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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 23, 2026 has been entered.
Response to Amendment
This correspondence is in response to amendments filed on March 23, 2026. Claims 1, 15, 22, and 32 are amended. Claims 2, 4, 6, 10, 17, 22, 24, 29-30, 35, and 38 are filed as previously presented. Claims 3, 5, 7-9, 11-14, 16, 18-21, 23, 25-28, 31, 33-34, 36-37, and 39-40 are cancelled. Claims 41-44 are new. Claim 6 remains interpreted under 112(f) claim interpretations as “the process mapping system” was not amended to contain structural recitation within the claim. Claim 38 remains rejected under 35 U.S.C. 112a as no such amendment was made to “default trajectory” in the limitation. Amendments to claim 15 obviate the 112b rejections set forth in the previous action. Response to arguments regarding the prior art are included below.
Response to Arguments
Applicant argues that Hausler does not teach or suggest modifying the selected trajectories based on the approximated topography derived from sampled points (Remarks Pages 9-10). Examiner disagrees. The system of Hausler requires the area around the defect to be approximated according to a surface topography from sampled points. This topography corresponds directly to the surface from the CAD model which the defect and sampled surface is additionally fit to, as such sampling is localized on the CAD surface. The trajectory is modified according to the features of the defect as demonstrated in Figs. 6-9 which correspond to the sampled surface topography. As such, argument has been considered but is NOT PERSUASIVE.
Applicant further argues that Hausler alone does not teach the specified amended limitations inclusive of the number of sampled points, the least-squares bivariate polynomial fitting process, calculated principal curvature, calculated derivatives of curvature along principle axes, and zero-crossings of curvature to detect surface features (Remarks Page 10). Applicant’s arguments with respect to these amended features have been considered but are moot because the new ground of rejection does not rely on the same combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant further argues that other such amended features including the modification of the trajectory template and mapping of the modified trajectory to the fitted surface by orthogonal projection are not taught by Hausler (Remarks Page 10 and 11). Although these features are newly amended in the claim and in some respect do not rely on the same combination of references, Examiner disagrees with most of these assertions with regard to how trajectories are modified and mapped in Hausler. The system of Hausler requires the area around the defect to be approximated according to a surface topography. This topography corresponds directly to the surface from the CAD model which the defect and sampled surface is additionally fit to. Fig. 4 shows the mapping of the template corresponding to the defect plane Ei as orthogonally projected points Xi1’ and Xi2’ on the fit surface corresponding to the model (see [0039]). Additionally, the modification of the trajectory includes warping of the template as shown in Fig. 9 and Paragraph [0036] describes processing parameters which are position-dependent. These position-dependent processing parameters will be adjusted for the projected positions Xi1’ and Xi2’ as exemplified in Fig. 4 and determined as a function of the surface curvature given normal directions ni1’ and ni2’ which are additionally projected. As such, Applicant’s arguments have been considered but are NOT PERSUASIVE.
Applicant further argues that Hausler in view of Kim does not teach the required surface fitting or calculations of curvature/ derivative of curvature (Remarks Page 11-12). For those arguments regarding the 20 or fewer sampled surface points, Applicant’s arguments with respect to this feature have been considered but are moot because the new ground of rejection does not rely on the same combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. For arguments regarding the combination of references, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). Additionally, in response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Examiner ascertains that the teachings of Kim would reduce the computational complexity and computational time of approximating a surface using only the associated sampling method as supported by MPEP 2143.I(G) wherein the teaching, suggestion, or motivation of the prior art would have led one of ordinary skill to modify the prior art reference to arrive at the claimed invention. For the purpose of further clarity, Examiner includes additional rationale which indicates the modification as a simple substitution of one known element for another to obtain predictable results (MPEP 2143.I(B)). See below for corresponding rejection regarding the combination of Hausler in view of Kim. Therefore, arguments have been considered but are NOT PERSUASIVE.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 recites “The robotic system of claim 3…” in line 1. Applicant cancelled claim 3 and thus Examiner ascertains that Applicant further forgot to change the dependency corresponding to claim 4. Thus, Examiner recommends correcting claim 4 to read “The robotic system of claim 1…”.
Claim 4 further recites “…the surface engaging tool…” in lines 1-2. Claim 1 recites “a surface processing tool”. Although these tools are considered as equivalents with regard to the specification, Examiner recommends amending claim 4 to also read “…the surface processing tool…” such that the terminology is consistent between claims.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is:
“process mapping system” introduced in claim 6. The disclosure defines such a system as, “Process mapping system 600 may be built into a robot controller of a robotic repair unit, in some embodiments. In other embodiments, process mapping system 600 may be remote from robotic repair unit 670, as indicated in FIG. 6” [0068]. Thus, the process mapping system is a software extension of the robotic system and as such any software equivalent will be considered when reviewing the prior art.
Because this/these claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 38 and 44 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 38 recites the limitation “default trajectory” in lines 8 and 2-3 respectively. Although Applicant discloses functions of “retrieving a selected trajectory template” in Paragraph [0073] of the specification, a selected trajectory template would not necessarily be considered as a default trajectory. In other words, default trajectories may include selected trajectory templates, but selected trajectory templates are not inherently default trajectories. As such, there exists no adequate support for such default trajectory and Examiner will instead read the limitation to include trajectory template in its place when rejecting the below limitations.
Claim 44 recites the limitation “…wherein a location of the zero-crossing is estimated by weighted interpolation of the derivative values at the sampled points…” in lines 4-5. The specification is absent any description of a weighted interpolation to estimate zero-crossing locations. The specification is additionally absent any such mention of weightings for any purpose. Thus, Examiner does not find adequate support for such a limitation and will interpret what is meant by said limitation to the best of their ability in absence of a pertinent description.
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 1-2, 4, 6, 10, and 41-43 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the region” in line 10. There is insufficient antecedent basis for this limitation in the claim. No such region has been defined and therefore it is unclear which region is being referred to. Examiner best interprets this region to be the sampled area of the worksurface, and as such will read the claim instead as “…a surface topography of the sampled worksurface”.
Claims 2, 4, 6, 10, 41, 42, and 43 are rejected as being dependent on claim 1.
Claim 6 recites “…wherein the process mapping system…” in lines 1-2. There is insufficient antecedent basis for this limitation. No such process mapping system is recited in claim 1. As such, it is unclear which process mapping system is being referred to. Examiner ascertains such a system to be an additional function of the one or more processors executing stored instructions as defined in claim 1 and thus will interpret claim 6 as such.
Claim 41 recites “…the bivariate polynomial is a quadratic (or cubic) polynomial…” in lines 1-2. It is unclear whether the limitation included in parenthesis is to be included in the limitation or not. Examiner ascertains that these are considered as pure alternatives and as such will read the claim simply as “…the bivariate polynomial is a quadratic polynomial or a cubic polynomial.”
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.
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.
Claims 1-2, 4, 6, 10, 15, 17, 22, 24, 29-30, 32, 35, 38, 41, and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Hausler (US 2018/0326591 A1) in view of Kim (“Extraction of Ridge and Valley Lines from Unorganized Points”, 2012) and further in view of OriginLab Corp. (“An Introduction to the Polynomial Surface Fit App”, 2016).
Regarding claim 1, Hausler teaches a robotic system (System of Fig. 1 and Fig. 2 which include robots 31, 32, 33, and 34.) comprising:
a surface inspection system configured to acquire, within a radius of a defect on a worksurface, … sampled surface points (“In a first step, the automated surface inspection is performed (see FIG. 3, step S1) in order to detect potential surface defects (defect candidates) (e.g. by use of image processing techniques as such known) and to obtain, for each defect candidate and with use of 3D measurement of the workpiece surface, a point cloud (see FIG. 3, step S2), which represents the workpiece surface in the area of a surface defect” [0034]. “In addition to the extension of a surface defect, the steepness of a defect may also be relevant to the subsequent machining. This may be, e.g., characterized by a parameter of the set P.sub.i and may be, for example, the ratio of the area of a surface defect with respect to its height or depth t.sub.i or the ratio t.sub.i|d.sub.i or an average gradient (slope) of the surface structure in the area of the defect. Further, the position and the orientation of a defect D.sub.i are represented by a point O.sub.i on the surface of the workpiece and the respective normal vector n.sub.i. The point O.sub.i may designate, e.g., approximately the “center” (e.g. the centroid) of a surface defect. The normal vector n.sub.i defines a plane E.sub.i, which is also referred to as defect plane (see also FIGS. 4 and 6)” [0035]. Thus, the surface inspection system is configured to acquire sampled surface points in the area of a surface defect. Given that the defect has a measured extension, height/depth, and gradient, the machining path as shown in Fig. 6 is evaluated with a respective radius on the defect plane, extending from the center of the localized defect. The sampled surface points are thus acquired within this radius extending from the defect on the worksurface.)
a robotic arm comprising a force control unit coupled to an end effector (“An actuator 25, acting between tool 24 and TCP of the manipulator 34, allows for an arbitrary regulation of the force F in accordance with specifications which are stored in the mentioned database for a specific machining process” [0043]. Thus, there is an actuator, i.e., force control unit, which regulates the force at the TCP and is thus coupled to an end effector at the TCP.), wherein the end effector is coupled to a surface processing tool (“FIG. 2 shows a robot cell with a manipulator 34 that is equipped with a grinding tool 24 (e.g. an orbital grinding machine)” [0033]. Thus, the end effector is coupled to a grinding tool, i.e., surface processing tool.);
one or more processors with memory storing instructions (“the data processing device 50 may include one or more processors with a memory containing instructions that, when executed, cause the optical inspection system to perform the activities described herein. In one example, the data processing device 50 may include a workstation computer or a personal computer including interface modules (hardware and software) allowing communication with the optical inspection system, e.g. with the robots 31, 32, and 33, and the sensors 21, 22, and 23” [0032]. Thus, there is a data processing device which includes one or more processors with a memory containing instructions that perform the designated methods.) that, when executed, cause the processor to:
fit … a … surface to the sampled surface points to approximate a surface topography of the region (“The first result of a three-dimensional measurement of a defect candidate is a point cloud that describes the three-dimensional structure (the topography) of the relevant surface area. For each defect candidate, for example, its lateral extension (across the surface) and its height or depth (extension perpendicular to the surface) can be determined with great precision from the point clouds provided by the sensor heads 21, 22, and 23 (see also FIG. 3) using surface reconstruction” [0031]. Thus, the sampled surface points are reconstructed, i.e., fit as a surface, to approximate the surface topography of the relevant area around the defect.),
…retrieve, based on a defect characteristic, a trajectory template (“As mentioned above, each defect category K.sub.j is associated with exactly one machining process R.sub.j which may include one or more machining steps, wherein in each machining step the tool is moved, by use of a manipulator, along at least one machining path (see FIG. 2, tool 24, manipulator 34). These machining paths are stored (e.g. in the mentioned database) in the form of templates, which are defined in a plane (the defect plane) independently from the actual geometry of the workpiece” [0039]. Thus, there is a trajectory template that is retrieved based on a defect category, i.e., defect characteristic.) and modify the trajectory template based on the fitted surface topography and the detected surface feature by warping the template and adjusting one or more process parameters along the template as functions of a curvature magnitude (As shown in Fig. 9 and described in [0042], the template is transformed, i.e., warped, based on the fitted surface model, i.e., topography, and the detected edge, i.e., surface feature, which it is to avoid. As can be seen in Fig. 4, the projection determines machining path points as a function of a curvature magnitude which additionally varies the normal direction of the surface machining point. “A machining process R.sub.j for the machining of a defect D.sub.i of a specific defect category K.sub.j is defined by a the tool to be used and the machining steps to be performed with the tool. A machining step is defined by one or more machining paths, which are defined by base points, a path velocity with which the machining paths are to be run through, as well as time and/or position-dependent trigger points on the machining paths at which specifiable actions may be triggered (e.g. change of process parameters such as, e.g., contact pressure, rotational speed, activation of a rotational and/or eccentric motion of the grinding tool and the like)” [0036]. Given that the position of the machining points are transformed in the projection process, the position-dependent trigger points of the processing parameters such as contact pressure or rotational speed are additionally adjusted along the template as a function of the curvature magnitude which they are projected to. ); and
map the modified trajectory to the fitted surface by orthogonal projection (As seen in Fig. 4, the modified trajectory is fitted to the surface such that each point of the machining path is fit orthogonally. See [0039] which explains that the machining tool is always pressed perpendicular to the workpiece, thereby indicating that the path and associated parameters of the modified trajectory are orthogonal projections.) and generate a control signal comprising a path for the robotic arm, wherein the robotic arm is configured to cause the surface processing tool to engage the region of the worksurface based on the control signal (“…in accordance with the selected machining process, a robot program for the robot-assisted machining of the at least one defect is generated with computer assistance” [0008]. Thus, the robot program implements the machining of the defect via the robot manipulator over the designated machining path, providing the appropriate control signal to the controller 40 to machine the defect, i.e., engage the processing tool with the worksurface according to robot-assisted machining. The machining process is projected onto the model of the worksurface, i.e., modified, as is stated above.).
However, Hausler does not explicitly teach …a surface inspection system configured to acquire … twenty or fewer sampled surface points…
fit, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region;
compute, at each point on the fitted surface, a principal curvature and a derivative of curvature along principal axes and detect a surface feature by a zero-crossing in one of the derivatives of curvature…
Kim, pertinent to the problem at hand, teaches … fit, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region (“An MLS surface [1, 9] is implicitly defined as the set of points that project on to themselves under the MLS projection. Approximating this surface involves two steps: the approximation of a local reference plane and the fitting of a local bivariate polynomial to points projected on to that reference plane” (Section 3.2). Thus, there is a bivariate polynomial surface fit to the sampled surface points which approximate the surface topography of a region. MLS as indicated in the abstract represents a “moving least-squares” approximation.);
compute, at each point on the fitted surface, a principal curvature and a derivative of curvature along principal axes (“Assuming a dense smooth surface, it is possible to estimate the principal curvatures (k max and k min) and the principal directions (t max and t min) at a point r. Let k max and k min be the maximal and minimal curvatures respectively (k max > k min), with t max and t min as the corresponding principal directions. The derivatives of k max and k min in directions t max and t min are e max = ∇k max ⋅ t max and e min = ∇k min ⋅ t min” (Section 2). Thus, the disclosed technique computes a principle curvature kmax and kmin and principal axes tmax and tmin at each point r which results in a derivative of curvature emax and emin along the principle axes.) and
detect a surface feature by a zero-crossing in one of the derivatives of curvature (“Having found the maximal and minimal curvatures (k max and k min) and their derivatives (e max and e min) at each point r, we can detect ridges by finding the zero-crossings of curvature derivatives” (Section 3.5). Thus, the surface features, i.e., ridges, are detected by determining a zero-crossing of the curvature derivatives.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Hausler to include the calculated bivariate polynomial surface by least-squares and associated curvature and derivative of curvature in principal directions for the extraction of surface features as taught by Kim with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the approximation for the derivative of curvature as taught by Kim reduces the computational time for such an approximation (Kim, Page 269). Such a modification would be considered as a simple substitution in which the CAD model of Hausler could be substituted for the surface estimation as taught by Kim to obtain predictable results, given that Hausler already provides a three-dimensional estimation of the surface topography around the defect (MPEP 2143.I(B)).
However, Hausler as modified by Kim still does not teach … a surface inspection system configured to acquire … twenty or fewer sampled surface points…
OriginLab Corp., pertinent to the problem at hand, teaches a bivariate polynomial surface fitting procedure which uses only twenty data points for approximating the surface (see attached screen capture which shows a screenshot of the video at 19 seconds in which twenty sampled points are used for the estimation).
Therefore, it would have been obvious to one of ordinary skill in the art to have modified the surface inspection system of Hausler to acquire twenty or fewer sampled surface points to be fit to the surface as OriginLab Corp. exemplifies such a low resolution sampling as a sufficient amount of data for bivariate polynomial fitting over a designated region. One of ordinary skill in the art would have been motivated to make such a modification because by sampling fewer points, the surface estimation would be more efficient, thereby producing results faster and increasing work productivity of the automated system.
Regarding claim 2, Hausler as modified by Kim and OriginLab Corp teaches the robotic system of claim 1,
with Hausler further teaching wherein the worksurface is a vehicle (“…the surface of a workpiece 10, for example, a car body painted with base coat and primer” [0028]. Thus, a car, i.e., vehicle, is the example workpiece used in the prior art’s disclosure.).
Regarding claim 4, Hausler as modified by Kim and OriginLab Corp teaches the robotic system of claim 3,
with Hausler further teaching wherein the surface engaging tool comprises a sander or polishing tool (Grinding is synonymous with sanding and thus the grinding tool 24 may be considered to be a sander.).
Regarding claim 6, Hausler as modified by Kim and OriginLab Corp teaches the robotic system of claim 1,
with Hausler further teaching wherein the process mapping system is configured to map the selected trajectory onto the approximated surface topography (“To calculate the actual machining path X.sub.i the points of the template are projected (see FIG. 5, step S7) from the defect plane E.sub.i onto the workpiece surface (in accordance with the CAD model)” [0040]. Thus, the trajectory is projected, i.e., mapped, onto the model, i.e., approximated, surface.).
Regarding claim 10, Hausler as modified by Kim and OriginLab Corp. teaches the robotic system of claim 1,
with Hausler further teaching wherein the defect characteristic comprises a defect size, defect type, or defect severity (“In practice, relevant or useful criteria for the categorization of surface defects may be, e.g., the distinction of defects with regard to size categories (e.g. very small, small, medium, large), the distinction of defects with regard to their lateral extension (e.g. defined by the average or maximum radius of the defect), the distinction of flaws with regard to their extension perpendicular to the workpiece surface (e.g. an encapsulation (bulge) with a height of more than 5 μm, a crater (dent) with a depth of more than 10 μm, etc.)” [0037]. Thus, defect size and severity are described to be relevant to the categorization of such defects, i.e., defect characteristic, in which the categorization determines the machining path. Paragraph [0038] continues on regarding the type, frequency, and spatial arrangement of defects in determining the repair plan.).
Regarding claim 15, Hausler teaches a robotic control system comprising:
a surface sampling system configured to acquire within a radius of a target area on a worksurface, … sampled surface points (“In a first step, the automated surface inspection is performed (see FIG. 3, step S1) in order to detect potential surface defects (defect candidates) (e.g. by use of image processing techniques as such known) and to obtain, for each defect candidate and with use of 3D measurement of the workpiece surface, a point cloud (see FIG. 3, step S2), which represents the workpiece surface in the area of a surface defect” [0034]. “In addition to the extension of a surface defect, the steepness of a defect may also be relevant to the subsequent machining. This may be, e.g., characterized by a parameter of the set P.sub.i and may be, for example, the ratio of the area of a surface defect with respect to its height or depth t.sub.i or the ratio t.sub.i|d.sub.i or an average gradient (slope) of the surface structure in the area of the defect. Further, the position and the orientation of a defect D.sub.i are represented by a point O.sub.i on the surface of the workpiece and the respective normal vector n.sub.i. The point O.sub.i may designate, e.g., approximately the “center” (e.g. the centroid) of a surface defect. The normal vector n.sub.i defines a plane E.sub.i, which is also referred to as defect plane (see also FIGS. 4 and 6)” [0035]. Thus, the surface inspection system is configured to acquire sampled surface points in the area of a surface defect. Given that the defect has a measured extension, height/depth, and gradient, the machining path as shown in Fig. 6 is evaluated with a respective radius on the defect plane, extending from the center of the localized defect. The sampled surface points are thus acquired within this radius extending from the defect on the worksurface.); and
one or more processors with memory storing instructions (“the data processing device 50 may include one or more processors with a memory containing instructions that, when executed, cause the optical inspection system to perform the activities described herein. In one example, the data processing device 50 may include a workstation computer or a personal computer including interface modules (hardware and software) allowing communication with the optical inspection system, e.g. with the robots 31, 32, and 33, and the sensors 21, 22, and 23” [0032]. Thus, there is a data processing device which includes one or more processors with a memory containing instructions that perform the designated methods.) that, when executed, cause the processors to:
receive the sampled surface points and fit… a … surface to the sampled surface points to approximate a surface topography of the target area (“The first result of a three-dimensional measurement of a defect candidate is a point cloud that describes the three-dimensional structure (the topography) of the relevant surface area. For each defect candidate, for example, its lateral extension (across the surface) and its height or depth (extension perpendicular to the surface) can be determined with great precision from the point clouds provided by the sensor heads 21, 22, and 23 (see also FIG. 3) using surface reconstruction” [0031]. Thus, the sampled surface points are received and reconstructed, i.e., fit as a surface, to approximate the surface topography of the relevant area around the defect.);
…
retrieve, based on a defect characteristic, a trajectory template (“As mentioned above, each defect category K.sub.j is associated with exactly one machining process R.sub.j which may include one or more machining steps, wherein in each machining step the tool is moved, by use of a manipulator, along at least one machining path (see FIG. 2, tool 24, manipulator 34). These machining paths are stored (e.g. in the mentioned database) in the form of templates, which are defined in a plane (the defect plane) independently from the actual geometry of the workpiece” [0039]. Thus, there is a trajectory template that is retrieved based on a defect category, i.e., defect characteristic.) and modify the trajectory template based on the fitted surface topography and the detected surface feature by warping the template and adjusting one or more process parameters along the template as functions of curvature magnitude (As shown in Fig. 9 and described in [0042], the template is transformed, i.e., warped, based on the fitted surface model, i.e., topography, and the detected edge, i.e., surface feature, which it is to avoid. As can be seen in Fig. 4, the projection determines machining path points as a function of a curvature magnitude which additionally varies the normal direction of the surface machining point. “A machining process R.sub.j for the machining of a defect D.sub.i of a specific defect category K.sub.j is defined by a the tool to be used and the machining steps to be performed with the tool. A machining step is defined by one or more machining paths, which are defined by base points, a path velocity with which the machining paths are to be run through, as well as time and/or position-dependent trigger points on the machining paths at which specifiable actions may be triggered (e.g. change of process parameters such as, e.g., contact pressure, rotational speed, activation of a rotational and/or eccentric motion of the grinding tool and the like)” [0036]. Given that the position of the machining points are transformed in the projection process, the position-dependent trigger points of the processing parameters such as contact pressure or rotational speed are additionally adjusted along the template as a function of the curvature magnitude which they are projected to. );
map the modified trajectory to the fitted surface by orthogonal projection (As seen in Fig. 4, the modified trajectory is fitted to the surface such that each point of the machining path is fit orthogonally. See [0039] which explains that the machining tool is always pressed perpendicular to the workpiece, thereby indicating that the path and associated parameters of the modified trajectory are orthogonal projections.); and
generate robotic control signal comprising a path for a robotic system to navigate a surface modification tool mounted on a robotic arm to the target area (“…in accordance with the selected machining process, a robot program for the robot-assisted machining of the at least one defect is generated with computer assistance” [0008]. Thus, the robot program implements the machining of the defect via the robot manipulator over the designated machining path, providing the appropriate control signal to the controller 40 to machine the defect, i.e., engage the processing tool with the worksurface according to robot-assisted machining. The machining process is projected onto the model of the worksurface, i.e., modified, as is stated above.).
However, Hausler does not explicitly teach …a surface sampling system configured to acquire … twenty or fewer sampled surface points…
fit, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region;
compute, at each point on the fitted surface, a principal curvature and a derivative of curvature along principal axes and detect a surface feature by a zero-crossing in one of the derivatives of curvature…
Kim, pertinent to the problem at hand, teaches … fit, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region (“An MLS surface [1, 9] is implicitly defined as the set of points that project on to themselves under the MLS projection. Approximating this surface involves two steps: the approximation of a local reference plane and the fitting of a local bivariate polynomial to points projected on to that reference plane” (Section 3.2). Thus, there is a bivariate polynomial surface fit to the sampled surface points which approximate the surface topography of a region. MLS as indicated in the abstract represents a “moving least-squares” approximation.);
compute, at each point on the fitted surface, a principal curvature and a derivative of curvature along principal axes (“Assuming a dense smooth surface, it is possible to estimate the principal curvatures (k max and k min) and the principal directions (t max and t min) at a point r. Let k max and k min be the maximal and minimal curvatures respectively (k max > k min), with t max and t min as the corresponding principal directions. The derivatives of k max and k min in directions t max and t min are e max = ∇k max ⋅ t max and e min = ∇k min ⋅ t min” (Section 2). Thus, the disclosed technique computes a principle curvature kmax and kmin and principal axes tmax and tmin at each point r which results in a derivative of curvature emax and emin along the principle axes.) and
detect a surface feature by a zero-crossing in one of the derivatives of curvature (“Having found the maximal and minimal curvatures (k max and k min) and their derivatives (e max and e min) at each point r, we can detect ridges by finding the zero-crossings of curvature derivatives” (Section 3.5). Thus, the surface features, i.e., ridges, are detected by determining a zero-crossing of the curvature derivatives.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Hausler to include the calculated bivariate polynomial surface by least-squares and associated curvature and derivative of curvature in principal directions for the extraction of surface features as taught by Kim with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the approximation for the derivative of curvature as taught by Kim reduces the computational time for such an approximation (Kim, Page 269). Such a modification would be considered as a simple substitution in which the CAD model of Hausler could be substituted for the surface estimation as taught by Kim to obtain predictable results, given that Hausler already provides a three-dimensional estimation of the surface topography around the defect (MPEP 2143.I(B)).
However, Hausler as modified by Kim still does not teach … a surface sampling system configured to acquire … twenty or fewer sampled surface points…
OriginLab Corp., pertinent to the problem at hand, teaches a bivariate polynomial surface fitting procedure which uses only twenty data points for approximating the surface (see attached screen capture which shows a screenshot of the video at 19 seconds in which twenty sampled points are used for the estimation).
Therefore, it would have been obvious to one of ordinary skill in the art to have modified the surface inspection system of Hausler to acquire twenty or fewer sampled surface points to be fit to the surface as OriginLab Corp. exemplifies such a low resolution sampling as a sufficient amount of data for bivariate polynomial fitting over a designated region. One of ordinary skill in the art would have been motivated to make such a modification because by sampling fewer points, the surface estimation would be more efficient, thereby producing results faster and increasing work productivity of the automated system.
Regarding claim 17, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Hausler further teaching wherein the detected feature is overlayed over the approximated surface or the sampled surfaces (“Dependent on the geometry of the workpiece, certain areas of the workpiece surface may not be able to be machined (e.g. design edges and the like). Such “forbidden areas” of the workpiece surface may be marked in the CAD model, for example, as a set of edges (depicted as spread lines), which must not overlap with a machining area (see FIG. 9, edge 11)” [0042]. Thus, the edge is marked, i.e., overlayed over, the approximated surface, i.e., CAD model.).
Regarding claim 22, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Hausler further teaching wherein the worksurface is a vehicle surface (“…the surface of a workpiece 10, for example, a car body painted with base coat and primer” [0028]. Thus, a car, i.e., vehicle, is the example workpiece used in the prior art’s disclosure.),
the target area comprises a defect (“The purpose of the surface inspection is a detection (this includes a localization) of surface defects and a three-dimensional measurement of at least those areas of the workpiece surface in or on which a defect has been detected” [0028]. Thus, the area which is inspected, i.e., target area, is determined to comprise a defect.), and
wherein the surface feature is a valley, a ridge, an area of increased curvature, an area of convex curvature, an area of concave curvature or an area of rapid transition between convex and concave curvature on the vehicle surface proximate the defect (The surface feature is determined as an “edge” which may be synonymous with a ridge. This edge is shown as edge 11 in Fig. 9.).
Regarding claim 24, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Hausler as modified by Kim further teaching …a repair evaluator that, based on the approximated curvature and approximated derivative of curvature, classifies an area as robotically repairable or not robotically repairable (Kim teaches the determination of a valley or ridge based on the approximated curvature and derivative of curvature. The edge (11) of Fig. 9 in Hausler may be considered as the determined ridge. “Dependent on the geometry of the workpiece, certain areas of the workpiece surface may not be able to be machined (e.g. design edges and the like). Such “forbidden areas” of the workpiece surface may be marked in the CAD model, for example, as a set of edges (depicted as spread lines), which must not overlap with a machining area (see FIG. 9, edge 11)” [0042]. Thus, features such as the edge 11 are determined as “forbidden areas” which may not be machined, and therefore are not robotically repairable. All other such regions of the worksurface have been determined to be robotically repairable, as such other areas are provided the trajectory projections for the robot to perform the machining. See also [0038] which discusses other criteria for determining whether or not defects are machined.).
Regarding claim 29, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Hausler further teaching wherein the modified trajectory includes a modified tool force, disk speed, or tool speed (“The controller 40 does not only set the trajectory of the robot but also the tool-dependent parameters relevant to the repair process such as, e.g., contact pressure of the grinding tool 24, rotational speed or velocity of the abrasives and the like” [0033]. Thus, the control of the path/trajectory is dependent upon contact pressure (force), rotational (disk) speed, and velocity (tool speed).).
Regarding claim 30, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Hausler further teaching wherein the trajectory template is selected based on a defect size, defect location, defect type or defect severity (“In practice, relevant or useful criteria for the categorization of surface defects may be, e.g., the distinction of defects with regard to size categories (e.g. very small, small, medium, large), the distinction of defects with regard to their lateral extension (e.g. defined by the average or maximum radius of the defect), the distinction of flaws with regard to their extension perpendicular to the workpiece surface (e.g. an encapsulation (bulge) with a height of more than 5 μm, a crater (dent) with a depth of more than 10 μm, etc.)” [0037]. Thus, defect size and severity are described to be relevant to the categorization of such defects, in which the categorization determines the machining path. Paragraph [0038] continues on regarding the type, frequency, and spatial arrangement of defects in determining the repair plan.).
Regarding claim 32, Hausler teaches a method of removing material from a worksurface (“The present disclosure generally relates to the field of industrial robots, in particular to a system and a method for the automated detection of defects in surfaces (e.g. painting defects on a car body) and the robot-assisted machining thereof, in particular by grinding and polishing” [0002]. Thus, there is such a method of machining defects in a worksurface by grinding and polishing which equates to removing of material.), the method comprising:
identifying a target area on the worksurface for material removal (“The purpose of the surface inspection is a detection (this includes a localization) of surface defects and a three-dimensional measurement of at least those areas of the workpiece surface in or on which a defect has been detected” [0028]. Thus, a detected defect determines the target area of the worksurface.), wherein identifying comprises imaging the target area with an image capturing device (“In the present example, each of the sensor heads includes an LCD monitor (for illumination), a plurality of (e.g. four) cameras, and a controller unit. With the use of the LCD monitor structured light may be generated for the illumination of the workpiece surface, which is imaged by high-resolution cameras. The structured light generated by the LCD monitor has a stripe pattern with a sinusoidal brightness modulation which is projected onto the workpiece. The resulting reflected pattern is captured—for different phase shifts of the stripe pattern—by the cameras of the respective sensor heads 21, 22, and 23, and the captured images are evaluated to determine the coordinates of surface defects (“defect candidates”, to be precise) on the surface of the workpiece” [0029]. Thus, defect candidates which are localized are determined by imaging the worksurface using cameras, i.e., image capturing devices, of sensor heads 21, 22, and 23.);
acquiring, within a radius of the target area, … sampled surface points (“In a first step, the automated surface inspection is performed (see FIG. 3, step S1) in order to detect potential surface defects (defect candidates) (e.g. by use of image processing techniques as such known) and to obtain, for each defect candidate and with use of 3D measurement of the workpiece surface, a point cloud (see FIG. 3, step S2), which represents the workpiece surface in the area of a surface defect” [0034]. “In addition to the extension of a surface defect, the steepness of a defect may also be relevant to the subsequent machining. This may be, e.g., characterized by a parameter of the set P.sub.i and may be, for example, the ratio of the area of a surface defect with respect to its height or depth t.sub.i or the ratio t.sub.i|d.sub.i or an average gradient (slope) of the surface structure in the area of the defect. Further, the position and the orientation of a defect D.sub.i are represented by a point O.sub.i on the surface of the workpiece and the respective normal vector n.sub.i. The point O.sub.i may designate, e.g., approximately the “center” (e.g. the centroid) of a surface defect. The normal vector n.sub.i defines a plane E.sub.i, which is also referred to as defect plane (see also FIGS. 4 and 6)” [0035]. Thus, the system acquires sampled surface points in the area of a surface defect. Given that the defect has a measured extension, height/depth, and gradient, the machining path as shown in Fig. 6 is evaluated with a respective radius on the defect plane, extending from the center of the localized defect. The sampled surface points are thus acquired within this radius extending from the defect on the worksurface.);
fitting… a … surface to the sampled surface points to approximate a surface topography of the target area (“The first result of a three-dimensional measurement of a defect candidate is a point cloud that describes the three-dimensional structure (the topography) of the relevant surface area. For each defect candidate, for example, its lateral extension (across the surface) and its height or depth (extension perpendicular to the surface) can be determined with great precision from the point clouds provided by the sensor heads 21, 22, and 23 (see also FIG. 3) using surface reconstruction” [0031]. Thus, the sampled surface points are reconstructed, i.e., fit as a surface, to approximate the surface topography of the relevant area around the defect.);
…
retrieving, based on a defect characteristic, a trajectory template (“As mentioned above, each defect category K.sub.j is associated with exactly one machining process R.sub.j which may include one or more machining steps, wherein in each machining step the tool is moved, by use of a manipulator, along at least one machining path (see FIG. 2, tool 24, manipulator 34). These machining paths are stored (e.g. in the mentioned database) in the form of templates, which are defined in a plane (the defect plane) independently from the actual geometry of the workpiece” [0039]. Thus, there is a trajectory template that is retrieved based on a defect category, i.e., defect characteristic.) and modifying the trajectory template based on the fitted surface topography and the detected surface feature by warping the template and adjusting one or more process parameters along the template as functions of a curvature magnitude (As shown in Fig. 9 and described in [0042], the template is transformed, i.e., warped, based on the fitted surface model, i.e., topography, and the detected edge, i.e., surface feature, which it is to avoid. As can be seen in Fig. 4, the projection determines machining path points as a function of a curvature magnitude which additionally varies the normal direction of the surface machining point. “A machining process R.sub.j for the machining of a defect D.sub.i of a specific defect category K.sub.j is defined by a the tool to be used and the machining steps to be performed with the tool. A machining step is defined by one or more machining paths, which are defined by base points, a path velocity with which the machining paths are to be run through, as well as time and/or position-dependent trigger points on the machining paths at which specifiable actions may be triggered (e.g. change of process parameters such as, e.g., contact pressure, rotational speed, activation of a rotational and/or eccentric motion of the grinding tool and the like)” [0036]. Given that the position of the machining points are transformed in the projection process, the position-dependent trigger points of the processing parameters such as contact pressure or rotational speed are additionally adjusted along the template as a function of the curvature magnitude which they are projected to. );
mapping the modified trajectory to the fitted surface (As seen in Fig. 4, the modified trajectory is fitted to the surface such that each point of the machining path is fit orthogonally. See [0039] which explains that the machining tool is always pressed perpendicular to the workpiece, thereby indicating that the path and associated parameters of the modified trajectory are orthogonal projections.);
generating and transmitting to a robotic material removal system a control signal comprising the mapped path; and causing the robotic material removal system to navigate a surface engaging tool mounted on a robotic arm along the mapped path to remove material in the target area (“…in accordance with the selected machining process, a robot program for the robot-assisted machining of the at least one defect is generated with computer assistance” [0008]. Thus, the robot program implements the machining of the defect via the robot manipulator over the designated machining path which is transmitted to the controller, thus providing the appropriate control signal to the controller 40 to machine the defect, i.e., engage the processing tool with the worksurface according to robot-assisted machining. The machining process is projected onto the model of the worksurface, i.e., modified, as is stated above.).
However, Hausler does not explicitly teach … acquiring … twenty or fewer sampled surface points…
fitting, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region;
computing, at each of the sampled surface points on the fitted surface, a principal curvature and a derivative of curvature along principal axes and detecting a surface feature by a zero-crossing in one of the derivatives of curvature…
Kim, pertinent to the problem at hand, teaches fitting, by least squares, a bivariate polynomial surface to the sampled surface points to approximate a surface topography of the region (“An MLS surface [1, 9] is implicitly defined as the set of points that project on to themselves under the MLS projection. Approximating this surface involves two steps: the approximation of a local reference plane and the fitting of a local bivariate polynomial to points projected on to that reference plane” (Section 3.2). Thus, there is a bivariate polynomial surface fit to the sampled surface points which approximate the surface topography of a region. MLS as indicated in the abstract represents a “moving least-squares” approximation.);
computing, at each of the sampled surface points on the fitted surface, a principal curvature and a derivative of curvature along principal axes (“Assuming a dense smooth surface, it is possible to estimate the principal curvatures (k max and k min) and the principal directions (t max and t min) at a point r. Let k max and k min be the maximal and minimal curvatures respectively (k max > k min), with t max and t min as the corresponding principal directions. The derivatives of k max and k min in directions t max and t min are e max = ∇k max ⋅ t max and e min = ∇k min ⋅ t min” (Section 2). Thus, the disclosed technique computes a principle curvature kmax and kmin and principal axes tmax and tmin at each point r which results in a derivative of curvature emax and emin along the principle axes.) and
detecting a surface feature by a zero-crossing in one of the derivatives of curvature (“Having found the maximal and minimal curvatures (k max and k min) and their derivatives (e max and e min) at each point r, we can detect ridges by finding the zero-crossings of curvature derivatives” (Section 3.5). Thus, the surface features, i.e., ridges, are detected by determining a zero-crossing of the curvature derivatives.)…
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Hausler to include the calculated bivariate polynomial surface by least-squares and associated curvature and derivative of curvature in principal directions for the extraction of surface features as taught by Kim with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because the approximation for the derivative of curvature as taught by Kim reduces the computational time for such an approximation (Kim, Page 269). Such a modification would be considered as a simple substitution in which the CAD model of Hausler could be substituted for the surface estimation as taught by Kim to obtain predictable results, given that Hausler already provides a three-dimensional estimation of the surface topography around the defect (MPEP 2143.I(B)).
However, Hausler as modified by Kim still does not teach acquiring… twenty or fewer sampled surface points…
OriginLab Corp., pertinent to the problem at hand, teaches a bivariate polynomial surface fitting procedure which uses only twenty data points for approximating the surface (see attached screen capture which shows a screenshot of the video at 19 seconds in which twenty sampled points are used for the estimation).
Therefore, it would have been obvious to one of ordinary skill in the art to have modified the surface inspection system of Hausler to acquire twenty or fewer sampled surface points to be fit to the surface as OriginLab Corp. exemplifies such a low resolution sampling as a sufficient amount of data for bivariate polynomial fitting over a designated region. One of ordinary skill in the art would have been motivated to make such a modification because by sampling fewer points, the surface estimation would be more efficient, thereby producing results faster and increasing work productivity of the automated system.
Regarding claim 35, Hausler as modified by Kim and OriginLab Corp. teaches the method of claim 32,
with Hausler further teaching wherein the target area comprises a defect (“The purpose of the surface inspection is a detection (this includes a localization) of surface defects and a three-dimensional measurement of at least those areas of the workpiece surface in or on which a defect has been detected” [0028]. Thus, the area which is inspected, i.e., target area, is determined to comprise a defect.).
Regarding claim 38, Hausler as modified by Kim and OriginLab Corp. teaches the method of claim 32.
However, Hausler as modified does not explicitly teach …wherein the modified surface processing trajectory has a smaller area than the trajectory template.
The disclosure does however note, “The template may be adapted to the defect D.sub.i dependent on its lateral extension, e.g. by means of transformation by shifting, rotating, scaling or skewing or an arbitrary combination of shifting, rotating, scaling and skewing” [0041]. Thus, the transformation of such a template would lead the resulting area of the modified movement path to be either larger, smaller, or the same as the area of the original movement path based on the results of the template, surface model, parameters of the defect, and the transformation. Such modification would be obvious as a result-effective variable (see 2144.05.II(B)).
Regarding claim 41, Hausler as modified by Kim and OriginLab Corp. teaches the robotic system of claim 1,
with Kim further teaching wherein the bivariate polynomial is a quadratic (or cubic) polynomial (Section 3.2 describes the bivariate polynomial to be of degree m with the example explicitly described as a third-degree polynomial, i.e., cubic.).
Regarding claim 43, Hausler as modified by Kim and OriginLab Corp teaches the robotic system of claim 1,
with Hausler in view of Kim further teaching wherein the system classifies the region as robotically repairable when curvature magnitude and curvature-derivative metrics are below respective thresholds (Kim Section 2 describes definitions regarding the curvature magnitude and curvature-derivative metrics for defining ridges and valleys. For those metrics which are below the respective thresholds and do not qualify as either a ridge or a valley, the surface would be considered as “smooth” since neither extreme is met. The edge (11) of Fig. 9 in Hausler may be considered as the determined ridge. “Dependent on the geometry of the workpiece, certain areas of the workpiece surface may not be able to be machined (e.g. design edges and the like). Such “forbidden areas” of the workpiece surface may be marked in the CAD model, for example, as a set of edges (depicted as spread lines), which must not overlap with a machining area (see FIG. 9, edge 11)” [0042]. Thus, features such as the edge 11 are determined as “forbidden areas” which may not be machined, and therefore are not robotically repairable. All other such regions of the worksurface have been determined to be robotically repairable, as such other areas are provided the trajectory projections for the robot to perform the machining. See also [0038] which discusses other criteria for determining whether or not defects are machined.).
Claims 42 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Hausler in view of Kim, further in view of OriginLab Corp., and further in view of Southern (“Principal Curvature Aligned Anisotropic Shading”, 2018).
Regarding claim 42, Hausler as modified by Kim and OriginLab Corp. teaches the robotic system of claim 1.
However, Hausler as modified does not currently teach …wherein detecting the surface feature comprises aligning principal curvature directions between neighboring sample points to prevent vector flipping and identifying a zero-crossing in the derivative of curvature.
Although Kim teaches the identification of zero-crossing in the derivative of curvature, no such explanation regarding an alignment of principle curvatures has been contemplated in this work as the principal curvature directions are already provided as givens.
In similar work, Southern, pertinent to the problem at hand, teaches an alignment process using triangular meshes which aligns vertex properties of independent triangles such that each face is aligned and vector flipping is avoided (See “Curvature Direction Correction on the GPU” which Examiner considers as most pertinent section).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date to have further modified Hausler in view of Kim to include the alignment process as taught by Southern with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by aligning the principle curvature directions between neighboring samples in an attempt to avoid vector flipping, the reconstructed surface features will further have a consistent flow over the surface (see Southern, “Early Results”) such that ridges defined on a smooth surface may be more accurately detected. Such a modification is further considered as a combination of known methods to yield predictable results (see MPEP 2143.I(A)).
Regarding claim 44, Hausler as modified by Kim and OriginLab Corp. teaches the system of claim 15,
with Kim further teaching …wherein a location of the zero-crossing is estimated by weighted interpolation of the derivative values at the sampled points (See Sections 3.5 and 4.1 which may be considered as a weighted interpolation of derivative values on the sampled points to determine the location of zero-crossings as sampled ridge points.).
However, Hausler as modified does not currently teach …wherein detecting the surface feature comprises aligning principal axes of curvature between neighboring sampled points to prevent eigenvector flipping and identifying a zero-crossing based on a sign change in the derivative of curvature…
Although Kim teaches the identification of zero-crossing in the derivative of curvature, no such explanation regarding an alignment of principle curvatures has been contemplated in this work as the principal curvature directions are already provided as givens.
In similar work, Southern, pertinent to the problem at hand, teaches an alignment process using triangular meshes which aligns vertex properties of independent triangles such that each face is aligned and vector flipping is avoided (See “Curvature Direction Correction on the GPU” which Examiner considers as most pertinent section).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date to have further modified Hausler in view of Kim to include the alignment process as taught by Southern with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because by aligning the principle curvature directions between neighboring samples in an attempt to avoid vector flipping, the reconstructed surface features will further have a consistent flow over the surface (see Southern, “Early Results”) such that ridges defined on a smooth surface may be more accurately detected. Such a modification is further considered as a combination of known methods to yield predictable results (see MPEP 2143.I(A)).
Conclusion
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/S.L.M./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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