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 § 112
The following is a quotation of 35 U.S.C. 112(b):
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-11, 13-16 and 29 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 1, the phrase “the identified sponge type of the surgical sponge that was counted out” is indefinite. The claim does not previously recite counting out. It is recommended to further define counted out.
Claims 2-11, 13-16 and 29 depend from claim 1.
Regarding claim 11, “the depth map” lacks antecedent basis. Claim 10 recites “depth data,” but does not recite a depth map.
Regarding claims 13 and 16, “the image feed” lacks antecedent basis because claim 1 does not recite an image feed. Claim 2 does recite an image feed.
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 1, 10-11, 17 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Satish (20170186160) in view of Jackson (20130088354).
Regarding claim 1, Satish teaches a method of assessing bloodied surgical sponges with a processor, display, depth imaging system and color imaging system, including detecting a sponge presented in a field of view, capturing an image and estimating blood or a blood component from the image (pars. 17-21, 29-30 and 64).
Satish teaches detecting a presented surgical sponge in the field of view of the imaging system (pars. 18 and 20).
Satish teaches determining whether the presented sponge satisfies acceptance criteria, including distance, depth-plane, physical-dimension and sponge-type criteria, and rejecting a presentation that fails a criterion (pars. 38, 40-46). Jackson teaches storing the identifiers and types of sponges that have been discarded, i.e., counted out, and identifying a discarded sponge by its unique identifier (pars. 35-38 and 42-44).
Satish teaches automatically capturing the sponge image after the presentation satisfies the imaging criteria (pars. 20-21 and 38-46).
Satish teaches estimating a volume of blood or a blood component from color values of the captured image (pars. 21 and 64).
Satish teaches displaying the estimated blood or blood-component volume on the user interface (pars. 30 and 66).
Jackson teaches detecting a tag of a surgical sponge with an RFID reader and receiving the unique identifier stored by the tag (pars. 13-15 and 42-44).
Jackson teaches identifying the sponge type from a database of predetermined asset types based on the unique identifier (pars. 42-44).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Satish the RFID identification, sponge database and counted out record taught by Jackson. The reason is to associate the image-derived blood estimate and sponge counter with the particular discarded sponge, improving sponge accountability and reducing duplicate or mismatched measurements.
Regarding claim 10, Satish teaches generating a sponge mask, obtaining depth data, converting pixel dimensions into real-world dimensions, comparing the real-world dimensions with ranges for a selected sponge type, rejecting a sponge outside the range and preventing capture (pars. 44-45 and 59-62).
Regarding claim 11, Satish teaches fitting a virtual plane to pixels of a depth map, determining each pixel’s distance from the plane and rejecting pixels outside a threshold (par. 40).
Regarding claim 17, Satish teaches assessing bloodied surgical sponges with a processor, user interface, depth sensor and optical sensor (pars. 17-21 and 29-30).
Satish teaches capturing a color image of the surgical sponge (pars. 20-21).
Satish teaches estimating blood or a blood component on the sponge from color-component values and a sponge-type determination (pars. 21, 44-46 and 64).
Satish teaches displaying the blood or blood-component volume (pars. 30 and 66). The reason to combine Satish and Jackson is stated above.
Jackson teaches detecting a tag with a data reader and receiving the unique identifier stored on the tag (pars. 13-15 and 42-44).
Jackson teaches identifying the surgical sponge as counted out by recording and later reading the unique identifiers of discarded sponges (pars. 35-38).
Jackson teaches identifying the sponge type from a database of predetermined types based on the unique identifier (pars. 42-44).
Regarding claim 29, Satish teaches activating the depth and color imaging systems when the imaging process is triggered (par. 29). Jackson teaches that reading a sponge tag identifies the sponge and begins the discard-record workflow (pars. 35-38 and 42-44).
Claims 2, 4-9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Satish (20170186160) in view of Jackson (20130088354) in further view of Roach (20130155474).
Roach teaches displaying a presentation window over a live camera feed, sizing the window according to stored dimensions for the selected object type, providing alignment feedback and automatically capturing an image after the object satisfies pre-capture quality criteria (pars. 7, 70, 92, 126-132 and 148).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Satish and Jackson the presentation window and automatic quality-gated capture taught by Roach. The reason is to give the user immediate alignment feedback.
Regarding claim 4, Roach teaches locating an object with a bounding border, determining whether all four sides are inside the presentation boundary and testing whether the detected sides and corners satisfy expected rectangular geometry (pars. 95-98, 113 and 148).
Regarding claim 5, Roach teaches testing detected width and height against stored type specific dimensions and aspect, detecting a warped or folded object, instructing the user to flatten it and rejecting capture when the test fails (pars. 130-132 and 415-425).
Regarding claim 6, Roach teaches determining distances between detected corners or sides and the image boundary, comparing those distances with thresholds, identifying a cut off object and requiring recapture (pars. 389-414).
Regarding claim 7, see Roach pars. 207-209 and 389-414.
Regarding claim 8, see Roach (pars. 126-132 and 426-432).
Regarding claim 9, see Satish teaches generating and processing a segmentation mask containing foreground sponge pixels (pars. 59-62).
Roach teaches image-size thresholds and rejecting an object that is too small or outside the expected stored size (pars. 126-132 and 426-432).
Regarding claim 19, Satish teaches a method of assessing bloodied surgical sponges with a data reader, processors, a user interface and an optical sensor (pars. 17-21 and 29-30).
Jackson teaches detecting the sponge tag, receiving its unique identifier and identifying the sponge type from a database of predetermined types based on the unique identifier (pars. 13-15 and 42-44).
Roach teaches displaying a presentation window over the live image feed with dimensions based on the selected object type (pars. 126-132 and 148).
Roach teaches automatically capturing an image once the object is presented within the window and the pre-capture quality criteria are satisfied (pars. 7, 70, 92 and 148).
Satish teaches estimating the volume of blood or a blood component on the sponge from the captured image and displaying the estimate (pars. 21, 30, 64 and 66).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Satish (20170186160) in view of Jackson (20130088354) in further view of Satish2 (20170351894).
Regarding claim 18, Satish2 teaches training image classifiers from training images, grouping templates according to the type of physical sample, selecting a subset according to the determined class, providing different blood-indicator color palettes for different sponge types, and using an algorithm or parametric function to estimate hemoglobin from image color (pars. 34, 38, 40 and 43-47).
It would have been obvious prior to the effective filing date of the invention to one of ordinary skill in the art to include in Satish and Jackson a trained hemoglobin-estimation model selected for the identified sponge type as taught by Satish2. The reason is that sponge material and type affect the relationship between image color and hemoglobin content.
Allowable Subject Matter
Claims 3, 13-16 and 28 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 prior art of record does not teach or suggest a sponge-type folding protocol together with a presentation window sized to the sponge after the protocol, as recited in claims 3 and 28.
Determining average motion magnitude of a segmentation mask centroid across a predetermined number of frames and preventing capture when the magnitude exceeds a centroid-change threshold, as recited in claim 13.
Torso and sponge centerline comparisons and corresponding movement guidance recited in claims 14 and 15.
Aggregating localization and segmentation labels, selecting a dominant aggregated label with a sliding window, and requiring that label to satisfy acceptance criteria for a preset number of latest consecutive frames, as recited in claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Amtrup (20140368890) teaches tracking an object with a bounding border, comparing a motion vector with a threshold and automatically capturing a video frame that satisfies quality criteria.
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/HADI AKHAVANNIK/Primary Examiner, Art Unit 2676
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