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
