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 Arguments
Applicant’s arguments, see pages 1 and 2, filed 03/26/2026, with respect to claims 1-20, have been fully considered and are persuasive. The 35 USC § 101 rejection of claims 1-20, 35 USC § 112 rejection of claim 10, and objection of claims 1, 2, 5, 11, 12, 13, 15, 16, and 17 have been withdrawn.
Applicant’s arguments with respect to claims 1 and 13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-3, 5-9, 11, 13-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et. al (“The evaluation of a hybrid biomechanical deformable registration method on a multistage physical phantom with reproducible deformation”) in view of Hébert et. al (U.S. 20,220,398,717), further in view of Popuri et. al (“A FEM Deformable Mesh for Active Region Segmentation”).
Regarding Claim 1, Qin teaches a method for deformable image registration (Fig. 1 (shown below)), the method comprising:
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receiving at least one set of medical images for a patient, wherein at least one medical image comprises pre-drawn contours (Qin: Methods and Materials);
Multi-modality tissue-equivalent phantom: “The contours of the prostate phantom and urethra structure were delineated on MR phase 0.”
Explanation: Qin teaches receiving medical images (MR), each with manually drawn contours that generates the volume mesh.
determining deformed contours for the one or more regions of interest in the at least one set of medical images using a finite element analysis algorithm over one or more of a plurality of phases of a physiological cycle by applying the deformation field over the delineated one or more regions of interest over one or more of a plurality of phases of the physiological cycle, to form the deformed contours; and (Qin: Abstract and Methods and Materials);
Methods: “The BM-DIR method was quantitatively evaluated on a novel phantom capable of ten reproducible gradually-increasing deformation stages using the urethra tube as a surrogate.”
Mesh Generation: “A multi-organ tetrahedral mesh is generated using TetGen.”
Boundary Conditions: “Based on that assumption, the surface node displacement is linearly interpolated from IM-DIR DVF and utilized as the boundary condition for the FEM deformation.”
BM-DIR refinement: “A finite element solver package (ABAQUS, v6.14, Pawtucket, RI) was used to calculate the displacement of internal nodes…the refined DVF is generated by interpolating the displacements on internal nodes to the image grid by scattered linear interpolation.”
Quantitative evaluation of BM-DIR on the multistage phantom: “The IM-DIR and BM-DIR were performed between Phase 0 as reference and Phase i (i = 1 to 10) for CT, MR and masked MR with prostate contour as constrain, generating six types of DVFs for each phase.”
Quantitative evaluation of BM-DIR on the multistage phantom: “The urethra tube was deformed from phase i to phase 0 via different DVFs.”
Multi-modality tissue-equivalent phantom: “The contours of the prostate phantom and urethra structure were delineated on MR phase 0, and propagated to other phases by using ADMIRE, then reviewed and edited if necessary.”
Explanation: Qin teaches FEM mesh creation, FEM solver execution, applying boundary conditions, and producing deformed contours using a deformable vector field (DVF). Qin uses a 10-phase physiologic deformation phantom, where he teaches a plurality (10) of physiologic deformation states, each with its own medical image and each requiring FEM computation. This shows multiple phases, deformation across phases, and application of DVF across phases.
and outputting the deformed contours relative to the one or more regions of interest, wherein the deformation contours are subsequently used for diagnostic, treatment planning, or to direct operation of radiation equipment (Qin: Introduction (shown below) and Methods and Materials and Figure 3 (shown below)).
Introduction: “enable adaptive radiation therapy (ART), not only for the purposes of contour propagation, but also for dose warping [1,2,3,4,5], treatment response evaluation [6, 7] and 4D-inverse plan adaptation.”
Quantitative evaluation of BM-DIR on the multistage phantom: “The DSC, Hausdorff and mean surface distance as defined in [44] were calculated between the propagated and human-delineated urethra tube to evaluate the accuracy of internal DVF.”
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Qin fails to teach the limitation of “delineating, by a processor, one or more regions of interest in the at least one set of medical images using a trained clustering machine learning algorithm.”
However, Hébert teaches a “deep learning model (or a machine learning model), such as a deep convolutional neural network (DCNN), autoencoder neural network, or variational autoencoder neural network,” that “can be trained and used to generate, from one or more training data (e.g., medical images), a plurality of feature vectors that include the low-dimensional feature space representation. The plurality of feature vectors is then split or clustered into different groups each corresponding to a different motion phase of periodic (physiological) motion of a target region in a patient” (paragraph [0092]). Hébert explains that conventional image registration “is a very time-consuming operation that consumes a great amount of resources” and that these “typical approaches are limited in their applications and do not allow for performing a fast image classification, such as in real time during a radiotherapy treatment fraction” (paragraph [0036]). It further states that “the disclosed embodiments address these challenges providing an image classification technique that replaces the current classification algorithm with a clustering approach in a lower-dimensional (low-dimensional) feature space, such as k-means clustering,” and emphasizes that this approach “provides results faster which increases the overall applicability and use of the disclosed image classification techniques” (paragraph [0037]).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Hébert’s ML-based clustering delineation with Qin’s FEM-based contour deformation method because both references address the same underlying task of obtaining anatomical structure contours for deformation and registration. Qin relies on manually drawn contours, which introduce labor, variability, and delay, whereas Hébert teaches an automatic, reproducible, clustering-based delineation techniques that reduces time and computational burden while producing comparable anatomical classification results. Substituting automated ML delineation for manual segmentation constitutes a predictable improvement because better and more efficient contour delineation directly improves downstream FEM accuracy, reduces operator dependence, and is explicitly taught as a superior alternative to registration-based delineation.
Qin and Hébert fail to teach the limitations: “including (i) a first region of interest having a first non-uniform voxel distribution defined by a parametric non- uniform diffusion equation for a first set of tissue property and (ii) a second region surrounding the first region of interest, the second region having a second non-uniform voxel distribution defined by the parametric non-uniform diffusion equation for a second set of tissue property,” and “determining, by the processor, a deformation field using a finite element analysis algorithm, a deformation field in (i) the first region of interest having the first non-uniform voxel distribution and (ii) a second region surrounding the first region of interest.”
However, Popuri teaches regions of interest (ROI) and surrounding regions (multi-region modeling), disclosing “the simultaneous segmentation of multiple non-overlapping anatomical regions with weak boundaries… a template which is a label image defining the topology of the ROIs is smoothly deformed in a non-rigid registration framework such that a region-based energy is minimized to match the actual ROIs in the input image… the desired segmentation boundaries are then implicitly given by the contours of the initial ROIs defined in the template and the deformation field estimated between the template and the input image” (Introduction, pg. 1). This supports a first ROI, a second surrounding region, and an explicit multi-region relationship. Popuri also teaches a non-uniform voxel/mesh distribution tied to the ROIs, stating that “we propose a finite element method (FEM) deformation model, which parametrizes the deformation field on a non-uniform mesh well adapted to the contours of the ROIs in the template” (Introduction, pg. 1), and “in our case, we place more points near the boundaries of the regions defined in the template…the subsequent Delaunay triangulation and the refinement steps automatically ensure that the fine triangular elements are placed around the contours while the coarse elements are used elsewhere” (2.4. Generation of the non-uniform mesh, pg. 3). This directly corresponds to non-uniform voxel/element distribution, region-dependent discretization, and different distributions across regions. Popuri also explicitly discloses that “the deformation field is explicitly regularized by solving the diffusion equation using the Galerkin FEM method” (Introduction, pg. 1), and that “for regularizing the deformation field we solve a diffusion equation: ∂U ∂t =div(∇U)” (2.2. Multi-region segmentation using FEM deform. model, pg. 2). This satisfies a parametric non-uniform diffusion equation governing deformation behavior with region-dependent effects (via mesh and statistics). As shown above, Popuri teaches an FEM-based deformation field applied across regions.
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Qin and Hébert’s system to incorporate Popuri’s FEM-based, diffusion-regularized, non-uniform mesh deformation model. Popuri teaches that FEM-based non-uniform meshes “improve computational efficiency without compromising segmentation accuracy” (Abstract) and “match the actual ROIs in the input image” (Introduction, pg. 1). Therefore, because Qin already performs deformable registration of ROIs, a person of ordinary skill in the art would have been motivated to modify Qin and Hébert’s system as Popuri provides a known improved technique for handling multi-region anatomy, improving computational efficiency, and enabling region-adaptive deformation. Applying a known technique (FEM-based deformation with diffusion regularization) to improve a similar image registration system would have yielded predictable results.
Regarding Claim 2, Qin in view of Hébert and Popuri teaches the method of claim 1, and Hébert further teaches that delineating (e.g. clustering) one or more regions of interest in the at least one set of medical images using a trained clustering machine learning algorithm comprises:
initializing cluster parameters (Hébert: Fig. 7);
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training a cluster machine learning algorithm using the at least one medical image comprising pre-drawn contours (Hébert: Detailed Description);
Paragraph [0092]: “FIG. 7 illustrates an example flow diagram for deep learning, where a deep learning model (or a machine learning model), such as a deep convolutional neural network (DCNN), autoencoder neural network, or variational autoencoder neural network, can be trained and used to generate, from one or more training data (e.g., medical images), a plurality of feature vectors that include the low-dimensional feature space representation.
Paragraph [0047]: “Further, the medical images 146 may also include medical image data, for instance, training images, ground truth images, contoured images, and dose images.”
delineating a plurality of contours of the one or more regions of interest (e.g., clustering) (Hébert: Detailed Description);
Paragraph [0092]: “The plurality of feature vectors is then split or clustered into different groups each corresponding to a different motion phase of periodic (physiological) motion of a target region in a patient.”
and clustering voxels of a medical scan image of the second set within the one or more regions of interest (Hébert: Detailed Description);
Paragraph [0037]: “The disclosed embodiments address these challenges providing an image classification technique that replaces the current classification algorithm with a clustering approach in a lower-dimensional (low-dimensional) feature space, such as k-means clustering.”
Explanation: Clustering feature vectors generated from localized voxel neighborhoods constitutes clustering the underlying voxels, because the feature vectors represent the voxel intensities and spatial characteristics of the medical image. K-means clustering is a well-known voxel-clustering technique in medical imaging segmentation.
Thus, it would have been obvious to one of ordinary skill in the art, prior to filing, to combine Hébert’s ML-based clustering delineation with Qin and Popuri’s FEM-based contour deformation method because automatic ML-based ROI delineation is a known substitute for manual contouring and improves reproducibility and speed (see paragraph [0036] above). Qin explicitly depends on accurate organ contours to construct FEM meshes. Thus, using ML-generated contours provides more consistent FEM initialization. Additionally, these references address identifying anatomical structures for downstream deformation analysis, yielding predictable improvements in processing speed, automation, and clustering accuracy.
Regarding Claim 3, Qin in view of Hébert and Popuri teaches the method of claim 1, and Hébert further teaches that the trained clustering machine learning algorithm comprises an unsupervised or supervised machine learning algorithm (see paragraph [0037] and [0092] above).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Hébert’s teaching of supervised or unsupervised training into Qin and Popuri’s FEM-based contour deformation method because selecting a supervised or unsupervised learning method is a routine design choice in machine learning, and each method yields predictable benefits. Unsupervised clustering (such as k-means) is conventionally used when ground truth labels are unavailable, while supervised models are used when labeled training data improve segmentation accuracy. A person of ordinary skill in the art would have recognized that either method could be predictably substituted to improve or automate contour delineation in Qin and Popuri’s deformable registration pipeline.
Regarding Claim 5, Qin in view of Hébert and Popuri teaches the method of claim 1, and Qin further teaches that determining deformed contours for the at least one set of medical images comprises:
deriving a deformation field using the finite element analysis algorithm, wherein the deformation field is dependent on an adjustable parameter (i.e., k) (Qin: Methods and Materials);
Boundary conditions: “Surface node displacement is linearly interpolated from IM-DIR DVF and utilized as the boundary condition for the FEM deformation.”
BM-DIR refinement: “A finite element solver package (ABAQUS, v6.14, Pawtucket, RI) was used to calculate the displacement of internal nodes.”
BM-DIR refinement: “For simplicity, uniform physical properties of Poisson ratio and Young’s moduli were assigned for the prostate phantom in this study.”
BM-DIR refinement: “The refined DVF is generated by interpolating the displacements on internal nodes to the image grid by scattered linear interpolation.”
Explanation: The FEM deformation field necessarily depends on adjustable parameters including Young’s modulus, Poisson’s ratio, deformation-specific boundary conditions, and phase-specific deformable vector field (DVF) inputs.
and applying the deformation field over the delineated one or more regions of interest over one or more of a plurality of phases of the physiological cycle, thereby forming deformed contours (Qin: Abstract (shown above), Methods and Materials (shown above), and Fig. 3 (shown above));
Regarding Claim 6, Qin in view of Hébert and Popuri teaches the method of claim 1, and Qin further teaches that the method further comprises receiving an adjusted value of the adjustable parameter from a user (Qin: Methods and Materials);
Mesh generation: “To ensure the generation of high-quality mesh, we fine-tuned the parameters of TetGen based on nine tetrahedron quality measures for the typical critical organs in radiotherapy treatment.”
BM-DIR refinement: “A finite element solver package (ABAQUS, v6.14, Pawtucket, RI) was used to calculate the displacement of internal nodes.”
BM-DIR refinement: “Solid soft-tissue organs physical deformation was described using a linear elastic model with the default setting of Poisson ratio 0.40 and Young’s moduli 0.27 MPa.”
Explanation: Qin states that the material parameters (Young’s modulus, Poisson ration) were assigned, requiring user input. Additionally, TetGen mesh generation requires user-chosen parameters such as element size, mesh density, and smoothing levels. Lastly, ABAQUS requires user adjustment of step size, solver type, convergence tolerance, and boundary condition weighting. Therefore, Qin requires user input to set and adjust FEM parameters including material properties, meshing parameters, and solver settings.
Regarding Claim 7, Qin in view of Hébert and Popuri teaches the method of claim 1, and Qin further teaches that in response to the user adjusting the adjustable parameter, the deformed contours are re-evaluated with the adjusted parameter (e.g. k') and the re-evaluated deformed contours are output. Qin explicitly discloses that the FEM must be re-solved whenever model parameters change, including boundary conditions derived from the IM-DIR field (which differ across the 10 phases), material parameters such as Young’s Modulus and Poisson’s ratio assigned to each organ, and solver/mesh parameters chosen in ABAQUS and TetGen (see Qin: Boundary conditions and BM-DIR refinement (shown above)). Because the DVF depends directly on these user-assigned parameters, Qin necessarily recomputes the DVF whenever an adjustable parameter changes. Then, Qin propagates contours using an updated DVF that is different for each phase, producing new deformed contours for all 10 phases (see Qin: Quantitative evaluation of BM-DIR on the multistage phantom (shown above)).
Regarding Claim 8, Qin in view of Hébert and Popuri teaches the method of claim 1, and Qin further teaches that the finite element analysis algorithm comprises a modified Poisson's equation, a modified Navier-Stokes equation, a modified Lagrangian mechanics-based algorithm, or an elastodynamic equation (Qin: Methods and Materials)
BM-DIR refinement: “A finite element solver package (ABAQUS, v6.14, Pawtucket, RI) was used to calculate the displacement of internal nodes.”
BM-DIR refinement: “Solid soft-tissue organs physical deformation was described using a linear elastic model with the default setting of Poisson ratio 0.40 and Young’s moduli 0.27 MPa.”
Explanation: Linear elasticity FEM is a Lagrangian solid-mechanics formulation, because the displacement field is computed with respect to the material configuration of the organ. The standard governing PDE used in linear elasticity FEM is the Navier-Cauchy equation, which is a classical form of a Lagrangian mechanics equation and an elastodynamic equation when time terms are included.
Regarding Claim 9, Qin in view of Hébert and Popuri teaches the method of claim 1, and Hébert further teaches determining a dose distribution for one or more of the plurality of phases (Hébert: Fig. 1 (shown below)), wherein determining the dose distribution comprises:
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determining a dose distribution for fewer than the plurality of phases (Hébert: Detailed Description);
Paragraph [0059]: “The radiation therapy treatment plans 142 may provide information about a particular radiation dose to be applied to each patient.”
Paragraph [0063]: “After the target tumor and the OAR(s) have been located and delineated, a dosimetrist, physician, or healthcare worker may determine a dose of radiation to be applied to the target tumor, as well as any maximum amounts of dose that may be received by the OAR proximate to the tumor (e.g., left and right parotid, optic nerves, eyes, lens, inner ears, spinal cord, brain stem, and the like).”
deriving dose parameters from the fewer than the plurality of phases dose distribution (Hébert: Detailed Description);
Paragraph [0063]: “After the radiation dose is determined for each anatomical structure (e.g., target tumor, OAR), a process known as inverse planning may be performed to determine one or more treatment plan parameters that would achieve the desired radiation dose distribution. Examples of treatment plan parameters include volume delineation parameters (e.g., which define target volumes, contour sensitive structures, etc.), margins around the target tumor and OARs, beam angle selection, collimator settings, and beam-on times.”
Paragraph [0063]: “During the inverse-planning process, the physician may define dose constraint parameters that set bounds on how much radiation an OAR may receive (e.g., defining full dose to the tumor target and zero dose to any OAR; defining 95% of dose to the target tumor, defining that the spinal cord, brain stem, and optic structures receive ≤45 Gy, ≤55 Gy and <54 Gy, respectively).”
and applying the dose parameters to deformed distribution for the plurality of phases (Hébert: Detailed Description);
Paragraph [0060]: “In addition, if the target tumor is close to the OAR (e.g., prostate in near proximity to the bladder and rectum), then by segmenting the OAR from the tumor, the radiotherapy system 100 may study the dose distribution not only in the target but also in the OAR.”
Paragraph [0063]: “Thus, the image processing device 112 can generate a tailored radiation therapy treatment plan 142 having these parameters in order for the radiation therapy device 130 to provide radiotherapy treatment to the patient.”
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Hébert’s phase-dependent dose evaluation into Qin and Popuri’s deformable image registration workflow because Qin already focuses on accurately modeling organ deformation across multiple phases of a physiological cycle (e.g., ten phantom phases). Qin teaches that the purpose of deformable image registration is to “enable adaptive radiation therapy (ART), not only for the purposes of contour propagation, but also for dose warping [1,2,3,4,5], treatment response evaluation [6, 7] and 4D-inverse plan adaptation” (Qin: Introduction). Using those deformations to compute dose accumulation is a well-known objective in radiotherapy planning, as evaluating dose over physiological phases improves treatment accuracy, accounts for anatomic motion, and reduces unintended dose to organs-at-risk (see Hébert: paragraphs [0060] and [0063] (shown above)).
Regarding Claim 11, Qin in view of Hébert and Popuri teaches the method of claim 1, and Qin further teaches that the medical images comprise 4D CT, 4D PET CT, MRI, ultrasound, radionuclide imaging, or optical imaging (see Qin: Quantitative evaluation of BM-DIR on the multistage phantom (shown above)).
Regarding Claim 13, Qin in view of Hébert and Popuri teaches the same limitations as discussed with respect to claim 1 above. The only additional feature in claim 13 is the recitation of a “a user device comprising a means for input and output” and “one or more computing systems comprising one or more processors and one or more storage devices, wherein the one or more storage devices have instructions stored thereon, that when executed by the one or more processors, causes the one or more processors to perform a method.” Qin’s system necessarily includes these components. Qin explicitly performs all computations using ABAQUS, IM-DIR, and TetGen executed on a workstation-based computing platform, which inherently comprises processors (for running ABAQUS and IM-DIR), memory and storage (for storing CT/MRI images, meshes, displacement fields, and FEM parameters), input/output means (for importing images, exporting deformation fields, and displaying registration results), and computer executable instructions stored on a non-transitory medium (ABAQUS and TetGen are software packages executed by the workstation).
Regarding Claim 14, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 11 above.
Regarding Claim 15, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 2 above.
Regarding Claim 17, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 5 above.
Regarding Claim 18, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 6 above.
Regarding Claim 19, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 7 above.
Regarding Claim 20, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 8 above.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Qin et. al in view of Hébert et. al and Popuri et. al, further in view of Layer et. al (“PET image segmentation using a Gaussian mixture model and Markov random fields”). Layer teaches the use a Gaussian Mixture Model (GMM) for image segmentation and clustering of image intensities into anatomical classes.
It would have been obvious to one of ordinary skill in the art, prior to filing, to incorporate the GMM-based clustering approach of Layer into the trained clustering algorithm of Hébert. Hébert teaches that image delineation is performed using a clustering machine learning algorithm in a low-dimensional feature space such as k-means clustering (see paragraph [0037] above), and emphasizes that clustering algorithms may be substituted to improve segmentation speed and accuracy. Layer discloses that “an improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume” (Layer: Abstract). Layer further explains that the GMM approach provides “stable results,” “improvements for small volumes,” and more accurate volume estimates compared to threshold-based or hard-clustering methods (Layer: Abstract). Because both references address automatic segmentation of medical images for radiotherapy workflows, and because GMM is a well-known probabilistic clustering machine learning model that provides predictable improvements over k-means (e.g., soft labeling, better modeling of tissue heterogeneity), a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to substitute the k-means example in Hébert with the GMM of Layer to obtain better performance, robustness, and swiftness (Layer: Abstract), yielding improved clustering performance.
Regarding Claim 16, Qin in view of Hébert and Popuri teaches the system of claim 13, and additional limitations are met as in the consideration of Claim 4 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khan (U.S Patent No. 9,919,163) teaches determining a dose distribution for one or more target regions across multiple phases, applying a deformation parameter to a dose distribution for a subset of phases, determining dose parameters based on dose distribution changes across phases, and identifying an optimal treatment phase based on motion or dose characteristics.
Novosad (U.S Patent No. 20,220,180,523) teaches systems using trained neural networks to perform radiotherapy image segmentation.
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 WILLIAM ADU-JAMFI whose telephone number is (571)272-9298. The examiner can normally be reached M-T 8:00-6:00.
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/WILLIAM ADU-JAMFI/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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