DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. This Office Action is in response to the replied to restrictions filed on 01/23/2026.
III. Claims 1-9 are pending and have been examined, where claims 1-9 is/are rejected. Explanations will be provided below.
IV. Inventor and/or assignee search were performed and determined double patenting rejection(s) is/are necessary. Details are shown below.
V. Patent eligibility (updated in 2019) shown by the following: Claims 1-9 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations: “determine a misclassification for a first blood vessel of the one or more blood vessels, the misclassification denoting the neural network assigning the one of the vein classification and the artery classification to the first blood vessel in a first ultrasound image of the ultrasound images and an other of the vein classification and the artery classification to the first blood vessel in one or more additional ultrasound images of the ultrasound images” effects a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of blood vessel detection, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
[2] Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 1-9 are not interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): limitations are modified by sufficient structure or material for performing the claimed function.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention.
[3] Grounds of Rejection
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claim 1 of U.S. Patent No. 11900593. The conflicting claims are not identical because patent claim YY requires the additional step of “the one ultrasound image represents a later frame in the ultrasound video than previous frames represented by the additional ultrasound images, wherein the assigning the one of the vein classification and the artery classification in the one ultrasound image is based on the one ultrasound image and at least one of the additional ultrasound images, and wherein the one ultrasound image and the at least one of the additional ultrasound images are greyscale images, and the assigning the one of the vein classification and the artery classification in the one ultrasound image includes receiving the greyscale images on separate color channels of the neural network”, not required by claim 1. However, the conflicting claims are not patentably distinct from each other because: Claim 1 and patent claim 1 recite common subject matter; whereby claim 1, which recites the open ended transitional phrase “comprising”, does not preclude the additional elements recited by patent claim YY, and whereby the elements of claim XX are fully anticipated by patent claim YY, and anticipation is “the ultimate or epitome of obviousness” (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Since claim 1 is an independent claim, all claims that depend on claim 1 will be indicated rejected in the office action.
[4] Allowable Subject Matter
Claims 1-9 are allowable / patentable if the double patenting rejection is overcome. The following is an examiner’s statement of reasons for allowance by comparing claims to closest found references. The references are divided into primary and secondary, where primary would have been utilized in a USC 102 or main USC 103 reference and secondary would had been utilized a secondary USC 103 reference, but these references do not cover enough of the claim’s scope to warrant a rejection.
Primary reference, MATSUMOTO (US 20210128101) discloses a processing device for blood vessel identification, comprising: a memory; and one or more processors coupled to the memory that is configured to:
assign, with a neural network implemented at least partially in hardware of the processing device, one of a vein classification and an artery classification to one or more blood vessels in ultrasound images (see paragraph 39, a general image recognition method using deep learning includes image classification by a convolutional neural network (CNN) in which an ultrasound image);
display, in the first ultrasound image, an indication that the
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MATSUMOTO is silent in disclosing determine a misclassification for a first blood vessel of the one or more blood vessels, the misclassification denoting the neural network assigning the one of the vein classification and the artery classification to the first blood vessel in a first ultrasound image of the ultrasound images and an other of the vein classification and the artery classification to the first blood vessel in one or more additional ultrasound images of the ultrasound images.
MATSUMOTO also does not qualify as a prior art reference because MATSUMOTO qualifies as a USC 102(a)(2) reference, but MATSUMOTO and the current application is commonly owned by FUJIFLIM corporation.
Secondary reference, Brattain (US 20210045711) discloses determine a misclassification for a first blood vessel of the one or mo blood vessels, the misclassification denoting the neural network assigning the one of the vein classification and the artery classification to the first blood vessel in a first ultrasound image of the ultrasound images and an other of the vein classification and the artery classification to the first blood vessel in one or more additional ultrasound images of the ultrasound images (see paragraph 48, a neural network may be deployed for machine learning and may learn features at multiple spatial and temporal scales, paragraph 50, prevent any potential misclassifications conflicting information checks may be included in the system, where a conflicting information check may include taking into consideration the general configuration of the anatomy at the location of the probe, if the system initially identifies two arteries at a location of the probe, but the general anatomy at the location of the probe indicates that an artery and a vein should be returned as results instead then the system will automatically correct to properly identify an artery and a vein … ); and
display (see paragraph 46, an image is displayed for a user of the vessel of interest with any tracking information for the needle overlaid on the image. In some configurations, no image is displayed for a user and instead only the insertion point may be identified by illuminating a portion of the surface of a subject), in the first ultrasound image, an indication that the misclassification of the one of the vein classification and the artery classification is adjusted to the other of the vein classification and the artery classification for the first blood vessel (see paragraph 50, then the system will automatically correct to properly identify an artery and a vein instead of the mistaken two arteries to prevent a misclassification).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include “determine a misclassification for a first blood vessel of the one or more blood vessels, the misclassification denoting the neural network assigning the one of the vein classification and the artery classification to the first blood vessel in a first ultrasound image” in order to correct automated errors, ensuring consistency, and refining the accuracy of identification, which helps validate the correct classification across multiple frames and ensuring reliable clinical diagnostics.
MATSUMOTO also discloses the processing device as described in claim 1, wherein the first ultrasound image and the one or more additional ultrasound images represent frames in an ultrasound video, and the first ultrasound image represents a later frame in the ultrasound video than previous frames represented by the one or more additional ultrasound images (see paragraph 145, the one ultrasound image represents a later frame in the ultrasound video than the additional ultrasound images that represent previous frames in the ultrasound video. In one embodiment, the previous frames represented by the additional ultrasound images are consecutive frames in the ultrasound video, where the video includes plurality of frames in sequential order, which includes previous and later frames).
MATSUMOTO also discloses the processing device as described in claim 2, wherein the previous frames represented by the one or more additional ultrasound images are consecutive in the ultrasound video (see paragraph 47, the ultrasound machine can detect a blood vessel in multiple ultrasound images, and then fail to detect the blood vessel in a subsequent ultrasound image, e.g., an ultrasound image that follows the multiple ultrasound images in a video sequence).
Brattain also discloses the processing device as described in claim 2, wherein said assign the one of the vein classification and the artery classification in the first ultrasound image is based on the assigning the one of the vein classification and the artery classification in the one or more additional ultrasound images (see paragraph 50, then the system will automatically correct to properly identify an artery and a vein instead of the mistaken two arteries to prevent a misclassification).
MATSUMOTO also discloses the processing device as described in claim 2, wherein said assign the one of the vein classification and the artery classification in the first ultrasound image is based on the first ultrasound image and the one or more additional ultrasound images (see figure 8A to 8B, the artery and vein are assigned on the ultrasound please, where 8C is read as the additional ultrasound image).
MATSUMOTO also discloses the processing device as described in claim 5, wherein the first ultrasound image and the one or more additional ultrasound images are greyscale images, and the one or more processors is further configured to: receive the greyscale images on separate color channels of the neural network (see paragraph 39, Example of a general image recognition method using deep learning includes image classification by a convolutional neural network, the medical image are in grayscale, see figure 5).
Primary reference, Hemelings et al. (Ruben Hemelings, Bart Elen, Ingeborg Stalmans, Karel Van Keer, Patrick De Boever, Matthew B. Blaschko, Artery–vein segmentation in fundus images using a fully convolutional network, Computerized Medical Imaging and Graphics, Volume 76, 2019, 101636) discloses a processing device for blood vessel identification, comprising: a memory; and one or more processors coupled to the memory that is configured to:
assign, with a neural network implemented at least partially in hardware of the processing device (see Training Details below), one of a vein classification and an artery classification to one or more blood vessels in Fundus images (see figure 1 showing fundus images, where Arteries are colored red while veins are colored blue);
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determine a misclassification for a first blood vessel of the one or more blood vessels, the misclassification denoting the neural network assigning the one of the vein classification and the artery classification to the first blood vessel in a first ultrasound image of the ultrasound images (see figure 5 illustration below) but is silent in disclosing another of the vein classification and the artery classification to the first blood vessel in one or more additional ultrasound images of the ultrasound images; and
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display, in the first ultrasound image, an indication that the misclassification of the one of the vein classification (see figure 5, visualization of the region surrounding the fovea, with exclusively secondary vessels, the misclassification) but is silent in disclosing the artery classification is adjusted to the other of the vein classification and the artery classification for the first blood vessel:
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MATSUMOTO, Brattain, and Hemelings, taken alone or in combination with each other, are silent in disclosing all the limitations of claim 1. Combining Brattain and Hemelings constitutes an improper piecemeal approach.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 2/6/26