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 statement (IDS) submitted on 03/05/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are 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.
Regarding the independent claims 1, 15 and 16, the claims all recite the term “suspicious cell”, the term suspicious makes the claim unclear what the meets and bounds of the claim are. Could a suspicious cell be a cell that is potentially cancerous or a normal cell? It is unclear to the examiner how one can determine suspicion in the context of the claim. Perhaps amending the claim to determine abnormal cells that aren’t health cells might overcome the current rejection.
The remaining claims are rejected by virtue of dependency on the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-9, 11-12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over McDevitt et al. US PG-Pub(US 20210215703 A1) in view of Jamme et al. ("Deep UV auto fluorescence microscopy for cell biology and tissue histology").
Regarding Claim 1, McDevitt teaches a method for assessing a presence of cancerous or suspicious regions(¶[0009] discloses determining cell areas associated with oral disease.), comprising: using a brush to obtain a sample from a patient’s oral cavity or cervix(¶[0056], “A biological sample of a subject is obtained and prepared for analysis. The sample may be any suitable cytological sample. For example, in certain embodiments, the sample is a suspension of cells collected with a brush, such as a rotating brush. The sample may be obtained from a lesion or suspected lesion in the oral cavity to assess the risk or presence of oral cancer, PMOL, and/or OED.”, ¶[0056] discloses using a brush to obtain a sample of a patients oral cavity.); spreading the sample to a microscope slide and transporting the slide to a microscope (¶[0131] discloses that the sample acquired from the brush is examined by a microscope to determine lesions in the sample);receiving and processing, at a processor, signals associated with the one or more images((¶[0059], “The optical sensing means is configured to receive a signal from cells within the assay chamber, and the microfluidics are configured so as to allow fluid movement to and from the assay chamber. The processor and user interface control the system and the processor records data from said optical sensing means”, ¶[0059] discloses receiving a signal from the processor to image the cell.) to: determine one or more geometrical characteristics of one or more cells of the sample(¶[0085], “inputting the following data points into a computer: one or more morphological characteristics from individual oral cells from a patient, said morphological characteristics selected from nuclear area, cell area, cell circularity, cell aspect ratio, and cell roundness.” ¶[0085[ discloses determining characteristics of the cell in the sample by inputting the data into a computer.), compare the one or more geometrical characteristics to one or more references (¶[0113] discloses using a neural network to compare characteristics from the cell image from a predicted value.), and based on comparison of the one or more geometrical characteristics to the one or more references, identify the one or more cells as one of a benign, cancerous or suspicious cell.(¶[0086], “calculating a risk score based on each of the above inputs, said risk score allowing a user to distinguish at least the following: i) benign lesions, ii) dysplastic lesions, iii) cancerous lesions, and iv) potentially malignant lesions. In one embodiment, the method comprises displaying said risk score on an output device.”, ¶[0086] discloses calculating a score for the sample and determining if there is cancerous or benign cells.)
McDevitt does not explicitly teach utilizing a deep ultraviolet (UV) light source, having illumination wavelengths smaller than 300 nm, to obtain one or more images of the sample by the microscope;
Jamme teaches utilizing a deep ultraviolet (UV) light source, having illumination wavelengths smaller than 300 nm, to obtain one or more images of the sample by the microscope; (Page 281, Applications to cell and tissue, Isolated living cells, paragraph 1, “To assess the distribution of endogenous fluorophorein single cell, the whole living cell was scanned under 275 nm excitation with 1 s exposure time and the flu-orescence emission spectra of the cell were recordedat each pixel. Figure 6 represents the spectra of auto-fluorescence obtained from the highlighted pixel (ingrey)”, in this section of the prior art deep UV microscopy is used to illuminate cells under a microscope and the wavelength was set to 275nm which is below 300nm.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by McDevitt with Jamme in order to incorporate deep ultraviolet microscopy when obtaining images of a sample. One skilled in the art would have been motivated to modify McDevitt in this manner in order to demonstrate that DUV autofluorescence is a powerful tool for tissue histology and cell biology. (Jamme, Page 277, Conclusions.)
Regarding Claim 2, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein the one or more geometrical characteristics include a cell diameter and nucleus diameter(¶[0098] discloses “morphological measurements, including but not limited to nuclear area, cell area, nuclear to cytoplasm ratio distribution” which cell and nucleus diameter would inherently be included in the measurement data.), and the one or more cells are identified as the benign, cancerous or suspicious cell based on comparison of a ratio of the nucleus diameter to the cell diameter to one or more reference ratios indicative of a benign, cancerous or suspicious cell. ([0144] “A cell phenotype classification model was explored for its ability to discriminate and quantitate the frequency and distributions of four cell phenotypes: Type 1: cells presenting as polygonal in shape with a low nuclear-cytoplasmic ratio (NC ratio) which represent mature squamous epithelial cells; Type 2: cells presenting as small round cells representing immature parabasal cells; Type 3: cells presenting as mononuclear leukocytes; Type 4: cells represented by lone (naked) nuclei without cell membrane and cytoplasm. To recognize these cell types, a machine learning algorithm was trained on 144 cellular/nuclear features from single-cell analyses, including morphological and intensity-based measurements.”, ¶[0144] discloses using a machine learning model to compare ratios to determine if the cell is a cancerous or benign cell type.)
Regarding Claim 3, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein the one or more geometrical characteristics include a cell size and nucleus size (¶[0044] The word “morphometric” as used herein means the measurement of such cellular shape or morphological characteristics as cell shape, size, nuclear to cytoplasm ratio, membrane to volume ratio, and the like.), and the one or more cells are identified as the benign, cancerous or suspicious cell based on comparison of a ratio of the nucleus size to the cell size to one or more reference ratios indicative of a benign, cancerous or suspicious cell. (¶[0072], “The present invention also includes the detection and identification of the cellular phenotype of cells within the sample. For example, the presence and relative amount of mature squamous cells, presence or absence of nuclear actin in mature squamous cells, small round cells, leukocytes, and/or lone nuclei in a sample are determined to assess oral disease status in a sample of interest. In certain embodiments, the various cellular phenotypes are identified using complex object recognition routines as defined by machine learning methods. For example, in one embodiment, a user (e.g., a cytology expert) initially selects the cell types of interest. Then, various unsupervised learning routines are exploited. In doing so, the learning cell-level visual representation can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. The cell recognition procedures use various parameters, including, but not limited to, morphological parameters, protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype.”, ¶[0072] discloses using the cell size and nucleus size to determine cell types in the sample..)
Regarding Claim 4, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein the one or more geometrical characteristics include a nucleus shape(¶[0044] The word “morphometric” as used herein means the measurement of such cellular shape or morphological characteristics as cell shape, size, nuclear to cytoplasm ratio, membrane to volume ratio, and the like.),, and the one or more cells are identified as the benign, cancerous or suspicious cell based on comparison of a cell shape to one or more a reference shapes indicative of a benign, cancerous or suspicious cell. (¶[0072], “The present invention also includes the detection and identification of the cellular phenotype of cells within the sample. For example, the presence and relative amount of mature squamous cells, presence or absence of nuclear actin in mature squamous cells, small round cells, leukocytes, and/or lone nuclei in a sample are determined to assess oral disease status in a sample of interest. In certain embodiments, the various cellular phenotypes are identified using complex object recognition routines as defined by machine learning methods. For example, in one embodiment, a user (e.g., a cytology expert) initially selects the cell types of interest. Then, various unsupervised learning routines are exploited. In doing so, the learning cell-level visual representation can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. The cell recognition procedures use various parameters, including, but not limited to, morphological parameters, protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype.”, ¶[0072] discloses using the cell shapes to determine cell types in the sample..)
Regarding Claim 5, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein the one or more geometrical characteristics include one or more irregularity measures associated with the one or more cells or one or more nuclei of the one or more cells(¶0072] discloses morphological parameters being measured of the cell ), and the one or more cells are identified as the benign, cancerous or suspicious cell based on comparison of the one or more irregularity measures to one or more reference irregularity measures indicative of a benign, cancerous or suspicious cell.(¶[0072]-¶[0074] disclose cell recognition based on morphological parameters such as protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype)
Regarding Claim 8, the combination of the McDevitt, Jamme and Nie teach the method of claim 7, where McDevitt further teaches comprising using the density of the one or more regions to determine a structural or compositional change associated with the one or more cells indicative of a progression of a malignancy associated with the one or more cells.(¶[0072], The cell recognition procedures use various parameters, including, but not limited to, morphological parameters, protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype.
[0073] In certain embodiments, the percentage of cells of a particular cellular phenotype is used to diagnose, assess the risk of developing, and/or assess the progression of oral cancer, potentially malignant oral lesions (PMOL), and/or oral epithelial dysplasia (OED).¶[0072]-¶[0073] disclose using the cell density to determine which percentage of cells could be malignant oral lesions.)
Regarding Claim 9, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein identification of the one or more cells as one of a benign, cancerous or suspicious cell is determined based on a combination of the following characteristics obtained from the one or more images: a shape of the one or more cells, a size of a nucleus of the one or more cells, a relative size of the one or more cells with respect to a nucleus of the one or more cells, an irregularity measure associated with the one or more cells or a nucleus of the one or more cells, or a density of a nucleus of the one or more cells. (¶[0072] of the prior art discloses determining cell type by using morphological features such as protein expression, nucleation size, shape, and intensity parameters.)
Regarding Claim 11, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein processing the signals associated with the one or more images includes using a neural network engine to classify one or more regions of the cell. (¶[0111], “Aspects of the invention relate to a machine learning algorithm, machine learning engine, or neural network. A neural network may be trained based on various attributes of one or more cells, examples of which are disclosed herein, and may output one or more predictive values based on the attributes. The resulting predictive values may then be judged according their success rate in matching one or more binary classifiers or quality metrics for known input values, and the weights of the attributes may be optimized to maximize the average success rate for binary classifiers or quality metrics. In this manner, a neural network can be trained to predict and optimize for any binary classifier or quality metric that can be experimentally measured. Examples of binary classifiers or quality metrics that a neural network can be trained on are discussed herein, including cancer severity, effectiveness of cancer treatment, or cancer diagnosis.”, ¶[0111] discloses using a neural network trained to determine cancer within the sample obtained.)
Regarding Claim 12, the combination of McDevitt and Jamme teach the method of claim 11, where McDevitt further teaches wherein the neural network engine comprises a deep learning module configured to process the one or more images, or portions thereof, to conduct a classification to distinguish between non-dysplasia, dysplasia, and cancer classifications based at least in part on one or more of a shape, a size, a nucleocytoplasmic ratio, or an intensity variation associated with the one or more cells or corresponding nuclei. (¶[0072], “The present invention also includes the detection and identification of the cellular phenotype of cells within the sample. For example, the presence and relative amount of mature squamous cells, presence or absence of nuclear actin in mature squamous cells, small round cells, leukocytes, and/or lone nuclei in a sample are determined to assess oral disease status in a sample of interest. In certain embodiments, the various cellular phenotypes are identified using complex object recognition routines as defined by machine learning methods. For example, in one embodiment, a user (e.g., a cytology expert) initially selects the cell types of interest. Then, various unsupervised learning routines are exploited. In doing so, the learning cell-level visual representation can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. The cell recognition procedures use various parameters, including, but not limited to, morphological parameters, protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype.”, ¶[0072] discloses using the cell shapes to determine cell types in the sample.)
Regarding Claim 14, the combination of McDevitt and Jamme teach the method of claim 1, where McDevitt further teaches wherein the one or more images are obtained from the sample that is not stained or treated with a liquid. (¶[0056], “A biological sample of a subject is obtained and prepared for analysis. The sample may be any suitable cytological sample. For example, in certain embodiments, the sample is a suspension of cells collected with a brush, such as a rotating brush. The sample may be obtained from a lesion or suspected lesion in the oral cavity to assess the risk or presence of oral cancer, PMOL, and/or OED.”, ¶[0056] discloses using a brush to obtain a sample of a patients oral cavity but does not mention staining the sample.)
Regarding Claim 15, claim 15 is considered an method claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, McDevitt teaches a detect one or more cells in the one or more images, and classify the one or more cells based on cellular features of the one or more cells using the one or more images. (¶[0072], “The present invention also includes the detection and identification of the cellular phenotype of cells within the sample. For example, the presence and relative amount of mature squamous cells, presence or absence of nuclear actin in mature squamous cells, small round cells, leukocytes, and/or lone nuclei in a sample are determined to assess oral disease status in a sample of interest. In certain embodiments, the various cellular phenotypes are identified using complex object recognition routines as defined by machine learning methods. For example, in one embodiment, a user (e.g., a cytology expert) initially selects the cell types of interest. Then, various unsupervised learning routines are exploited. In doing so, the learning cell-level visual representation can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. The cell recognition procedures use various parameters, including, but not limited to, morphological parameters, protein expression, nucleation size, shape, and intensity parameters, to recognize and identify a cell as being of a particular cellular phenotype.”, ¶[0072] discloses using the cell size and nucleus size to determine cell types in the sample.)
Regarding Claim 16, claim 15 is considered an method claim substantially corresponding to claim 1. Please see the discussion of claim 1 above for a discussion of similar limitations. Furthermore, McDevitt teaches a system for assessing a presence of cancerous or suspicious regions(See ¶[0059]), comprising: a microscope (See ¶[0137]) comprising: and a processor and a memory including instructions stored thereon, the processor coupled to the microscope to receive signals associated with one or more images of the sample obtained by the UV image sensor(See ¶[0059]),
Regarding Claim 17, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 18, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 19, it is substantially similar to claim 12 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 20, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over McDevitt et al. US PG-Pub(US 20210215703 A1) in view of Jamme et al. ("Deep UV auto fluorescence microscopy for cell biology and tissue histology") in view of Nie et al. US PG-Pub(US 20240161485 A1).
Regarding Claim 6, while the combination of McDevitt and Jamme teach the method of claim 1, they do not explicitly teach wherein processing the signals associated with the one or more images further includes determining one or more intensity values associated with one or more regions of the one or more cells, and using the one or more intensity values for identification of the one or more cells as one of the benign, cancerous or suspicious cell.
Nie teaches wherein processing the signals associated with the one or more images further includes determining one or more intensity values associated with one or more regions of the one or more cells (¶[0156] discloses using the intensity value a region of the image to classify cells.) and using the one or more intensity values for identification of the one or more cells as one of the benign, cancerous or suspicious cell. (¶[0156], “automatically analyzing spectral and/or shape features of the identified nuclei in an image for identifying nuclei of non-tumor cells. For example, blobs may be identified in the first digital image in a first step. A “blob” as used herein can be, for example, a region of a digital image in which some properties, e.g. the intensity or grey value, are constant or vary within a prescribed range of values”, ¶[0156] discloses using the intensity values in the image to classify tumor cells in the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by McDevitt and Jamme with Nie in order to determine intensity values in the sample. One skilled in the art would have been motivated to modify McDevitt and Jamme in this manner in order to classify cells within an unlabeled image. (Nie, Abstract)
Regarding Claim 7, the combination of the McDevitt, Jamme and Nie teach the method of claim 6, where Nie further teaches comprising using the one or more intensity values to determine an opacity level, a transparency level, or a density of the one or more regions. ([0165] “In some embodiments, spatial features include a local density of cells; average distance between two adjacent detected cells; and/or distance from a cell to a segmented region.”, ¶[0165] discloses density of the cells is determined based on intensity values.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by McDevitt and Jamme with Nie in order to determine intensity values in the sample. One skilled in the art would have been motivated to modify McDevitt and Jamme in this manner in order to classify cells within an unlabeled image. (Nie, Abstract)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over McDevitt et al. US PG-Pub(US 20210215703 A1) in view of Jamme et al. ("Deep UV auto fluorescence microscopy for cell biology and tissue histology") in view of Jo et al. US PG-Pub(US 20260083534 A1).
Regarding Claim 10, while the combination of McDevitt and Jamme teach the method of claim 1, they do not explicitly teach wherein the one or more images are black-and-white images.
Jo teaches wherein the one or more images are black-and-white images. (¶[0028], “a black-and-white camera including a color filter, the communicator may be further configured to, in response to the projector projecting near ultraviolet (NUV) light by using an NUV light source, receive, from the scanner, image data of the NUV light reflected from the object, the image data being obtained by the first camera and the second camera, and the processor may be further configured to generate, from the image data of the NUV light reflected from the object, the three-dimensional oral model in which an area in a certain wavelength band is identified.”, ¶[0028] discloses using a black and white camera to capture images to generate an oral model of the patient).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by McDevitt and Jamme with Jo in order to use black and white images. One skilled in the art would have been motivated to modify McDevitt and Jamme in this manner in order to determine a certain wavelength band is identified in the image. (Jo, ¶[0033])
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over McDevitt et al. US PG-Pub(US 20210215703 A1) in view of Jamme et al. ("Deep UV auto fluorescence microscopy for cell biology and tissue histology") in view of Moiso et al. US PG-Pub(US 20220415438 A1).
Regarding Claim 13, while the combination of McDevitt and Jamme teach the method of claim 12, they do not explicitly teach wherein the neural network engine uses a multilayer perceptron network to conduct the classification.
Moiso teaches wherein the neural network engine uses a multilayer perceptron network to conduct the classification. (¶[0090], “It was described above that the machine learning classifier 114 may be capable of outputting a tumor type of an input tumor based on gene expression data for the input tumor. The machine learning classifier 114 may be implemented as a multilayer perceptron (MLP).”, using a MLP to perform image classification.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by McDevitt and Jamme with Moiso in order to use a MLP to perform image classification. One skilled in the art would have been motivated to modify McDevitt and Jamme in this manner in order to apply the machine learning perceptron classifier to the gene expression data for the input tumor to generate the tumor type of the input tumor. (Moiso, ¶[0006])
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAN D HOANG whose telephone number is (571)272-4344. The examiner can normally be reached Monday-Friday 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN M VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAN HOANG/Primary Examiner, Art Unit 2661
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