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 .
Status of the Claims
Claims 1-12 and 14-20, as amended, are currently pending and have been considered below.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 9, 12, 14-17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li G, Gan Y, Liu G, Chen F. High-accuracy point cloud registration for 3D shape measurement based on double constrained intersurface mutual projections. Measurement. 2022 March 21;194:111050, hereinafter, “Li”, and further in view of Segal A, Haehnel D, Thrun S. Generalized-ICP. In Robotics: science and systems 2009 Jun 28 (Vol. 2, No. 4, p. 435), hereinafter, “Segal”.
As per claim 1, Li discloses a registration method (Li, Abstract, This paper proposes a new high-accuracy registration method), comprising:
obtaining a first image model of an object to be registered used in a previous registration and a first transformation matrix obtained in the previous registration (Li, page 3, 3.1. Traditional ICP registration, there is a fixed target point cloud P ∈ R3×M1 and a source point cloud Q ∈ R3×M2 … To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1);
performing a current registration, comprising:
adjusting, based on the first transformation matrix, the first image model to obtain a second image model (Li, Equation 1: page 3,
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projecting a first registration point set on a surface of the object to be registered onto a surface of the second image model to obtain a projection point set (Li, page 3, 3.2. The double constrained intersurface mutual projection, the initial corresponding relationships are constructed … and the corresponding neighbor point set is regarded as the bidirectional projection region. Then, the neighboring points are fitted with a local surface, and the original points are projected into the target area to create new correspondences).
Li does not explicitly disclose the following limitation as further recited however Segal discloses
registering the first registration point set with the projection point set to obtain a second transformation matrix (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0; output: The correct transformation, T, which aligns A and B).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Segal and Li because they are in the same field of endeavor. One skilled in the art would have been motivated to include the iteration to convergence as taught by Segal in the system of Li in order to fine tune the registration (Segal, Abstract).
As per claim 2, Li and Segal disclose the registration method according to claim 1, wherein projection points in the projection point set are projection points having closest distances to corresponding registration points in the first registration point set respectively (Li, page 3, 3.1. Traditional ICP registration, To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1 ... Take the sum of the squares of the point distances as the optimization function: [Equation 1] ... ICP method solves for coordinate transformation parameters R* and t* by minimizing the sum of square distances, as shown in Fig. 1a)).
As per claim 3, Li and Segal disclose the registration method according to claim 1, wherein registering the first registration point set with the projection point set to obtain the second transformation matrix comprises: determining, based on pose coordinates of respective registration points in the first registration point set and pose coordinates of respective projection points in the projection point set, the second transformation matrix (Li, page 3, 3.1. Traditional ICP registration, there is a fixed target point cloud P ∈ R3×M1 and a source point cloud Q ∈ R3×M2 with a stepwise pose correction, where M1 and M2 are the number of points in the target and the source cloud, respectively; Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0).
As per claim 4, Li and Segal disclose the registration method according to claim 3, wherein determining, based on the pose coordinates of the registration points in the first registration point set and the pose coordinates of the projection points in the projection point set, the second transformation matrix comprises: substituting the pose coordinates of the registration points in the first registration point set and the pose coordinates of the projection points in the projection point set as known quantities into a first objective function, and substituting the second transformation matrix as an unknown quantity into the first objective function, to solve the second transformation matrix (Li, page 3, 3.1. Traditional ICP registration, there is a fixed target point cloud P ∈ R3×M1 and a source point cloud Q ∈ R3×M2 with a stepwise pose correction, where M1 and M2 are the number of points in the target and the source cloud, respectively; Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0);
wherein the first objective function is configured to minimize a pose difference between a first transformation point set and the first registration point set, and the first transformation point set is obtained by transforming the projection point set based on the second transformation matrix (Li, page 3, 3.1. Traditional ICP registration, To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1 ... Take the sum of the squares of the point distances as the optimization function: [Equation 1] ... ICP method solves for coordinate transformation parameters R* and t* by minimizing the sum of square distances, as shown in Fig. 1a)).
As per claim 5, Li and Segal disclose the registration method according to claim 1, wherein registering the first registration point set with the projection point set to obtain the second transformation matrix comprises: obtaining registration weights corresponding to respective registration points in the first registration point set; and substituting the registration weights corresponding to the respective registration points in the first registration point set, pose coordinates of the registration points in the first registration point set, and pose coordinates of respective projection points in the projection point set as known quantities into a second objective function, and substituting the second transformation matrix as an unknown quantity into the second objective function, to solve the second transformation matrix (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input : Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0; output: The correct transformation, T, which aligns A and B; 1 T ← T0; 2 while not converged do 3 for i ← 1 to N do 4 mi ← FindClosestPointInA(T ∙ bi); 5 if ||mi - T ∙ bi|| ≤ dmax then 6 wi ← 1; 7 else 8 wi ← 0; 9 end 10 end – Algorithm 1 Standard ICP [the weights wi depend on a pose difference]);
the second objective function being configured to minimize a pose difference between a first transformation point set and the first registration point set, and the first transformation point set being obtained by transforming the projection point set based on the second transformation matrix (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation).
As per claim 9, Li and Segal disclose the registration method according to claim 1, wherein obtaining the first transformation matrix obtained in the previous registration comprises:
registering a registration point set on a surface of the first image model with the first registration point set to obtain the first transformation matrix (Li, page 3, 3.1. Traditional ICP registration, To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1 ... Take the sum of the squares of the point distances as the optimization function: [Equation 1] ... ICP method solves for coordinate transformation parameters R* and t* by minimizing the sum of square distances, as shown in Fig. 1a); Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0).
As per claim 12, Li and Segal disclose the registration method according to claim 1, further comprising: moving and rotating, based on an offset and a rotation angle in the second transformation matrix, the second image model to obtain a matched image model (Li, Equation 1: page 3, rotation term R and translation term t of the transformation
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As per claim 14, Li discloses a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the following steps:
obtaining a first image model of an object to be registered used in a previous registration and a first transformation matrix obtained in the previous registration (Li, page 3, 3.1. Traditional ICP registration, there is a fixed target point cloud P ∈ R3×M1 and a source point cloud Q ∈ R3×M2 … To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1);
performing a current registration, comprising:
adjusting, based on the first transformation matrix, the first image model to obtain a second image model (Li, Equation 1: page 3,
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projecting a first registration point set on a surface of the object to be registered onto a surface of the second image model to obtain a projection point set (Li, page 3, 3.2. The double constrained intersurface mutual projection, the initial corresponding relationships are constructed … and the corresponding neighbor point set is regarded as the bidirectional projection region. Then, the neighboring points are fitted with a local surface, and the original points are projected into the target area to create new correspondences).
Li does not explicitly disclose the following limitation as further recited however Segal discloses
registering the first registration point set with the projection point set to obtain a second transformation matrix (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0; output: The correct transformation, T, which aligns A and B).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Segal and Li because they are in the same field of endeavor. One skilled in the art would have been motivated to include the iteration to convergence as taught by Segal in the system of Li in order to fine tune the registration (Segal, Abstract).
As per claim 15, Li and Segal disclose a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, steps of the registration method of claim 1 are implemented (Li, Abstract; Li, page 3, 3.1. Traditional ICP registration; Li, page 3, Equation 1; Li, page 3, 3.2. The double constrained intersurface mutual projection; Segal, page 2, II. Scan Matching, A. ICP).
As per claim 16, Li and Segal disclose a computer program product comprising a computer program, wherein when the computer program is executed by a processor, steps of the registration method of claim 1 are implemented (Li, Abstract; Li, page 3, 3.1. Traditional ICP registration; Li, page 3, Equation 1; Li, page 3, 3.2. The double constrained intersurface mutual projection; Segal, page 2, II. Scan Matching, A. ICP).
As per claim 17, Li and Segal disclose the registration method according to claim 5, wherein for each registration point in the first registration point set, the registration weight being obtained based on at least one of a registration number of the current registration, a preset registration number, and a pose difference between the registration point and a projection point corresponding to the registration point (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input : Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0; output: The correct transformation, T, which aligns A and B; 1 T ← T0; 2 while not converged do 3 for i ← 1 to N do 4 mi ← FindClosestPointInA(T ∙ bi); 5 if ||mi - T ∙ bi|| ≤ dmax then 6 wi ← 1; 7 else 8 wi ← 0; 9 end 10 end – Algorithm 1: Standard ICP [the weights wi depend on a pose difference]).
As per claim 19, Li discloses a system, comprising a terminal and a medical scanning device, the terminal is communicatively connected with the medical scanning device, wherein the terminal is configured to:
obtain a first image model of an object to be registered used in a previous registration and a first transformation matrix obtained in the previous registration (Li, page 3, 3.1. Traditional ICP registration, there is a fixed target point cloud P ∈ R3×M1 and a source point cloud Q ∈ R3×M2 … To obtain the relative transformation between this pair of point clouds, it is necessary to solve the rotation matrix R ∈ R3×3 and translation matrix t ∈ R3×1);
perform a current registration, comprising:
adjust, based on the first transformation matrix, the first image model to obtain a second image model (Li, Equation 1: page 3,
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project a first registration point set on a surface of the object to be registered onto a surface of the second image model to obtain a projection point set (Li, page 3, 3.2. The double constrained intersurface mutual projection, the initial corresponding relationships are constructed … and the corresponding neighbor point set is regarded as the bidirectional projection region. Then, the neighboring points are fitted with a local surface, and the original points are projected into the target area to create new correspondences).
Li does not explicitly disclose the following limitation as further recited however Segal discloses
register the first registration point set with the projection point set to obtain a second transformation matrix (Segal, page 2, II. Scan Matching, A. ICP, The key concept of the standard ICP algorithm can be summarized in two steps: 1) compute correspondences between the two scans. 2) compute a transformation which minimizes distance between corresponding points. Iteratively repeating these two steps typically results in convergence to the desired transformation ... Standard ICP ... input: Two point clouds: A = {ai}, B = {bi}; An initial transformation: T0; output: The correct transformation, T, which aligns A and B).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Segal and Li because they are in the same field of endeavor. One skilled in the art would have been motivated to include the iteration to convergence as taught by Segal in the system of Li in order to fine tune the registration (Segal, Abstract).
Claim(s) 6, 8, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li G, Gan Y, Liu G, Chen F. High-accuracy point cloud registration for 3D shape measurement based on double constrained intersurface mutual projections. Measurement. 2022 March 21;194:111050, hereinafter, “Li”, in view of Segal A, Haehnel D, Thrun S. Generalized-ICP. In Robotics: science and systems 2009 Jun 28 (Vol. 2, No. 4, p. 435), hereinafter, “Segal” as applied to claim 1 above, and further in view of Qu et al., Chinese Publication No. CN 104778688A, hereinafter, “Qu”.
As per claim 6, Li and Segal disclose the registration method according to claim 1, but do not explicitly disclose the following limitations as further recited however Qu discloses
wherein respective registration points in the first registration point set are physiological and anatomical feature points on the surface of the object to be registered (Qu, ¶0004, 3D reconstruction technology has been widely used in fields such as computer-aided geometric design, computer graphics, computer animation, and medical image processing), and
after registering the first registration point set with the projection point set to obtain the second transformation matrix, the registration method further comprises: determining whether a registration number of the current registration reaches a first iteration number threshold (Qu, ¶0034, Using the partially matched point pairs, the ICP objective function is iteratively calculated to minimize its value. The attitude transformation parameter at which the minimum value of the ICP objective function is obtained is taken as the second initial attitude transformation parameter. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. The attitude transformation parameter obtained through iteration is then taken as the first attitude transformation parameter); and
if the registration number of the current registration does not reach the first iteration number threshold, taking the second image model as the first image model, taking the second transformation matrix as the first transformation matrix, and returning to perform the step of adjusting, based on the first transformation matrix, the first image model to obtain the second image model (Qu, ¶0083, The second iterative subunit is used to iteratively calculate the ICP objective function using the partially matched point pairs to minimize the value of the ICP objective function. The attitude transformation parameters obtained when the minimum value of the ICP objective function is obtained are used as the second initial attitude transformation parameters. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. Then, the attitude transformation parameters obtained by iteration are used as the first attitude transformation parameters).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Qu with Li and Segal because they are in the same field of endeavor. One skilled in the art would have been motivated to include the preset thresholds as taught by Qu in the system of Li and Segal in order to reduce accumulated errors during the point cloud registration process (Qu, Abstract).
As per claim 8, Li and Segal disclose the registration method according to claim 1, but do not explicitly disclose the following limitations as further recited however Qu discloses wherein respective registration points in the first registration point set are points other than physiological and anatomical feature points on the surface of the object to be registered (Qu, ¶0004, 3D reconstruction technology has been widely used in fields such as computer-aided geometric design, computer graphics, computer animation, and medical image processing), and
after registering the first registration point set with the projection point set to obtain the second transformation matrix, the registration method further comprises: determining whether a registration number of the current registration reaches a third iteration number threshold (Qu, ¶0034, Using the partially matched point pairs, the ICP objective function is iteratively calculated to minimize its value. The attitude transformation parameter at which the minimum value of the ICP objective function is obtained is taken as the second initial attitude transformation parameter. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. The attitude transformation parameter obtained through iteration is then taken as the first attitude transformation parameter); and
if the registration number of the current registration does not reach the third iteration number threshold, taking the second image model as the first image model, taking the second transformation matrix as the first transformation matrix, and returning to perform the step of adjusting, based on the first transformation matrix, the first image model to obtain the second image model (Qu, ¶0083, The second iterative subunit is used to iteratively calculate the ICP objective function using the partially matched point pairs to minimize the value of the ICP objective function. The attitude transformation parameters obtained when the minimum value of the ICP objective function is obtained are used as the second initial attitude transformation parameters. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. Then, the attitude transformation parameters obtained by iteration are used as the first attitude transformation parameters).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Qu with Li and Segal because they are in the same field of endeavor. One skilled in the art would have been motivated to include the preset thresholds as taught by Qu in the system of Li and Segal in order to reduce accumulated errors during the point cloud registration process (Qu, Abstract).
As per claim 11, Li and Segal disclose the registration method according to claim 1, but do not explicitly disclose the following limitations as further recited however Qu discloses further comprising:
determining whether the second transformation matrix obtained in the current registration reaches a preset transformation matrix threshold (Qu, ¶0034, Using the partially matched point pairs, the ICP objective function is iteratively calculated to minimize its value. The attitude transformation parameter at which the minimum value of the ICP objective function is obtained is taken as the second initial attitude transformation parameter. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. The attitude transformation parameter obtained through iteration is then taken as the first attitude transformation parameter); and
if the second transformation matrix does not reach the preset transformation matrix threshold, taking the second image model as the first image model, taking the second transformation matrix as the first transformation matrix, and returning to perform the step of adjusting, based on the first transformation matrix, the first image model to obtain the second image model, until the second transformation matrix obtained in the current registration reaches the preset transformation matrix threshold (Qu, ¶0083, The second iterative subunit is used to iteratively calculate the ICP objective function using the partially matched point pairs to minimize the value of the ICP objective function. The attitude transformation parameters obtained when the minimum value of the ICP objective function is obtained are used as the second initial attitude transformation parameters. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. Then, the attitude transformation parameters obtained by iteration are used as the first attitude transformation parameters).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Qu with Li and Segal because they are in the same field of endeavor. One skilled in the art would have been motivated to include the preset thresholds as taught by Qu in the system of Li and Segal in order to reduce accumulated errors during the point cloud registration process (Qu, Abstract).
As per claim 18, Li, Segal and Qu disclose the registration method according to claim 11, wherein the preset transformation matrix threshold comprises a threshold of an offset and a threshold of a rotation angle in the second transformation matrix (Qu, ¶0083, The second iterative subunit is used to iteratively calculate the ICP objective function using the partially matched point pairs to minimize the value of the ICP objective function. The attitude transformation parameters obtained when the minimum value of the ICP objective function is obtained are used as the second initial attitude transformation parameters. The process of filtering partially matched points and minimizing the value of the ICP objective function continues until the first specified value is not greater than the first preset threshold. Then, the attitude transformation parameters obtained by iteration are used as the first attitude transformation parameters; Qu, ¶0207, In the formula, n is the number of all matching point pairs between the first frame point cloud data F j and the second frame point cloud data F j+1, and E is the ICP objective function constructed with the three-dimensional rotation matrix and the three-dimensional translation vector as variables. Minimizing E is the process of minimizing the ICP objective function; Qu, ¶0208, In the process of iterating over the ICP objective function, we can first initialize a value for R and t respectively, and then use all the matching point pairs between the first frame point cloud data and the second frame point cloud data to calculate and obtain an E. In subsequent iterations, the values of R and t are continuously optimized until the iteration ends. The R and t corresponding to the minimum value of E during the iteration process are then used as the first initial attitude transformation parameters).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li G, Gan Y, Liu G, Chen F. High-accuracy point cloud registration for 3D shape measurement based on double constrained intersurface mutual projections. Measurement. 2022 March 21;194:111050, hereinafter, “Li”, in view of Segal A, Haehnel D, Thrun S. Generalized-ICP. In Robotics: science and systems 2009 Jun 28 (Vol. 2, No. 4, p. 435), hereinafter, “Segal” as applied to claim 9 above, and further in view of Guan et al., Chinese Publication No. CN 111414798 A, hereinafter, “Guan”.
As per claim 10, Li and Segal disclose the registration method according to claim 9, but do not explicitly disclose the following limitations as further recited however Guan discloses wherein obtaining the registration point set on the surface of the first image model comprises:
obtaining a template image model, a surface of the template image model having a template point set (Guan, ¶0011, Perform point cloud computing on the head pose image aligned in step (I) to obtain the head point cloud data to be detected); and
matching the template image model with the first image model to project the template point set to the surface of the first image model to obtain the registration point set on the surface of the first image model (Guan, ¶0012, Substitute the point cloud data from step (II) into the 3D standard head model and register it with the point cloud of the 3D standard head model to complete the head pose detection; Guan, ¶0029, Let P be the point cloud of the 3D standard head model, and Q be the point cloud of the head pose to be detected; Guan, ¶0030, Let T be the homogeneous transformation matrix of the head pose point cloud Q to be detected relative to each point in the standard 3D head model point cloud P, as shown in equation (15); Guan, ¶0032, In equation (15), the rotation matrix describes the pose of any point Q in the head coordinate system {Q} relative to its corresponding point P in the coordinate system {P}, and describes the positional relationship between the origin of the head coordinate system {Q} and the origin of the coordinate system {P}; Guan, ¶0041, Figure 6 shows the derivation process of coarse registration ... As shown in Figure 6, the matrix T is the coarse registration result of the head pose; Guan, ¶0042, After coarse registration, an iterative nearest-point cloud registration (fine registration) algorithm is performed; Guan, ¶0043, Assuming the source dataset is S, and the head pose point cloud to be detected is the target dataset G, based on the coarse registration result T, the target dataset G is transformed by rotation and translation to update the target data G).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Guan with Li and Segal because they are in the same field of endeavor. One skilled in the art would have been motivated to include the model as taught by Guan in the system of Li and Segal in order to fine tune the registration parameters including pose parameters (Guan, Abstract).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li G, Gan Y, Liu G, Chen F. High-accuracy point cloud registration for 3D shape measurement based on double constrained intersurface mutual projections. Measurement. 2022 March 21;194:111050, hereinafter, “Li”, in view of Segal A, Haehnel D, Thrun S. Generalized-ICP. In Robotics: science and systems 2009 Jun 28 (Vol. 2, No. 4, p. 435), hereinafter, “Segal” as applied to claim 19 above, and further in view of Dai et al., Chinese Publication No. CN 114219717 A, hereinafter, “Dai”.
As per claim 20, Li and Segal disclose the system according to claim 19, but do not explicitly disclose the following limitation as further recited however Dai discloses wherein the medical scanning device comprises a computed tomography (CT) device or a positron emission computed tomography (PET) device (Dai, ¶0053, after scanning the CT images before surgery, 3D model data is obtained from the CT images before surgery, and point cloud is extracted from the model to serve as a reference point cloud. Then, CT images of the lesion area during the operation are acquired, and corresponding 3D model data is obtained through the CT images. Point cloud is extracted from the model and used as the point cloud to be registered; Dai, ¶0006 -0010, Based on the positional correspondence between the reference point cloud and the point cloud to be registered, a coarse registration point cloud is obtained. Based on the bidirectional iterative nearest point algorithm, the coarse registration point cloud is finely registered, and the registration result is obtained … the step of obtaining a coarsely registered point cloud based on the positional correspondence between a reference point cloud and a point cloud to be registered includes: Obtain the rotational correspondence and translation parameters between the point cloud to be registered and the reference point cloud; Based on the rotation correspondence and the translation parameters, a coarse registration point cloud is obtained).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Dai with Li and Segal because they are in the same field of endeavor. One skilled in the art would have been motivated to include scanners as taught by Dai in the system of Li and Segal in order to enable fine tuning of point cloud registration during medical procedures (Dai, ¶0053).
Allowable Subject Matter
Claim 7 is 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.
The following is a statement of reasons for the indication of allowable subject matter: Claim 7 is 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 claim because while the prior art discloses various means of registering point clouds, the prior art does not disclose the limitations, “wherein the registration method further comprises: if the registration number of the current registration reaches the first iteration number threshold, taking the second image model as the first image model, taking the second transformation matrix as the first transformation matrix, taking a second registration point set on the surface of the object to be registered as the first registration point set, and returning to perform the step of adjusting, based on the first transformation matrix, the first image model to obtain the second image model, until the registration number of the current registration reaches a second iteration number threshold; wherein respective registration points in the second registration point set are points other than the physiological and anatomical feature points on the surface of the object to be registered” as recited in dependent claim 7.
Conclusion
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
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