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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-8 and 13-23 are rejected under 35 U.S.C. 103 as being unpatentable over Dodle et al. (U.S. Patent Application Publication 2025/0037280) in view of Ba et al. (U.S. Patent Application Publication 2024/0320562) in view of Kang et al. (U.S. Patent Application Publication 2023/0005155).
Regarding claim 1, Dodle et al. discloses a method comprising: interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a stain (paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0024] – specimen 112 on slide 116 may be stained; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom); based on interrogating the fluid sample, modifying a focal setting of an objective lens of the microscopy analyzer (paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); in response to modifying the focal setting of the objective lens, capturing one or more images of the fluid sample from an imaging sensor of the microscopy analyzer (paragraph [0020] – referring to Fig. 1A, imaging device 100 may include one or more sensors for capturing image signals representative of an image of a scene (e.g., a scene including specimen 112) – sensors may provide for representing and/or converting a captured image signal of a scene to digital data; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); inputting the one or more images into one or more machine learning models (paragraph [0088] – machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes – a “machine learning process,” as used in this disclosure, is a process that automatically uses training data 204 to generate an algorithm instantiated in hardware or software logic, data structures, and/or functions that will be performed by a computing device/module to produce outputs 208 given data provided as inputs 212; paragraph [0097] – still referring to Fig. 2, machine-learning module 200 may be configured to perform a lazy-learning process 220 and/or protocol, which may be alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand; paragraphs [0098]-[0100] – machine learning processes); identifying, via the one or more machine learning models, one or more characteristics of the fluid sample in the one or more images (paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like); and transmitting instructions that cause a graphical user interface to display a graphical indication of the identified characteristic (paragraph [0041] – output interface may include one or more elements through which imaging device 100 may communicate information to a user - output interface may include a display – a display may output images, videos, and the like to the user; paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like). However, Dodle et al. fails to explicitly disclose interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation; based on interrogating the fluid sample, modifying a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Ba et al. reference, Ba et al. discloses a method, comprising: interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation (paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had the fluid sample comprise a biological sample and a stain configured to react in an aqueous solution as disclosed by Ba et al. in the method disclosed by Dodle et al. in order to enhance visualization, differentiate cell types, and help diagnose infections by increasing contrast, revealing morphology, and enabling the identification of specific microorganisms and their components. Staining also makes it possible to see the size, shape, and arrangement of cells and other structures that would otherwise be invisible or difficult to distinguish against a plain background. However, Dodle et al. in view of Ba et al. fails to explicitly disclose based on interrogating the fluid sample, modifying a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Kang et al. reference, Kang et al. discloses a method comprising: based on interrogating the fluid sample, modifying a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample (paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had modified a focal setting of an objective lens of the microscopy analyzer based on an association with the thickness of the fluid sample as disclosed by Kang et al. in the method disclosed by Dodle et al. in view of Ba et al. in order to compensate for the additional height of the fluid.
Regarding claim 2, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the slide comprises a glass slide (Dodle et al.: paragraph [0016] – digital image acquisition of pathology glass slides; paragraph [0024] – slide 116 may be substantially transparent – slide 116 may include a glass slide; Ba et al.: paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components).
Regarding claim 3, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1, but fails to disclose wherein the slide comprises a plastic slide. Official Notice is taken that both the concept and advantages of the slide being comprised of a plastic slide is well-known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had used a plastic slide in the method disclosed by Dodle et al. in view of Ba et al. in view of Kang et al. in order to provide a shatterproof and lightweight slide.
Regarding claim 4, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1, but fails to explicitly disclose wherein the slide comprises a thickness less than 1.3mm. Official Notice is taken that both the concept and advantages of the slide having a thickness less than 1.3mm is well-known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had used a standard slide in the method disclosed by Dodle et al. in view of Ba et al. in view of Kang et al. in order to provide a lightweight slide. Standard microscope slides are typically about 1mm thick.
Regarding claim 5, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the slide comprises a cavity, wherein the cavity is at least partially enclosed within the slide (Dodle et al.: paragraph [0024] – specimen 112 may be disposed on a slide 116 – a “slide” is a container or surface for holding a specimen (the container indicates a cavity) – in some embodiments, a cover, such as a transparent cover, may be applied to slide 116 such that specimen 112 is disposed between slide 116 and cover).
Regarding claim 6, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claims 1 and 5 including that wherein the cavity is at least partially enclosed within the microscopic slide (Dodle et al.: paragraph [0024] – specimen 112 may be disposed on a slide 116 – a “slide” is a container or surface for holding a specimen (the container indicates a cavity) – in some embodiments, a cover, such as a transparent cover, may be applied to slide 116 such that specimen 112 is disposed between slide 116 and cover). However, Dodle et al. in view of Ba et al. in view of Kang et al. fails to explicitly disclose wherein the slide comprises a thickness less than 1.3mm. Official Notice is taken that both the concept and advantages of the slide having a thickness less than 1.3mm is well-known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had used a standard slide in the method disclosed by Dodle et al. in view of Ba et al. in view of Kang et al. in order to provide a lightweight slide. Standard microscope slides are typically about 1mm thick.
Regarding claim 7, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the biological sample comprises one or more of blood, urine, saliva, earwax, sperm, body cavity fluids, and/or fine needle aspirates (Dodle et al.: paragraph [0023] – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; Ba et al.: [0064] – biological samples – blood, serum, urine, semen, fecal matter, sweat, saliva, etc.).
Regarding claim 8, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the stain comprises methylene blue (Ba et al.: paragraph [0081] – methylene blue).
Regarding claim 13, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein modifying the focal setting of the objective lens of the microscopy analyzer comprises adjusting a focal setting of the objective lens from a focal setting associated with a dry sample to a focal setting associated with the fluid sample (Dodle et al.: paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom; Ba et al.: paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples; Kang et al.: paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed).
Regarding claim 14, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the one or more machine learning models comprises one or more of the following: (i) an artificial neural network, (ii) a support vector machine, (iii) a regression tree, or (iv) an ensemble of regression trees (Dodle et al.: paragraph [0091] – further referring to Fig. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below, such models may include without limitation a training data classifier 216 - training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like - machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204 - classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers; paragraph [0098] – a machine-learning model 224 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes; paragraph [0111] – referring now to Fig. 4, an exemplary embodiment of neural network 400 is illustrated – a neural network 400 also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs).
Regarding claim 15, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the method further comprises, prior to inputting the one or more images into the one or more machine learning models, training the one or more machine learning models with one or more training images that share the characteristic with the one or more images (Dodle et al.: paragraph [0081] – the neural network may be trained on a set of training data, and a back propagation algorithm may be used to modify the data in the array; paragraph [0089] – still referring to Fig. 2, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements – for instance, and without limitation, training data 204 may include a plurality of data entries, also known as “training examples,” each entry representing a set of data elements that were recorded, received, and/or generated together, data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like).
Regarding claim 16, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claims 1 and 15 including that wherein training the one or more machine learning models comprises, based on inputting the one or more training images into the machine learning model: (i) predicting, by the one or more machine learning models, an outcome of a determined condition of the one or more training images; (ii) comparing the at least one outcome to the characteristic of the one or more training images; and (iii) adjusting, based on the comparison, the machine learning model (Dodle et al.: paragraph [0081] – the neural network may be trained on a set of training data, and a back propagation algorithm may be used to modify the data in the array; paragraph [0089] – still referring to Fig. 2, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements – for instance, and without limitation, training data 204 may include a plurality of data entries, also known as “training examples,” each entry representing a set of data elements that were recorded, received, and/or generated together, data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like; paragraph [0106] – continuing to refer to Fig. 2, any process of training, retraining, deployment, and/or instantiation of any machine-learning model and/or algorithm may be performed and/or repeated after an initial deployment and/or instantiation to correct, refine, and/or improve the machine-learning model and/or algorithm - alternatively or additionally, retraining, deployment, and/or instantiation may be event-based, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and/or by automated field testing and/or auditing processes, which may compare outputs of machine-learning models and/or algorithms, and/or errors and/or error functions thereof, to any thresholds, convergence tests, or the like, and/or may compare outputs of processes described herein to similar thresholds, convergence tests or the like).
Regarding claim 17, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claims 1 and 15 including that wherein training the one or more machine learning models comprises one or more of supervised learning, semi-supervised learning, reinforcement learning, or unsupervised learning (Dodle et al.: paragraph [0081] – the neural network may be trained on a set of training data, and a back propagation algorithm may be used to modify the data in the array; paragraph [0089] – still referring to Fig. 2, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements – for instance, and without limitation, training data 204 may include a plurality of data entries, also known as “training examples,” each entry representing a set of data elements that were recorded, received, and/or generated together, data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like; paragraph [0091] – further referring to Fig. 2, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models).
Regarding claim 18, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the method further comprises: determining, via the one or more machine learning models, an image enhancement for the one or more images; applying, based on the determined image enhancement, the image enhancement to the one or more images; and outputting, via the graphical user interface, the one or more enhanced images (Dodle et al.: paragraph [0054] – image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques; paragraph [0059] – processing images may include enhancing at least a region of interest via a plurality of image processing techniques to improve the quality (or degree of quality of depiction) of an image for better processing and analysis; paragraph [0060] – image processing module may be configured to perform a contrast enhancement operation on an image – contrast enhancement operation may improve the contrast of an image by stretching the intensity range of the image and/or redistributing the intensity values (i.e., degree of brightness or darkness of a pixel in the image)).
Regarding claim 19, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claims 1 and 18 including that wherein applying the image enhancement to the one or more images comprises applying one or more of the following to the one or more images: (i) a saturation enhancement; (ii) a brightness enhancement; (iii) a contrast enhancement; and (iv) a focal setting enhancement (Dodle et al.: paragraph [0054] – image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques; paragraph [0059] – processing images may include enhancing at least a region of interest via a plurality of image processing techniques to improve the quality (or degree of quality of depiction) of an image for better processing and analysis; paragraph [0060] – image processing module may be configured to perform a contrast enhancement operation on an image – contrast enhancement operation may improve the contrast of an image by stretching the intensity range of the image and/or redistributing the intensity values (i.e., degree of brightness or darkness of a pixel in the image)).
Regarding claim 20, Dodle et al. discloses a non-transitory, computer-readable medium having instructions stored thereon, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform a set of operations comprising: interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a stain (paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0024] – specimen 112 on slide 116 may be stained; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom); modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer (paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); in response to modifying the focal setting of the objective lens, capturing one or more images of the fluid sample from an imaging sensor of the microscopy analyzer (paragraph [0020] – referring to Fig. 1A, imaging device 100 may include one or more sensors for capturing image signals representative of an image of a scene (e.g., a scene including specimen 112) – sensors may provide for representing and/or converting a captured image signal of a scene to digital data; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); inputting the one or more images into one or more machine learning models (paragraph [0088] – machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes – a “machine learning process,” as used in this disclosure, is a process that automatically uses training data 204 to generate an algorithm instantiated in hardware or software logic, data structures, and/or functions that will be performed by a computing device/module to produce outputs 208 given data provided as inputs 212; paragraph [0097] – still referring to Fig. 2, machine-learning module 200 may be configured to perform a lazy-learning process 220 and/or protocol, which may be alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand; paragraphs [0098]-[0100] – machine learning processes); identifying, via the one or more machine learning models, a characteristic of the fluid sample in the one or more images (paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like); and transmitting instructions that cause a graphical user interface to display a graphical indication of the identified characteristic (paragraph [0041] – output interface may include one or more elements through which imaging device 100 may communicate information to a user - output interface may include a display – a display may output images, videos, and the like to the user; paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like). However, Dodle et al. fails to explicitly disclose interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation; modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Ba et al. reference, Ba et al. discloses a method, comprising: interrogating a fluid sample disposed on a slide of a microscopy analyzer, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation (paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had the fluid sample comprise a biological sample and a stain configured to react in an aqueous solution as disclosed by Ba et al. in the method disclosed by Dodle et al. in order to enhance visualization, differentiate cell types, and help diagnose infections by increasing contrast, revealing morphology, and enabling the identification of specific microorganisms and their components. Staining also makes it possible to see the size, shape, and arrangement of cells and other structures that would otherwise be invisible or difficult to distinguish against a plain background. However, Dodle et al. in view of Ba et al. fails to explicitly disclose modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Kang et al. reference, Kang et al. discloses a method comprising: modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample (paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had modified a focal setting of an objective lens of the microscopy analyzer based on an association with the thickness of the fluid sample as disclosed by Kang et al. in the method disclosed by Dodle et al. in view of Ba et al. in order to compensate for the additional height of the fluid.
Regarding claim 21, Dodle et al. discloses a microscopy device comprising: an objective lens (paragraph [0044] – objective lens); a slide (paragraph [0024] - Fig. 1A – slide 116); an imaging sensor (paragraph [0020] – Fig. 1A – imaging device 100 may include one or more sensors for capturing image signals representative of an image of scene (e.g., a scene including specimen 112) – for instance, a sensor may include an image sensor; paragraph [0027] – image sensor); and a non-transitory computer-readable medium, having stored thereon program instructions that, when executed by a processor, cause the processor to perform a set of operations, the set of operations comprising: interrogating a fluid sample disposed on the slide, wherein the fluid sample comprises a stain (paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0024] – specimen 112 on slide 116 may be stained; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom); modifying, based on interrogating the fluid sample, a focal setting of the objective lens (paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); in response to modifying the focal setting of the objective lens, capturing one or more images of the fluid sample from the imaging sensor (paragraph [0020] – referring to Fig. 1A, imaging device 100 may include one or more sensors for capturing image signals representative of an image of a scene (e.g., a scene including specimen 112) – sensors may provide for representing and/or converting a captured image signal of a scene to digital data; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom); inputting the one or more images into one or more machine learning models (paragraph [0088] – machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes – a “machine learning process,” as used in this disclosure, is a process that automatically uses training data 204 to generate an algorithm instantiated in hardware or software logic, data structures, and/or functions that will be performed by a computing device/module to produce outputs 208 given data provided as inputs 212; paragraph [0097] – still referring to Fig. 2, machine-learning module 200 may be configured to perform a lazy-learning process 220 and/or protocol, which may be alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand; paragraphs [0098]-[0100] – machine learning processes); identifying, via the one or more machine learning models, a characteristic of the fluid sample in the one or more images (paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like); and transmitting instructions that cause a graphical user interface to display a graphical indication of the identified characteristic (paragraph [0041] – output interface may include one or more elements through which imaging device 100 may communicate information to a user - output interface may include a display – a display may output images, videos, and the like to the user; paragraph [0091] – training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and/or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith – a classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric, or the like). However, Dodle et al. fails to explicitly disclose interrogating a fluid sample disposed on the slide, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation; modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Ba et al. reference, Ba et al. discloses a microscopy device comprising: interrogating a fluid sample disposed on the slide, wherein the fluid sample comprises a biological sample and a stain configured to react in an aqueous solution, and wherein the fluid sample is disposed on the slide to preserve components of the biological sample of the fluid sample during interrogation (paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had the fluid sample comprise a biological sample and a stain configured to react in an aqueous solution as disclosed by Ba et al. in the device disclosed by Dodle et al. in order to enhance visualization, differentiate cell types, and help diagnose infections by increasing contrast, revealing morphology, and enabling the identification of specific microorganisms and their components. Staining also makes it possible to see the size, shape, and arrangement of cells and other structures that would otherwise be invisible or difficult to distinguish against a plain background. However, Dodle et al. in view of Ba et al. fails to explicitly disclose modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample.
Referring to the Kang et al. reference, Kang et al. discloses a microscopy device comprising: modifying, based on interrogating the fluid sample, a focal setting of an objective lens of the microscopy analyzer associated with a thickness of the fluid sample (paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had modified a focal setting of an objective lens of the microscopy analyzer based on an association with the thickness of the fluid sample as disclosed by Kang et al. in the device disclosed by Dodle et al. in view of Ba et al. in order to compensate for the additional height of the fluid.
Regarding claim 22, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the fluid sample deposited on the slide comprises a wet mount fluid sample (Dodle et al.: paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom; Ba et al.: paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples; Kang et al.: paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed; the wet sample mount is a sample suspended in a drop of liquid).
Regarding claim 23, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the thickness of the fluid sample is greater than a thickness of a dry sample (Dodle et al.: paragraph [0023] – with continued reference to Fig. 1A, in some embodiments, imaging device 100 may be used to generate an image of a specimen 112 – a “specimen” is a sample of organic material used for testing or observation purposes – specimen may include a pathology sample – for instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual; paragraph [0044] – now referring to Fig. 1A, imaging device 100 may receive one or more parameters from an operator of imaging device 100 – one or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”) – as used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and/or numerical values, that identify how an image is to be captured – in other embodiments, a trained machine-learning model, such as the machine-learning model described in Fig. 2, may be used to determine first location x and second location x’ – for example, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 112) – parameter set may include a desired focus depth and level of zoom – a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system – for instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight – a level of zoom may include optical zoom and/or digital zoom – as a non-limiting example, a level of zoom may be “8x” zoom – parameter set may include a desired focus depth – in some cases, setting a level of zoom may include changing one or more optical elements of optical system 120 – for example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification – additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom; Ba et al.: paragraph [0003] – digital pathology involves scanning of slides (e.g., histopathology or cytopathology glass slides) into digital images interpretable on a computer screen – in order to examine the tissue and/or cells (which are virtually transparent) within the digital images, the pathology slides may be prepared using various stain assays (e.g., immunohistochemistry) that bind selectively to tissue and/or cellular components; paragraph [0057] – different staining techniques – H&E staining usually preserves tissue morphology well; paragraph [0064] – biological samples; Kang et al.: paragraph [0055] – generally, even though the image analysis is performed on a high magnification image to determine a position of a lens for an optimal focus, not only an image analyzing method, but also a method of measuring a tissue specimen slide height (position) by a laser sensor is available - according to the method of estimating a slide height by a laser sensor, a precision is lower than that of a lens depth of focus – further, even though the height of the slide is identified, which height (position) in the thickness of the specimen on the slide where the cell is located at is not known – accordingly, it has an advantage of enabling a laser sensor which was difficult to be biologically used in the related art to be applicable to a lens height adjusting method – when the laser sensor is used, as compared with the method of determining a focal height position by means of the image analysis, the focal position may be quickly determined, so that it is possible to increase the capturing speed; the wet sample, which is a sample with fluid on it, will have a greater thickness than a dry sample, which is a sample that can be as thin as a smear).
Claims 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Dodle et al. in view of Ba et al. in view of Kang et al. as applied to claim 1 above, and further in view of Tsuchisaka (JP2009128292A).
Regarding claims 9-12, Dodle et al. in view of Ba et al. in view of Kang et al. discloses all of the limitations as previously discussed with respect to claim 1, but fails to disclose wherein the objective lens comprises a magnification of at least one of approximately 10x, 20x, and/or 40x; wherein the objective lens comprises a numerical aperture of approximately 0.40; wherein the objective lens comprises a cover glass thickness of approximately 0.17 mm; and wherein the objective lens comprises an infinity correction objective lens.
Referring to the Tsuchisaka reference, Tsuchisaka discloses a method, comprising: wherein the objective lens comprises a magnification of at least one of approximately 10x, 20x, and/or 40x (paragraphs [0053] and [0054] – objective lens 10x and 20x); wherein the objective lens comprises a numerical aperture of approximately 0.40 (paragraph [0021] – it is desirable that the said objective lens is 0.3-0.5 numerical aperture of infinity design – if the numerical aperture is too small, the depth of focus becomes deep and it is difficult to detect the in-focus position with high accuracy – on the other hand, if the numerical aperture is too large, the working distance (WD) becomes small due to the design of the objective lens and is thick – in addition to hindering the measurement of thick lenses, it is also difficult to correct aberrations; paragraph [0046]); wherein the objective lens comprises a cover glass thickness of approximately 0.17 mm (paragraph [0050] – the most common method for observing an object image by placing a parallel glass plate in a working distance between the tip of the objective lens 2 and the object position is a cover glass used for fixing a biological specimen – at this time, the objective lens 2 is a designed by correcting the thickness (for example, 0.17mm) of the cover glass); and wherein the objective lens comprises an infinity correction objective lens (paragraph [0023] – the infinity corrected objective lens 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had the objective lens comprise a magnification of at least one of approximately 10x, 20x, and/or 40x; a numerical aperture of approximately 0.40; a cover glass thickness of approximately 0.17 mm; and an infinity correction objective lens as disclosed by Tsuchisaka in the method disclosed by Dodle et al. in view of Ba et al. in view of Kang et al. in order to create a microscope with a higher-power observation of smaller, more intricate structures, and a good balance of resolution and depth of field.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/HEATHER R JONES/Primary Examiner, Art Unit 2481
May 29, 2026
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