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 .
DETAILED ACTION
Claim Objections
Claims 2, 4 and 6 are objected to because of the following informalities:
It is best to define acronyms the first time they appear in the claims. For example, claim 2, “ICP”, ”RGB”; claim 4, “LLE”; claim 6, “KPCA”.
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities: the acronyms “ICP” and “LLE” is not defined in the specification.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2 and 4 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
As to claim 2, applicant recites “ICP algorithm”. However, the acronyms “ICP” is not defined in the claim and specification. Therefore, there is no way to know what exactly the meaning of ICP.
As to claim 4, applicant recites “LLE algorithm”. However, the acronyms “LLE” is not defined in the claim and specification. Therefore, there is no way to know what exactly the meaning of LLE.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yu, Shumei, et al. ("Correlated skin surface and tumor motion modeling for treatment planning in robotic radiosurgery." Frontiers in Neurorobotics 14 (2020): 582385.)
As to claim 1, Yu discloses a respiration feature extraction method based on body surface significance analysis (Yu, Page 3, “Respiration features are extracted from voxel models by dimensionality reduction and used as a description of the whole thoracoabdominal torso. A correlation model between respiratory features and tumor motion was established. Finally, experimental results with model accuracy and correlation factor were studied to validate the proposed approach.”), comprising:
establishing a body surface voxel model of chest and abdomen respiration motion, which comprises acquisition of point cloud information, generation of the voxel model and extraction of respiration motion features (Yu, Page 3, “obtained more rich information about the patient's body surface by building 3D voxel models. A dimensionality reduction method based on 3D voxel model could extract more robust respiratory features from the established body surface model. It could also overcome the limitation of information loss problem that existed in current respiration tracking methods.” “The main framework of constructing correlation model is (1) establish dynamic thoracoabdominal surface voxel model, (2) reduce the dimension of the voxel model and extract the low-dimensional representation vector of the voxel model, and (3) establish the correlation model between the representation vector and the tumor motion state.” “Point Cloud Acquisition System” “Modeling of Thoracoabdominal Surface With Respiratory Movement”);
establishing a significance evaluation function by performing significance analysis on different regions of a body surface, and selecting the body surface region with high correlation with tumor motion based on the evaluation function (Page 5, “we first extract its physical significance features, then perform data analysis on the voxel model to extract respiratory features.” Page 6, “Correlation Between Characterization Vector and Tumor Motion” “The establishment of respiratory correlation model is a key part of tumor tracking. The correlation function is to take the breathing surrogate signal (the characteristic signal of skin surface motion that is highly correlated with tumor motion in the body) as the input data and realize the motion estimation of the internal tumor by correlating the surrogate with the movement of the internal tumor. Therefore, we used the extracted representation vector as external surface motion surrogate to establish a correlation model with internal tumor motion.” “The so-called hysteresis phenomenon (the phase lag between body surface characteristic respiratory motion and tumor respiratory motion) is attributed to the complex respiratory pressure–volume relationship of the lungs and chest and abdomen” Fig. 10-Fig.11); and
performing voxelization on the body surface region, and obtaining effective one- dimensional characterization information of the body surface region using a local linear embedding dimension reduction algorithm (Yu, Page 4, “Watertight Thoracoabdominal Model Establishment and Voxelization” Page 5-6, “Intrinsic data characteristics refer to the information that reveals the motional information of the body surface obtained by the voxel model. It is obtained by reducing the dimensionality of the voxel model. It is necessary for the construction of the respiration tracking model.” Fig. 6. Page 6, “Since the bounding box contained a large number of voxels, which could be illustrated as columns of a superhigh-dimensional vector, it would cost huge calculation if the original vectors were used to build the correlation model. Therefore, the vector Υ with superhigh dimension was transformed into a low-dimensional vector ψ that remained the characteristics of the voxel model changes. To accomplish dimension reduction, an algorithm based on LLE (Roweis, 2000) is shown in Algorithm 2.”).
As to claim 2, claim 1 is incorporated and Yu discloses wherein the acquisition of the point cloud information comprises:
unifying coordinates of two RGB-D depth cameras with fixed positions to the same coordinate system using a calibration plate (Yu, Page 3, “a calibration plate coordinate was built first by identification of the corner points, and then, the camera coordinate could be converted to the universal coordinates.” Page 8, “We used two Kinect V2 (Microsoft Co.) depth cameras placed at both sides of the experimental bed to collect data.”);
collecting point cloud information of the chest and abdomen body surface by the two RGB-D depth cameras with fixed positions (Fig. 2, Page 3, “Three-dimensional modeling of dynamic human thoracoabdominal skin surface during respiration mainly includes point cloud collecting of dynamic skin surface using multiple cameras of Kinect V2, model establishment of thoracoabdominal skin surface, and surface reconstruction into voxel model.”);
removing noise points using a statistical filtering algorithm and registering the two groups of point cloud information using an ICP algorithm (Page 3-4, “Although the point clouds of multiple cameras have been unified into the same coordinate system, the raw data of point cloud has noises and outliers brought by cameras themselves and infrared interference between each other camera. To pre-process the raw data, we used bilateral filtering (Tomasi and Manduchi, 1998) in denoise and statistical filtering (Moore, 1978) to eliminate outliers.” “Due to certain calibration errors, the registration of the adjusted multiple point clouds still has overlap, which requires precise registration. We used a classical point cloud registration algorithm iterative closest point (ICP) to register multiple point clouds.”);
removing redundant information utilizing RGB and boundary threshold segmentation (Page 3-4, “Due to the fixed placement of multiple cameras and the fixed scene, this paper adopted a fast and convenient segmentation algorithm with distance and color thresholds. The chest and abdomen area of the lying subject and the position of the treatment bed are constrained, and the auxiliary limit of the color threshold is applied to divide the expected chest and abdomen surface area.”); and
smoothing the point cloud data using a mobile least square algorithm (Page 4, “The segmented surface is uneven and has burrs. In order to make the thoracoabdominal surface model smooth, point clouds need to be smoothed. In this paper, moving least squares method (Breitkopf et al., 2005) was used to smooth point clouds.”).
As to claim 3, claim 2 is incorporated and Yu discloses wherein the generation of the voxel model comprises: inserting the processed point cloud information into Octomap, and creating a voxel map by defining occupied and idle states of the point cloud in a space (Page 5, “We used Octomap library (Hornung et al., 2013) to transform the point cloud into a voxel model. The voxelization of the model is shown in Figure 5B.” Details of occupied and idle states of point cloud are in Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., and Burgard, W. (2013). OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Rob. 34, 189–206. doi: 10.1007/s10514-012-9321-0).
As to claim 4, claim 3 is incorporated and Yu discloses wherein the extraction of the respiration motion features comprises:
modeling body surface information of each frame into a voxel model, the voxel model representing a body surface respiration motion state at a moment (Yu, Page 3, “Modeling of Thoracoabdominal Surface With Respiratory Movement”);
traversing the voxel models of all frames, constructing a rectangular parallelepiped bounding box, i.e., a minimum bounding box, and allowing the bounding box to accommodate the largest one-frame voxel model (Yu, Page 5, “To study the respiration characteristic displayed by volume changes, the voxel units of voxel model of each frame were traversed, and then, the volume of voxel units was accumulated; therefore, the volume change is reflected in the time series. The area change of voxel model is reflected in the area of voxel's outer layer in the similar way.” Page 6, algorithm 1. Fig.6);
taking out voxel blocks from the bounding box according to the same traversal order for each voxel model, so as to form a one-dimensional column vector (Yu, Page 5-6, “Intrinsic data characteristics refer to the information that reveals the motional information of the body surface obtained by the voxel model. It is obtained by reducing the dimensionality of the voxel model. It is necessary for the construction of the respiration tracking model.” Fig. 6. Page 6, “Since the bounding box contained a large number of voxels, which could be illustrated as columns of a superhigh-dimensional vector, it would cost huge calculation if the original vectors were used to build the correlation model. Therefore, the vector Υ with superhigh dimension was transformed into a low-dimensional vector ψ that remained the characteristics of the voxel model changes. To accomplish dimension reduction, an algorithm based on LLE (Roweis, 2000) is shown in Algorithm 2.”); and
reducing dimensions of the one-dimensional column vector using an LLE algorithm, so as to obtain a low-dimensional feature capable of characterizing the body surface respiration motion feature (Yu, Page 6, “Since the bounding box contained a large number of voxels, which could be illustrated as columns of a superhigh-dimensional vector, it would cost huge calculation if the original vectors were used to build the correlation model. Therefore, the vector Υ with superhigh dimension was transformed into a low-dimensional vector ψ that remained the characteristics of the voxel model changes. To accomplish dimension reduction, an algorithm based on LLE (Roweis, 2000) is shown in Algorithm 2.”).
As to claim 10, claim 1 is incorporated and Yu discloses
establishing a polynomial association model for the effective one-dimensional characterization information after dimension reduction and in-vivo tumor information (Yu, Page 6, “Due to the non-linear relationship between internal (the motion information of tumor in vivo) and external motion (the motion information of body surface), we adopt a polynomial model (Peressutti et al., 2012). That is, the trajectory of the tumor in vivo is approximated as a linear combination of multiple power terms of the external signal. In this paper, different polynomial functions are used to model the breathing movement during the exhalation and inhalation phases”).
Allowable Subject Matter
Claims 5-9 are 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 YU CHEN whose telephone number is (571)270-7951. The examiner can normally be reached on M-F 8-5 PST Mid-day flex.
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/YU CHEN/Primary Examiner, Art Unit 2613