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
Applicant’s amendment was received on 10/29/25 and has been entered and made of record. Currently, claims 1, 2, and 4-15 are pending.
Drawings
The applicant argues that there is no requirement to label boxes with a descriptive legend in addition to an alphanumeric reference. The Examiner respectfully disagrees. 37 C.F.R. 1.83(a) states, “The drawing in a nonprovisional application must show every feature of the invention specified in the claims. However, conventional features disclosed in the description and claims, where their detailed illustration is not essential for a proper understanding of the invention, should be illustrated in the drawing in the form of a graphical drawing symbol or a labeled representation (e.g., a labeled rectangular box). In addition, tables that are included in the specification and sequences that are included in sequence listings should not be duplicated in the drawings.”. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. It is not clear how an alphanumeric reference is showing an essential feature specified in the claims. A drawing is deficient if it requires one to have to look up in the specification what the feature actually is. Someone looking at the drawings should be able to understand that which is being disclosed by the invention. It is not reasonable to assert this is the case regarding Figs. 1-3, 6, 8, 9, 11, and 12.
Claim Rejections - 35 USC § 101
Applicant’s amendment to claims 1 and 15 and the remarks on pages 6-11 overcome the rejection set forth in the previous Office Action and has therefore been withdrawn.
Response to Arguments
Applicant’s arguments, see pages 12-14 of the remarks, filed 10/29/25, with respect to the rejection(s) of claim(s) 1 and 15 under 35 USC 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
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 and 4-15 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lay et al. (US 2016/0328855), as cited in the IDS dated 7/17/23, in view of Poole et al. (US 9,189,866), cited in the IDS dated 10/29/25.
Regarding claims 1 and 15, Lay discloses a computer-implemented method and a device for extracting vessel information from radiographic imaging data of a subject, comprising:
a memory that stores a plurality of instructions; and
at least one processor coupled to the memory and configured to execute the plurality of instructions to:
identifying a set of landmarks of a vessel in a subject's body using a machine learning model configured to identify adjacent landmarks of the vessel from radiographic imaging data of the subject's body, wherein the ML model is trained to recognize the set of landmarks based on a training data set comprising radiographic imaging data obtained from a plurality of training subjects (see paras 27-28, 33, and 36, a machine learning model identifies landmarks of a vessel from radiographic images); and
determine a path comprising the identified adjacent landmarks in the set (see paras 31, 38, and 46, a path of vessel tracing is determined based on adjacent landmarks); and
a shape of the vessel between the identified adjacent landmarks based on the determined path and an imaging condition indicative of a wall of the vessel (see paras 37, 49, and 51, a shape of a vessel is determined based on the landmarks and boundary of the vessel, the boundary being a vessel wall).
Lay does not disclose expressly wherein the radiographic imaging data obtained from each training subject comprises a target vessel annotated with the set of landmarks to be recognized.
Poole discloses identifying a set of landmarks of a vessel in a subject's body using a machine learning model configured to identify adjacent landmarks of the vessel from radiographic imaging data of the subject's body, wherein the ML model is trained to recognize the set of landmarks based on a training data set comprising radiographic imaging data obtained from a plurality of training subjects, and wherein the radiographic imaging data obtained from each training subject comprises a target vessel annotated with the set of landmarks to be recognized (see col 3 lines 23-31 and 48-52, col 4 lines 1-7 and 20-33, and col 5 lines 10-21, landmark identification unit 12 automatically detects landmarks of a vessel, a machine learning process is used in the identification, the machine learning model is trained with patient data stored in memory).
Before the effective filing of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the identifying of landmarks of a vessel, as described by Poole, with the system of Lay. Lay discloses identifying and segmenting both bone structures and vessel structures to ultimately mask the bone structures and generate a visualization of the vessel structure in a 3D medical image. Poole discloses utilizing landmark information to identify vessels.
The suggestion/motivation for doing so would have been to provide more accurate identification of vessels thereby increasing system efficiency.
Therefore, it would have been obvious to combine Poole with Lay to obtain the invention as specified in claims 1 and 15.
Regarding claim 4, Lay further discloses wherein determining the path comprises identifying a pixel intensity threshold within the radiographic imaging data, wherein the pixel intensity threshold is indicative of identified landmarks being connected to form the path (see paras 33 and 45-48, vessel tracing determination includes pixel intensity thresholding).
Regarding claim 5, Lay further discloses wherein the pixel intensity threshold is determined based on a minimum pixel intensity value and/or an average pixel intensity value associated with each of the identified landmarks (see paras 33 and 45-48, vessel tracing determination includes pixel intensity thresholding and a minimum pixel intensity value).
Regarding claim 6, Lay further discloses determining the path by identifying coordinates of corresponding vessel structures indicative of a vessel segment between adjacent landmarks and estimating a profile of the path based on the coordinates (see paras 31, 38, and 49, coordinates are used to determine a vessel grid and distance between landmarks).
Regarding claim 7, Lay further discloses wherein estimating the profile of the path comprises determining a shortest path between the adjacent landmarks by setting a first landmark as a seed point and, within an iteratively growing volumetric region, identifying a second landmark corresponding to the nearest landmark to the first landmark such that coordinates associated with the first and second landmarks define the path between the first and second landmarks (see para 48, a seed point is used as a starting point, voxels are determined, and distances between landmarks are used to determine vessel tracing/path).
Regarding claim 8, Lay further discloses wherein determining the path comprises identifying a vessel structure corresponding to at least part of a cross-section of an ellipse and determining a center point of the ellipse, the center point defining a coordinate of a center line of the vessel defining the path (see paras 46-49, a center point and centerline are utilized, a cross-sectional slice of the vessel is used to determine vessel structure, tracing, and shape of the vessel).
Regarding claim 9, Lay further discloses wherein the imaging condition indicative of the wall of the vessel is based on a gradient condition indicative of presence of the wall (see paras 49 and 51, the outer boundary of the vessel, or wall of the vessel, is determined).
Regarding claim 10, Lay further discloses determining a coordinate corresponding to a center point of the vessel that defines an origin according to a local coordinate system and determining the shape of the vessel within a vessel cross-section that is perpendicular to the path at the origin by simulating a plurality of rays extending radially from the origin until the gradient condition is met for each radial ray at a specified distance from the origin, wherein the specified distance of each radial ray from the origin is used to estimate a boundary within the vessel cross-section corresponding to at least part of the shape of the vessel (see paras 46-49 and 51, vessel center points/centerlines, vessel cross-sections, a vessel coordinate grid, and vessel boundaries are all used to determine a vessel path and shape).
Regarding claim 11, Lay further discloses determining a vessel segment corresponding to the shape of the vessel between adjacent landmarks by connecting estimated boundaries from adjacent vessel cross-sections (see paras 46-49 and 51, vessel center points/centerlines, vessel cross-sections, a vessel coordinate grid, vessel boundaries, and distance between landmarks are all used to determine a vessel path and shape).
Regarding claim 12, Lay further discloses wherein the gradient condition is determined based on a gradient model of pixel intensity values in a vessel cross-section (see paras 46-49 and 51, vessel center points/centerlines, vessel cross-sections, a vessel coordinate grid, and vessel boundaries are all used to determine a vessel path and shape).
Regarding claim 14, Lay further discloses a non-transitory computer-readable comprising instructions which, when executed by at least one processor, cause the at least one processor to implement the method according to claim 1 (see paras 73-75).
Claim 2 is rejected under 35 U.S.C. 103(a) as being unpatentable over Lay and Poole as applied to claim 1 above, and further in view of Ghesu et al. (US 2018/0253837).
Lay and Pool do not disclose expressly wherein the ML model comprises a Deep Q Network model.
Ghesu discloses wherein the ML model comprises a Deep Q Network model (see Fig. 1 and para 30, a Deep Q network model can be trained to identify landmarks).
Before the effective filing of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the Deep Q Network model to identify landmarks, as described by Ghesu, with the system of Lay.
The suggestion/motivation for doing so would have been to provide the ability to easily handle continuous data input and raw pixel data.
Therefore, it would have been obvious to combine Ghesu with Lay to obtain the invention as specified in claim 2.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 MARK R MILIA whose telephone number is (571)272-7408. The examiner can normally be reached Monday-Friday, 8am-5pm.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681