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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 110 (Fig. 1). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 5 is objected to because of the following informalities: in line 1, “The method method” should be “The method . Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 and 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Harris et al (US 2012/0190981) in view of Shahzad et al (“Subcutaneous Veins Detection and Backprojection Method Using Frangi Vesselness Filter,” provided by applicant).
Regarding claim 1, Harris discloses:
A method for determining at least one optimal insertion segment in a blood vessel of a patient for inserting a needle into said blood vessel (¶0031), said segment being representative of an insertion point in a part of the body of the patient, an insertion direction and a maximum insertion length (¶0033), comprising the following steps: a step of illuminating the part of the body of the patient with near-infrared illumination (¶0140 – “ample illumination onto the patient’s arm”), a step of acquiring near-infrared images of the part of the body of the patient with at least one camera (¶0140 – “obtain contrast of the patient’s vessel”, ¶0142 – “NIR camera 61”, ¶0143 – “insertion site may be identified and localized through the processing of image data 92 received from the single NIR camera 61”), a step of pre-processing the acquired images to obtain an image of the blood vessels visible on the surface of the part of the body of the patient (¶0143 – “some preprocessing is done to calculate a series of features”), referred to as pre-processed image (¶0115 – “a preprocessed image”), a step of defining insertion segments from said images of the blood vessels (¶0105 – “vein identification system”), for each blood vessel, a step of classifying the insertion segments according to predetermined classification parameters (¶0105 – “the vein-like blobs are then analyzed further to rank them”), so as to identify one or more optimal insertion segments (¶0143 – “a scoring module ranks the best veins, and presents them to the user in a highly distinguishable manner”).
Harris discloses all of the elements of the claim but is silent regarding “a step of applying a linear structure detection filter to said pre-processed image to obtain an image, referred to as vascular profile map, which identifies the blood vessels visible on the surface of the part of the body of the patient,” “a step of binarizing the vascular profile map,” and “a step of skeletonising the blood vessels on the binarized vascular profile map, configured to obtain, for each blood vessel, a skeleton of said blood vessel.” However, Shahzad teaches a blood vessel detection system (Abstract), thus being in the same field of endeavor, that uses Frangi vesselness filter, which is a type of pattern recognition technique that uses a multiscale approach. Shahzad teaches applying the Frangi detection filter to detect curvilinear structures in the image (page 66:column 1), which is then binarized (page 66:column 1) and then small vessel-like artifacts are removed (page 66:column 1) and then thinning is applied (page 67:column 2 – “used in many applications more often in skeletonization”). Using this process helps staff select suitable veins for venipuncture procedures (Abstract). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the process of Harris to incorporate use of the filtering steps of Shahzad in order to improve vessel detection, as recognized by Shahzad.
Regarding claim 2, Harris in view of Shahzad discloses:
The method as claimed in claim 1, wherein the predetermined classification parameters (¶0105 – parameters to rank and classify the vessels) for classifying the insertion segments are selected from one or more parameters from the following list: the location of the segment with respect to a known pattern of positions of blood vessels on the part of the body of the patient (¶0106 – “Junctions can be detected” and certain vessels can rank highest if identified); the average density of all of the points of the blood vessel included within contours of the blood vessel corresponding to the segment, calculated on the vascular profile map; the length of the segment (¶0191 – “length”); the depth of the blood vessel in the segment; the diameter of the blood vessel in the segment (¶0191 – “diameter”); the orientation of the segment (¶0191 – “orientation”); the presence or absence of irregularities on the skin on the insertion segment; a preference of the patient (¶0106 – “the user can override the selection made by the vein identification system 110d and choose any viable vein from the available options”); a previous insertion history for the same patient (¶0191 – “patient history and history of successful insertions”).
Regarding claim 3, Harris in view of Shahzad discloses:
The method of claim 1, wherein the linear structure detection filter is a Frangi filter (as taught by Shahzad in the rejection of claim 1).
Regarding claim 5, Harris in view of Shahzad discloses:
The method method of claim 1, wherein the camera is monochromatic and equipped with a near-infrared high-pass filter (¶0048 – “coupled with a bandpass filter used to isolate a near-infrared (NIR) frequency range”, ¶0143 – because the camera is compared to a white-light-sensing camera, camera can be understood to be a white-light sensing camera).
Regarding claim 6, Harris discloses:
A system (Fig. 2A) for determining at least one optimal insertion segment in a blood vessel of a patient for inserting a needle into said vessel (¶0031), said segment being representative of an insertion point in a part of the body of the patient, an insertion direction and a maximum insertion length (¶0033), comprising a unit for acquiring images (61; Fig. 3B) of the part of the body of the patient (¶0142) and a unit for processing the images (90; Fig. 20; ¶0083) acquired by said image acquiring unit (61), wherein said image acquiring unit (61) comprises: near-infrared illumination configured to illuminate the part of the body of the patient with near-infrared illumination (¶0140 – “ample illumination onto the patient’s arm”), and at least one camera (61) configured to acquire near-infrared images of the part of the body of the patient (¶0140), and in that the image processing unit (90) comprises: a module for pre-processing images (¶0143 – “some preprocessing is done to calculate a series of features”) configured to be able to provide an image of the blood vessels visible on the surface of the part of the body of the patient, referred to as pre-processed image (¶0115), a module for defining insertion segments from said images of the blood vessels (¶0105 – “vein identification system”), for each blood vessel, and a module for classifying the insertion segments according to predetermined classification parameters (¶0105 – “the vein-like blobs are then analyzed further to rank them”), configured to identify one or more optimal insertion segments (¶0143 – “a scoring module ranks the best veins, and presents them to the user in a highly distinguishable manner”).
Harris discloses all of the elements of the claim but is silent regarding “a module for filtering, configured to apply a linear structure detection filter to said pre-processed image to obtain an image, referred to as vascular profile map, which identifies the blood vessels visible on the surface of the part of the body of the patient,” “a module for binarizing the vascular profile map,” “a module for skeletonising the blood vessels on the binarized vascular profile map, in order to obtain, for each blood vessel, a skeleton of said blood vessel.” However, Shahzad teaches a blood vessel detection system (Abstract), thus being in the same field of endeavor, that uses Frangi vesselness filter, which is a type of pattern recognition technique that uses a multiscale approach. Shahzad teaches applying the Frangi detection filter to detect curvilinear structures in the image (page 66:column 1), which is then binarized (page 66:column 1) and then small vessel-like artifacts are removed (page 66:column 1) and then thinning is applied (page 67:column 2 – “used in many applications more often in skeletonization”). Using this process helps staff select suitable veins for venipuncture procedures (Abstract). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the processing of Harris to incorporate use of the filtering steps of Shahzad in order to improve vessel detection, as recognized by Shahzad.
Regarding claim 7, Harris in view of Shahzad discloses:
The system as claimed in claim 6, wherein the camera is monochromatic and equipped with a near-infrared high-pass filter (¶0048 – “coupled with a bandpass filter used to isolate a near-infrared (NIR) frequency range”, ¶0143 – because the camera is compared to a white-light-sensing camera, camera can be understood to be a white-light sensing camera).
Regarding claim 8, Harris discloses:
An automatic or semi-automatic insertion machine (Fig. 2A) for the insertion of a needle into a part of the body of a patient (¶0031), comprising a mechatronic assembly (8), a unit for controlling said mechatronic assembly (90; Fig. 20), and an insertion head (3) for a needle (41) mounted on the mechatronic assembly (8), the machine further comprising a determining system (2), configured to determine an optimal insertion segment for inserting the needle into the part of the body of the patient (¶0031) the determining system (2) comprising: near-infrared illumination configured to illuminate the part of the body of the patient with near-infrared illumination (¶0140 – “ample illumination onto the patient’s arm”), and at least one camera (61; Fig. 3B) configured to acquire near-infrared images of the part of the body of the patient (¶0140), and in that the image processing unit (90) comprises: a module for pre-processing images (¶0143 – “some preprocessing is done to calculate a series of features”) configured to be able to provide an image of the blood vessels visible on the surface of the part of the body of the patient, referred to as pre-processed image (¶0115), a module for defining insertion segments from said Images of the blood vessels (¶0105 – “vein identification system”), for each blood vessel, and a module for classifying the insertion segments according to predetermined classification parameters (¶0105 – “the vein-like blobs are then analyzed further to rank them”), configured to identify one or more optimal insertion segments (¶0143 – “a scoring module ranks the best veins, and presents them to the user in a highly distinguishable manner”).
Harris discloses all of the elements of the claim but is silent regarding “a module for filtering, configured to apply a linear structure detection filter to said pre-processed image to obtain an image, referred to as vascular profile map, which identifies the blood vessels visible on the surface of the part of the body of the patient,” “a module for binarizing the vascular profile map,” “a module for skeletonising the blood vessels on the binarized vascular profile map, in order to obtain, for each blood vessel, a skeleton of said blood vessel.” However, Shahzad teaches a blood vessel detection system (Abstract), thus being in the same field of endeavor, that uses Frangi vesselness filter, which is a type of pattern recognition technique that uses a multiscale approach. Shahzad teaches applying the Frangi detection filter to detect curvilinear structures in the image (page 66:column 1), which is then binarized (page 66:column 1) and then small vessel-like artifacts are removed (page 66:column 1) and then thinning is applied (page 67:column 2 – “used in many applications more often in skeletonization”). Using this process helps staff select suitable veins for venipuncture procedures (Abstract). It would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the processing of Harris to incorporate use of the filtering steps of Shahzad in order to improve vessel detection, as recognized by Shahzad.
Allowable Subject Matter
Claim 4 is 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TASNIM M AHMED whose telephone number is (571)272-9536. The examiner can normally be reached M-F 9am-5pm Pacific time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhisma Mehta can be reached at (571)272-3383. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TASNIM MEHJABIN AHMED/Primary Examiner, Art Unit 3783