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
This office action is responsive to the amendment filed on 13 July 2026. As directed by the amendment: claims 4 and 5 have been amended; no claims have been canceled or added. Thus, claims 1-8 are presently pending in this application. Applicant’s amendments to the claims have overcome each and every objection made in the previous office action.
Response to Arguments
Applicant's arguments filed 13 July 2026 have been fully considered but they are not persuasive. Applicant argues that Harris fails to interpret the insertion segment. The insertion segment is defined as the insertion point, insertion direction, and a maximum insertion length. However, Harris chooses a suitable vein based on location, size, and orientation, and the vein identification system (110d) further includes a motion control decision system (111) that includes the direction and depth information. Specifically, the system decides the best direction (¶0108) and the depth of a vessel to determine a final insertion position (¶0111). In addition, the “white shape in the binary image” described in Harris ¶0105 is interpreted as the skeleton of the veins as required by the claims. As such, the rejection is maintained as detailed below.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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, 0108, 0111 – “vein identification system”; the vein identification system further includes a motion control decision engine 111 that determines “the best direction with respect to the patient's vein and forearm” and calculates the depth of the vein for calculating “the final insertion position”), 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, 0108, 0111 – “vein identification system”; the vein identification system further includes a motion control decision engine 111 that determines “the best direction with respect to the patient's vein and forearm” and calculates the depth of the vein for calculating “the final insertion position”), 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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/TASNIM MEHJABIN AHMED/Primary Examiner, Art Unit 3783
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