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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-6, 10-15, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) &(a)(1) as being anticipated by applicant cited Chen et al. (U.S. Publication No. 2011/0102542, hereinafter Chen).
With respect to Claim 1, Chen discloses [see fig 1 unless otherwise noted] a method of determining a viewpoint for optically inspecting a sample within a sample container, comprising:
employing a sensor [imager 14] to capture image data of a sample container [12] including a sample, wherein a portion of the sample container includes a label [barcode; fig 2, para 21] having a first end and a second end [barcodes known to have first and second ends];
rotating a sample container [12] about a central axis of the sample container so that the sensor captures image data that includes at least the first end of the label and the second end of the label [fig 1 and para 21 show rotated 360 deg to capture image of entire surface of the container];
employing the captured image data to generate an unwrapped image of the sample container [para 21; multiple images stitched into one unwrapped panorama of container 14. Fig 5A-5S show individual images that generate unwrapped image 5T];
processing the unwrapped image to characterize the label and produce label characterization information [fig 7A and para 39 and 40 shows characterizing the label as window C and window E is characterized as a region of interest that is not the label]; and
employing the label characterization information to identify a viewpoint through which to optically inspect the sample within the sample container [fig 7A and para 39 and 40; window E is the identified viewpoint though which to optically inspect the sample].
With respect to Claim 2, Chen discloses that employing a sensor to capture image data includes capturing a plurality of images of the sample container at different viewpoints relative to the sample container [figs 5A-5S show different viewpoints of images of the sample container].
With respect to Claim 4, Chen discloses that rotating the sample container includes rotating the sample container 360 degrees relative to the sensor. See para 21.
With respect to Claim 5, Chen discloses that employing the captured image data to generate an unwrapped image of the label comprises stitching together a plurality of images of the sample container, each image depicting a different portion of the sample container [para 21 shows stitching the images together; fig 5A-S show individual images showing different portions of the sample container used to generate fig 5T which shows the entire unwrapped image, including the label].
With respect to Claim 6, Chen discloses that stitching together a plurality of images comprises: obtaining an image window from each of the plurality of images, each image window including only a portion of the pixels of its respective image; and stitching together the image windows from the plurality of images. This is shown when figs 5A-S are stitched together, the duplicative portions are clipped to generate fig 5T. Para 26 mentions imaging coefficeints, which is how panoramas make image windows that only include a portion of the pixels, after clipping duplicative portions, which are then combined into the final image.
With respect to Claim 10, Chen discloses that further comprising inspecting the sample using the identified viewpoint. See fig 7a, the viewpoint that includes window E is used inspect the level of the sample, see para 40.
With respect to Claim 11, Chen discloses further comprising characterizing the sample based on the inspection of the sample using the identified viewpoint [para 40 further characterizes the sample into bubble portions and non-bubble portions].
With respect to Claim 12, Chen discloses an apparatus adapted to determine a viewpoint for optically inspecting a sample within a sample container [12], comprising:
a sensor [imager 14] configured to capture image data of a sample container [12] including a sample, wherein a portion of the sample container includes a label [barcode; fig 2, para 21] having a first end and a second end [barcodes have first and second ends];
a rotation mechanism [robotic handler; para 19] configured to rotate the sample container [12] a central axis of the sample container so that the sensor captures image data that includes at least the first end of the label and the second end of the label [para 21; captures 360 deg]; and
a computer [16] coupled to the sensor [14] and the rotation mechanism [18], the computer including computer program code that, when executed by the computer, causes the computer to:
direct the rotation mechanism to rotate at least one of the sample container and the sensor about the central axis of the sample container so that the sensor captures image data that includes at least the first end of the label and the second end of the label [para 21];
employ the captured image data to generate an unwrapped image of the sample container [see para 26, fig 5A-S are images stitched together to make unwrapped image 5T];
process the unwrapped image to characterize the label and produce label characterization information [fig 7A and para 39 and 40 shows characterizing the label as window C and window E is characterized as a region of interest that is not the label]; and
employ the label characterization information to identify a viewpoint through which to optically inspect the sample within the sample container [fig 7A and para 39 and 40; window E is the identified viewpoint though which to optically inspect the sample].
With respect to Claim 13, Chen discloses that the computer includes computer program code that causes the sensor to capture a plurality of images of the sample container at different viewpoints relative to the sample container [figs 5A-S show different captured viewpoints].
With respect to Claim 14, Chen discloses that the computer includes computer program code that generates an unwrapped image by stitching together a plurality of images of the sample container, each image depicting a different portion of the sample container [see para 26, fig 5A-S are images stitched together to make unwrapped image 5T].
With respect to Claim 15, Chen discloses that the computer includes computer program code that generates an unwrapped image by: obtaining an image window from each of the plurality of images, each image window including only a portion of the pixels of its respective image; and stitching together the image windows from the plurality of images. This is shown when figs 5A-S are stitched together, the duplicative portions are clipped to generate fig 5T. Para 26 mentions imaging coefficeints, which is how panoramas make image windows that only include a portion of the pixels, after clipping duplicative portions, which are then combined into the final image.
With respect to Claim 19, Chen discloses that the computer includes computer program code that employs the sensor [14] to inspect the sample using the identified viewpoint. See fig 7a, the viewpoint that includes window E is used inspect the level of the sample, see para 40.
With respect to Claim 20, Chen discloses that the computer includes computer program code that characterizes the sample based on the inspection of the sample using the identified viewpoint [para 40 further characterizes the sample into bubble portions and non-bubble portions].
Claim Rejections - 35 USC § 103
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 3 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of applicant cited Miller (U.S. Publication No. 2015/0241457, hereinafter Miller).
With respect to Claim 3, Chen does not disclose further comprising employing two or more of the plurality of images to determine one or more of height, width, and diameter of the sample container.
Miller discloses a similar method of taking images of a sample container that uses two or more of the plurality of images to determine the height and width of the sample container.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add employing two or more of the plurality of images to determine the height and width of the sample container to combine with Chen’s fluid level determination to calculate the volume of fluid in the sample container.
With respect to Claim 21, Chen discloses a diagnostic analysis system comprising: a sample container [12] including a sample, wherein a portion of the sample container includes a label [barcode; fig 2, para 21] having a first end and a second end; a sensor [14] configured to capture image data of the sample container; a rotation mechanism [18] configured to rotate at least one of the sample container and the sensor about a central axis of the sample container so that the sensor captures image data that includes at least the first end of the label and the second end of the label [para 21; rotates 360 deg]; and a computer [16] coupled to the sensor [14] and the rotation mechanism [18], the computer including computer program code that, when executed by the computer, causes the computer to: direct the rotation mechanism to rotate at least one of the sample container and the sensor about the central axis of the sample container so that the sensor captures image data that includes at least the first end of the label and the second end of the label [para 21; rotates 360 deg which captures the entire surface, including the entire barcode] ; employ the captured image data to generate an unwrapped image of the label [see para 26, fig 5A-S are images stitched together to make unwrapped image 5T]; process the unwrapped image to characterize the label and produce label characterization information [fig 7A and para 39 and 40 shows characterizing the label as window C and window E is characterized as a region of interest that is not the label]; and employ the label characterization information to identify a viewpoint through which to optically inspect the sample within the sample container [fig 7A and para 39 and 40; window E is the identified viewpoint though which to optically inspect the sample].
Chen does not disclose a track, or that the sample container is on a carrier that is moveable on the track.
Miller shows a similar system what uses a track [para 37; track 130]
a track and a carrier [125; fig 1a and 1d] moveable on the track that contains sample container [120].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add a track and a carrier moveable on the track that contains sample container to Chen for the benefit of being able to easily move sample containers.
5. Claims 7, 8, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Kluckner et al. (U.S. Patent No. 10,824,832, hereinafter Kluckner).
With respect to Claim 7, Chen discloses processing the unwrapped image to characterize the label [para 39 and 40] by generating a segmentation mask [although the term segmentation mask isn’t used this is a pixel level classification technique. Since Chen classifies pixels in the unwrapped image as either “label” in window C or “non-label” as everything else, Chen reads on a segmentation mask].
Chen does not disclose processing the unwrapped image through a trained segmentation network.
Kluckner discloses a similar system that uses multiple images of a sample container [see fig 1] that segments the image to determine the location of the barcode label using a trained network. See column 3, line 47-column 4, line 2.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use a trained network to improve Chen’s segmentation abilities.
With respect to Claim 8, Chen discloses that the segmentation mask includes a plurality of pixels, wherein each pixel is identified as either a label pixel or a non-label pixel. See fig 7a, pixels in Window C are label pixels, everything else is a non-label pixel.
With respect to Claim 16, Chen discloses that the computer includes computer program code that processes the unwrapped image to characterize the label [para 39 and 40] using a segmentation mask [although the term segmentation mask isn’t used this is a pixel level classification technique. Since Chen classifies pixels in the unwrapped image as either “label” in window C or “non-label” as everything else, Chen reads on a segmentation mask].
Chen does not disclose processing the unwrapped image through a trained segmentation network.
Kluckner discloses a similar system that uses multiple images of a sample container [see fig 1] that segments the image to determine the location of the barcode label using a trained network. See column 3, line 47-column 4, line 2.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use a trained network to improve Chen’s segmentation abilities.
With respect to Claim 17, Chen discloses that the segmentation mask includes a plurality of pixels, wherein each pixel is identified as either a label pixel or a non-label pixel. See fig 7a, pixels in Window C are label pixels, everything else is a non-label pixel.
Allowable Subject Matter
Claims 9 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Chen does not disclose employing the label characterization information to identify a viewpoint comprises employing the segmentation mask to identify a viewpoint by: determining a center of a gap between the first and second ends of the label; and determining how far to rotate the sample container to align the center of the gap between the first and second ends of the label and a center of view of the sensor. Chen takes a series of images while the sample container is being rotated and cannot identify the barcode label until the container is fully rotated.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX T DEVITO whose telephone number is (571)270-7551. The examiner can normally be reached 12pm- 8 pm EST M-S.
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/ALEX T DEVITO/Examiner, Art Unit 2855
/SUMAN K NATH/Primary Examiner, Art Unit 2855
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