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 Amendment
Applicant’s Amendments filed 06/23/2026 has been entered and made of record.
Status of Claims
Currently pending Claim(s):
Amended claim(s):
1-20
1, 11, and 20
Response to Arguments
This office action is responsive to Applicant’s Arguments/Remarks made in an Amendments received on 06/23/2026.
In view of the new claim amendments and applicant arguments, Remarks filed on 06/23/2026, with respect to the 35 U.S.C. 112(b) claim rejections have been carefully considered and the claims rejections to claims 1-20 under 35 U.S.C. 112(b) are withdrawn.
In view of the applicant’s arguments, Remarks filed on 06/23/2026, with respect to the 35 U.S.C. 101 claim rejections have been carefully considered and the claims rejections to claims 1-20 under 35 U.S.C. 101 are withdrawn.
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In view of applicant's argument, Remarks filed on 06/23/2026, with respect to independent claims 1, 11, and 20 under 35 U.S.C. 103, claim rejections have been fully considered but they are not persuasive. The Applicant argues on pages 9-10:
The Examiner respectfully disagrees. Bachelet teaches a sample-informative feature at paragraph [0125] "the term "candidate" refers to a patch which, during the algorithmic processing stages, is suspected to contain a pathogen.” and paragraph [0126] "Given a novel candidate, the algorithm uses the separation model computed in the previous phase and extracts classification features to determine whether the candidate is a target or not.". The extracted classification features include “location in respect to additional biological structures” (paragraph [0078]) and the location of a pathogen “with respect to other elements of the biological sample” (paragraph [0108]). Such information provide contextual information related to the bodily sample in which the candidate is located. As such, the interpretation does not conflate the claimed “sample-informative feature” with candidate-level classification features; as the extracted candidate features, taught by Bachelet, provide information of the candidate in context of the biological sample.
On page 10 of Remarks, the Applicant argues:
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In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “The specification provides numerous examples of sample-informative features, including "number of candidates in the sample," "brightness of the candidates relative to background brightness," "a probability of candidates being pathogens," "number of candidates that have a probability of being a pathogen that exceeds a threshold," "a number of platelets in the sample," "a number of reticulocytes in the sample," and "a number of red blood cells in the sample" (specification, paragraph [0197]”) that are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Further, Bachelet is not simply candidate-level processing and determining whether an individual suspected pathogen is actually a pathogen. Bachelet teaches that the extracted classification features include “location in respect to additional biological structures” (paragraph [0078]) and the location of a pathogen “with respect to other elements of the biological sample” (paragraph [0108]). As explained above, Bachelet considers the candidate in context of the biological sample and provides information related to the bodily sample.
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On page 10-11 of Remarks, the Applicant argues:
The Examiner respectfully disagrees. Bachelet, as explained above, teaches considering the suspected candidate in relation to “location in respect to additional biological structures” (paragraph [0078]) and the location of a pathogen “with respect to other elements of the biological sample” (paragraph [0108]). Further, Bachelet teaches processing the features to trigger the responsive actions at paragraph [0148] "with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.".
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Finally on page 11 of Remarks, the Applicant argues:
The Examiner respectfully disagrees. The pending claim limitation “…and modulating a frame rate at which microscope images are acquired by the microscope system”, refers to the modulating a frame rate at which imaging is acquired and is not limited to body sample specifically. As such, Beecher teaches “modulating a frame rate at which microscope images are acquired by the microscope system” at paragraph [0043] “The imaging management system 112 adjusts a video fame rate based on the bandwidth to control the rate at which images are provided between the requested one of the remotely operable microscope systems 110(1-n) and the one or more of the authorized user computing systems”. Further, Beecher is not limited to imaging a whole specimen, Beecher discloses at paragraph [0035] “the desired specimen, such as a particular pinned insect or a slide with a portion of a particular insect by way of example.”. Thus, Bachelet provides the bodily-sample analysis and Beecher provides the frame-rate modulation. Therefore, the combination of these two references meets the limitation of the pending claims.
Accordingly, the rejection of claims 1-20 under 35 U.S.C. 103 is maintained.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bachelet et al. (US 2012/0169863 A1) (hereinafter, Bachelet) in view of Beecher et al. (US 2009/0040325 A1) (hereinafter, Beecher).
Regarding claim 1, Bachelet discloses apparatus comprising: a microscope system configured to acquire one or more microscope images of a bodily sample (paragraph [0015] “for an automated apparatus capable of inspecting blood donations or blood samples for the presence of parasitic infection…where the apparatus includes components for automated microscopy, and machine-vision processing for performing automatic identification of pathogens.);
an output device (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following:”); and
at least one computer processor (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following:”); configured to:
in the one or more images, identify at least one element as being a candidate of a given entity (paragraph [0017] “provides an apparatus for automatic detection of pathogens within a sample… wherein the processor is adapted to perform image processing using classification algorithms on visual classification features to detect one or more suspected pathogens, when present, in the sample.”; paragraph [0078] “wherein the classification features include one or more of the following: motion, size, shape, coloring, contrast, location in respect to additional biological structures, presence of internal structures, presence of extracellular structures, the aspect ratio, the optical density, florescence at predetermined wavelengths, optical birefringence, clustering behavior, and pattern matching.”),
extract, from the one or more images, at least one candidate-informative feature associated with the candidate (paragraph [0107] “images of known pathogens are saved in a database, and image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”),
extract, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample (paragraph [0125] “the term "candidate" refers to a patch which, during the algorithmic processing stages, is suspected to contain a pathogen.”; paragraph [0126] “Given a novel candidate, the algorithm uses the separation model computed in the previous phase and extracts classification features to determine whether the candidate is a target or not.”),
process the candidate-informative feature in combination with the sample-informative feature (paragraph [0108] “The apparatus then utilizes image analysis software to locate putative appearances of the pathogen in the image. The apparatus compares the characteristics of a suspected pathogen present in the image, to a succinct set of characteristics extracted from images of known pathogens. The characteristics, termed "classification features" herein, may include, but are not limited to, typical motion of live parasites, their typical shape, size, their coloring, their contrast, and their location with respect to other elements of the biological sample (for example, if the pathogen is located within a mammalian cell)”), and
in response thereto, perform an action selected from the group consisting of: [generating an output on the output device] indicating that presence of an infection within the bodily sample could not be reliably determined (Examiner interprets algorithmic decision in Bachelet as degree of reliability. paragraph [0180] “Central to the invention is the use of classification features which are associated with specific pathogens, in order to reach an algorithmic decision whether a pathogen is identified in the sample or not.”),
[generating an output on the output device] indicating that a portion of the sample should be re-imaged (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
[generating an output on the output device] indicating that a portion of the sample should be re-imaged using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope system to re-image a portion of the sample (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope system to re-image a portion of the sample using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”).
However, Bachelet fails to teach generating an output on the output device and modulating a frame rate at which microscope images are acquired by the microscope system.
Beecher teaches generating an output on the output device (paragraph [0022] “The display system in each of the user computing systems 114(1-n) is used to show data and information to the user… other types of data and information could be displayed and other manners of providing the information can be used. The display system comprises a computer display screen, such as a CRT or LCD screen by way of example only, although other types and numbers of displays could be used…”) and
modulating a frame rate at which microscope images are acquired by the microscope system (paragraph [0043] “The imaging management system 112 adjusts a video fame rate based on the bandwidth to control the rate at which images are provided between the requested one of the remotely operable microscope systems 110(1-n) and the one or more of the authorized user computing systems”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Bachelet’s reference to include generating an output on an output device and modulating a frame rate at which microscope images are acquired by the microscope system taught by Beecher’s reference. The motivation for doing so would have been to show the data to the user and to provide multiple views of the specimen as suggested by Beecher (see Beecher paragraph [0019] and paragraph [0043]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more that predictable results. Therefore, it would have been obvious to combine Beecher with Bachelet to obtain the invention specified in claim 1.
Regarding claim 2, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, a number of candidates of the given entity within the sample (paragraph [0031] “at least one processor outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected;”; paragraph [0082] “processing the candidates for finding if more than one candidate belongs to the same target, and where found, candidates of the same target are clustered together;”).
Regarding claim 3, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, a brightness of the candidates relative to a background brightness (paragraph [0215] “The fluorescence image, which roughly overlays the intensity image, is segmented and clustered in a manner analogous to the motion detection above. Instead of looking for patches which move differently from the background, patches whose fluorescence is higher than the background, e.g. patches having high SNR, are sought. High fluorescence can refer to high fluorescence intensity values, or high sum fluorescence, e.g. as integrated over an area.”).
Figure 8:
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Regarding claim 4, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, an indication of a number of candidates that are determined to be the given entity (paragraph [0031] “at least one processor outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected;”; paragraph [0082] “processing the candidates for finding if more than one candidate belongs to the same target, and where found, candidates of the same target are clustered together;”).
Regarding claim 5, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, an indication of probabilities of the candidates being the given entity (paragraph [0080] “calculating the likelihood that the at least one candidate contains a target;”; paragraph [0082] “processing the candidates for finding if more than one candidate belongs to the same target, and where found, candidates of the same target are clustered together;”; paragraph [0083] “tracking at least one candidate, in relation to the at least one cluster, that may belong to the cluster, and where the tracked candidate belongs, adding the tracked candidate to the cluster; determining and classifying the likelihood that the at least one cluster contains a target”).
Regarding claim 6, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, an indication of a number of the candidates that have a probability of being the given entity that exceeds a threshold (paragraph [0080] “calculating the likelihood that the at least one candidate contains a target;”; paragraph [0082] “processing the candidates for finding if more than one candidate belongs to the same target, and where found, candidates of the same target are clustered together;”; paragraph [0083] “tracking at least one candidate, in relation to the at least one cluster, that may belong to the cluster, and where the tracked candidate belongs, adding the tracked candidate to the cluster; determining and classifying the likelihood that the at least one cluster contains a target” paragraph [0211-212] “which it is likely that a pathogen of interest, referred to hereinafter as the "target", appears. One or more of the following methods may be used to detect these candidate patches: a. Pattern matching--if the general form of the target is well defined, a pattern describing this form is constructed in a pre-processing stage: numerous examples of its form are manually collected, and the general pattern is extracted. When processing an image, this pattern is then matched at every location, and those locations which exhibit high similarity to the pattern are taken as candidates”).
Regarding claim 7, which claim 1 is incorporated, Bachelet discloses wherein the at least one computer processor is configured to extract the sample-informative feature from the one or more images (paragraph [0107] “image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”), by extracting, from the one or more images, an indication of a number of the candidates that have a probability of being a given type of the given entity that exceeds a threshold (paragraph [0080] “calculating the likelihood that the at least one candidate contains a target;”; paragraph [0082] “processing the candidates for finding if more than one candidate belongs to the same target, and where found, candidates of the same target are clustered together;”; paragraph [0083] “tracking at least one candidate, in relation to the at least one cluster, that may belong to the cluster, and where the tracked candidate belongs, adding the tracked candidate to the cluster; determining and classifying the likelihood that the at least one cluster contains a target” paragraph [0211-212] “which it is likely that a pathogen of interest, referred to hereinafter as the "target", appears. One or more of the following methods may be used to detect these candidate patches: a. Pattern matching--if the general form of the target is well defined, a pattern describing this form is constructed in a pre-processing stage: numerous examples of its form are manually collected, and the general pattern is extracted. When processing an image, this pattern is then matched at every location, and those locations which exhibit high similarity to the pattern are taken as candidates”).
Regarding claim 8, which claim 1 is incorporated, Bachelet discloses wherein the bodily sample includes a sample that contains blood, and wherein the computer processor is configured to identify elements as being candidates of the given entity by identifying elements as being candidates of the given entity within the blood (paragraph [0046] “the sample is a slide selected from one or more of the following: a blood smear (thin or thick)”; paragraph [0119] “The terms "bodily material", "bodily fluid", "bodily waste product", "tissue" and "sample" are used interchangeably to refer to a material originating in the human or mammalian body, and from which a portion may be readily removed for analysis for the presence of pathogens or for visually apparent changes related to disease progression. Non-limiting examples include: blood…”; paragraph [0247] “module may be used to identify sample elements that are not the pathogens themselves but are useful in determining pathogen presence. For example, red blood cells may be identified in malaria diagnosis in order to determine whether a suspected target is located within a red blood cell.”).
Regarding claim 9, which claim 8 is incorporated, Bachelet discloses wherein the computer processor is configured to identify elements as being candidates of the given entity by identifying elements as being candidates of an entity selected from the group consisting of: a white blood cell (paragraph [0198] “the resultant images can emphasize pathogen markers. For example, when blood is stained with acridine orange, fluorescence images reveal only white blood cells and parasites”), an anomalous white blood cell (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected; the life stage of the pathogen; a finding of anemia; a finding of an unusual white blood cell count; and information on the quality of the sample.”), a red blood cell (paragraph [0247] “module may be used to identify sample elements that are not the pathogens themselves but are useful in determining pathogen presence. For example, red blood cells may be identified in malaria diagnosis in order to determine whether a suspected target is located within a red blood cell”), and a pathogen (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected; the life stage of the pathogen).
Regarding claim 10, which claim 8 is incorporated, Bachelet discloses wherein the computer processor is configured to perform a blood count on the sample that contains blood, and the computer processor is configured to generate the output by outputting the blood count. (paragraph [0031] “apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following:… a finding of an unusual white blood cell count; and information on the quality of the sample.”; paragraph [0267] “software may report medically pertinent information obtained from the biological sample yet unrelated to parasites, (such as detection of anemia, or detection of an unusual white blood-cell count).”).
Regarding claim 11, Bachelet discloses a method comprising: acquiring one or more microscope images of a bodily sample, using a microscope (paragraph [0015] “for an automated apparatus capable of inspecting blood donations or blood samples for the presence of parasitic infection…where the apparatus includes components for automated microscopy, and machine-vision processing for performing automatic identification of pathogens.);
using at least one computer processor (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following:”):
in the one or more images, identifying at least one element as being a candidate of a given entity (paragraph [0017] “provides an apparatus for automatic detection of pathogens within a sample… wherein the processor is adapted to perform image processing using classification algorithms on visual classification features to detect one or more suspected pathogens, when present, in the sample.”; paragraph [0078] “wherein the classification features include one or more of the following: motion, size, shape, coloring, contrast, location in respect to additional biological structures, presence of internal structures, presence of extracellular structures, the aspect ratio, the optical density, florescence at predetermined wavelengths, optical birefringence, clustering behavior, and pattern matching.”);
extracting, from the one or more images, at least one candidate-informative feature associated with the candidate (paragraph [0107] “images of known pathogens are saved in a database, and image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”);
extracting, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample (paragraph [0125] “the term "candidate" refers to a patch which, during the algorithmic processing stages, is suspected to contain a pathogen.”; paragraph [0126] “Given a novel candidate, the algorithm uses the separation model computed in the previous phase and extracts classification features to determine whether the candidate is a target or not.”);
processing the candidate-informative feature in combination with the sample-informative feature (paragraph [0108] “The apparatus then utilizes image analysis software to locate putative appearances of the pathogen in the image. The apparatus compares the characteristics of a suspected pathogen present in the image, to a succinct set of characteristics extracted from images of known pathogens. The characteristics, termed "classification features" herein, may include, but are not limited to, typical motion of live parasites, their typical shape, size, their coloring, their contrast, and their location with respect to other elements of the biological sample (for example, if the pathogen is located within a mammalian cell)”); and
in response thereto, performing an action selected from the group consisting of: [generating an output] indicating that presence of an infection within the bodily sample could not be reliably determined (Examiner interprets algorithmic decision in Bachelet as degree of reliability. paragraph [0180] “Central to the invention is the use of classification features which are associated with specific pathogens, in order to reach an algorithmic decision whether a pathogen is identified in the sample or not.”),
[generating an output] indicating that a portion of the sample should be re-imaged (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
[generating an output] indicating that a portion of the sample should be re-imaged using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope to re-image a portion of the sample (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope to re-image a portion of the sample using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”).
Bachelet fails to teach generating an output and modulating a frame rate at which microscope images are acquired by the microscope.
Beecher teaches generating an output (paragraph [0022] “The display system in each of the user computing systems 114(1-n) is used to show data and information to the user… other types of data and information could be displayed and other manners of providing the information can be used. The display system comprises a computer display screen, such as a CRT or LCD screen by way of example only, although other types and numbers of displays could be used…”) and
modulating a frame rate at which microscope images are acquired by the microscope system (paragraph [0043] “The imaging management system 112 adjusts a video fame rate based on the bandwidth to control the rate at which images are provided between the requested one of the remotely operable microscope systems 110(1-n) and the one or more of the authorized user computing systems”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Bachelet’s reference to include generating an output and modulating a frame rate at which microscope images are acquired by the microscope system taught by Beecher’s reference. The motivation for doing so would have been to show the data to the user and to provide multiple views of the specimen as suggested by Beecher (see Beecher paragraph [0019] and paragraph [0043]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more that predictable results. Therefore, it would have been obvious to combine Beecher with Bachelet to obtain the invention specified in claim 11.
Regarding claim 12 (drawn to a method), claim 12 is rejected the same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to the claim 12, and all the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
Regarding claim 13 (drawn to a method), claim 13 is rejected the same as claim 3 and the arguments similar to that presented above for claim 3 are equally applicable to the claim 13, and all the other limitations similar to claim 3 are not repeated herein, but incorporated by reference.
Regarding claim 14 (drawn to a method), claim 14 is rejected the same as claim 4 and the arguments similar to that presented above for claim 4 are equally applicable to the claim 14, and all the other limitations similar to claim 4 are not repeated herein, but incorporated by reference.
Regarding claim 15 (drawn to a method), claim 15 is rejected the same as claim 5 and the arguments similar to that presented above for claim 5 are equally applicable to the claim 15, and all the other limitations similar to claim 5 are not repeated herein, but incorporated by reference.
Regarding claim 16 (drawn to a method), claim 16 is rejected the same as claim 6 and the arguments similar to that presented above for claim 6 are equally applicable to the claim 16, and all the other limitations similar to claim 6 are not repeated herein, but incorporated by reference.
Regarding claim 17 (drawn to a method), claim 17 is rejected the same as claim 7 and the arguments similar to that presented above for claim 7 are equally applicable to the claim 17, and all the other limitations similar to claim 7 are not repeated herein, but incorporated by reference.
Regarding claim 18 (drawn to a method), claim 18 is rejected the same as claim 8 and the arguments similar to that presented above for claim 8 are equally applicable to the claim 18, and all the other limitations similar to claim 8 are not repeated herein, but incorporated by reference.
Regarding claim 19 (drawn to a method), claim 19 is rejected the same as claim 10 and the arguments similar to that presented above for claim 10 are equally applicable to the claim 19, and all the other limitations similar to claim 10 are not repeated herein, but incorporated by reference.
Regarding claim 20, Bachelet discloses a computer software product, for use with a bodily sample, an output device (paragraph [0031] “The apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following:”) and a microscope system configured to acquire one or more microscope images of a bodily sample (paragraph [0015] “for an automated apparatus capable of inspecting blood donations or blood samples for the presence of parasitic infection…where the apparatus includes components for automated microscopy, and machine-vision processing for performing automatic identification of pathogens.),
the computer software product comprising a non-transitory computer-readable medium in which program instructions are stored (paragraph [0093] provides computer readable storage medium that includes software capable of performing the image processing method of the invention) ,
which instructions, when read by a computer cause the computer to perform the steps of: in the one or more images, identifying at least one element as being a candidate of a given entity (paragraph [0017] “provides an apparatus for automatic detection of pathogens within a sample… wherein the processor is adapted to perform image processing using classification algorithms on visual classification features to detect one or more suspected pathogens, when present, in the sample.”; paragraph [0078] “wherein the classification features include one or more of the following: motion, size, shape, coloring, contrast, location in respect to additional biological structures, presence of internal structures, presence of extracellular structures, the aspect ratio, the optical density, florescence at predetermined wavelengths, optical birefringence, clustering behavior, and pattern matching.”);
extracting, from the one or more images, at least one candidate-informative feature associated with the candidate (paragraph [0107] “images of known pathogens are saved in a database, and image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.”);
extracting, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample (paragraph [0125] “the term "candidate" refers to a patch which, during the algorithmic processing stages, is suspected to contain a pathogen.”; paragraph [0126] “Given a novel candidate, the algorithm uses the separation model computed in the previous phase and extracts classification features to determine whether the candidate is a target or not.”);
processing the candidate-informative feature in combination with the sample-informative feature (paragraph [0108] “The apparatus then utilizes image analysis software to locate putative appearances of the pathogen in the image. The apparatus compares the characteristics of a suspected pathogen present in the image, to a succinct set of characteristics extracted from images of known pathogens. The characteristics, termed "classification features" herein, may include, but are not limited to, typical motion of live parasites, their typical shape, size, their coloring, their contrast, and their location with respect to other elements of the biological sample (for example, if the pathogen is located within a mammalian cell)”); and
in response thereto, performing an action selected from the group consisting of: [generating an output on the output device] indicating that presence of an infection within the bodily sample could not be reliably determined (Examiner interprets algorithmic decision in Bachelet as degree of reliability. paragraph [0180] “Central to the invention is the use of classification features which are associated with specific pathogens, in order to reach an algorithmic decision whether a pathogen is identified in the sample or not.”),
[generating an output on the output device] indicating that a portion of the sample should be re-imaged (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
[generating an output on the output device] indicating that a portion of the sample should be re-imaged using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope system to re-image a portion of the sample (paragraph [0148] “with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
driving the microscope system to re-image a portion of the sample using different settings (paragraph [0148] “controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.”),
However, Bachelet fails to teach generating an output on the output device and modulating a frame rate at which microscope images are acquired by the microscope system.
Beecher teaches generating an output on the output device (paragraph [0022] “The display system in each of the user computing systems 114(1-n) is used to show data and information to the user… other types of data and information could be displayed and other manners of providing the information can be used. The display system comprises a computer display screen, such as a CRT or LCD screen by way of example only, although other types and numbers of displays could be used…”) and
modulating a frame rate at which microscope images are acquired by the microscope system (paragraph [0043] “The imaging management system 112 adjusts a video fame rate based on the bandwidth to control the rate at which images are provided between the requested one of the remotely operable microscope systems 110(1-n) and the one or more of the authorized user computing systems”).
Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date to modify Bachelet’s reference to include generating an output on an output device and modulating a frame rate at which microscope images are acquired by the microscope system taught by Beecher’s reference. The motivation for doing so would have been to show the data to the user and to provide multiple views of the specimen as suggested by Beecher (see Beecher paragraph [0019] and paragraph [0043]).
Further, one skilled in the art could have combined the elements described above by known methods with no change to the respective functions, and the combination would have yielded nothing more that predictable results. Therefore, it would have been obvious to combine Beecher with Bachelet to obtain the invention specified in claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lin et al. (US 2004/0241677 A1) discloses a method for automatically analyzing biological samples with a microscope by determining objects inside a cell and determining the calibration factors of the imaging system.
Kahlman (US 2011/0007178 A1) discloses a device for sensing brightness of a sample from a video and adjusts boundaries of the given area, and uses the measures of brightness for different boundaries, to determine location of edges of the sample
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/UROOJ FATIMA/Examiner, Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
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