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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 2, 2026 has been entered.
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.
Claims 12, 14, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gripp et al (US-PGPUB 20190287237), in view of Richardson et al, (US-PGPUB 20190096057); and further in view of Matthews et al, (US-PGPUB 20030139836); and further in view of Paquin et al, (US-PGPUB 20180361589)
In regards to claim 12, Gripp discloses an orange peel imaging system, (Figs. 8a, 8b), comprising:
an orange peel light source configured to direct light at an area on a worksurface, wherein the area includes a known defect, (see at least: Par. 0097, using light source 810 for inspection function, and reflects light patterns on the inspected material, including paint defects such as dirt, scratches, bubbles and surface roughness, “wherein the area includes a known defect”);
an orange peel camera configured to capture an orange peel image of the area, (see at least: Par. 0097, using second image-capturing system composed by one or more video and/or photographic camera, infrared camera, or an ultraviolet camera or any array of electromagnetic sensors capable to capture an image for detecting paint defects such as dirt or bubbles, “an orange peel image of the area”);
an orange peel processor being configured to analyze the orange peel image, detect a contour in the area indicative of orange peel, calculate a delta value for the detected orange peel contour, and output the calculated orange peel delta value, (see at least: Par. 0097, bubbles or fragments in the paint can be found by processing the image of the light and dark patterns made by the light source 810 reflected on the part 820 and by implementing a software in device 850 for checking the appearance of dark spots within the clear patterns and clear points where there should be no light reflection; and the paint roughness of the part 820 can also be assessed by the ripple and degradation of the transition edges between the light and dark patterns in the captured image, [i.e., analyzing the orange peel image to assess the paint roughness of the part 820 by the ripple and degradation of the transition edges between the light and dark patterns in the captured image, “analyzing the orange peel image to assess the paint roughness indicative of orange peel”]).
However, while disclosing assess the paint roughness of the part 820 by the ripple and degradation of the transition edges between the light and dark patterns in the captured image, (Par 0097); Gripp does not expressly disclose detecting a contour in the area indicative of orange peel, calculate a delta value for the detected orange peel contour, and output the calculated orange peel delta value.
Richardson discloses detecting a contour in the area indicative of orange peel, calculate a delta value for the detected orange peel contour, and output the calculated orange peel delta value, (see at least: Par. 0411-0414, creating the outer edge image 2003, shown in FIG. 110, and the inner edge image 2004, which is shown in FIG. 111, “implicitly detecting a contour in the area indicative of orange peel”; where the algorithm 3000 inputs an edge image (either the top_edge_image or the bottom edge image) and returns a score, and the orange_edge_score is calculated using the ScoreEdge algorithm for both the top_edge_image and the bottom edge image, and these two scores are averaged together to create the final orange_peel_score, [i.e., calculate a delta value, “final orange_peel_score”, for the detected orange peel contour, “(the top_edge_image and the bottom_edge_image”]). When orange peel is located, the information can be added to an orange peel defect record, along with the centroid point of the light bar, and this information can be used for reporting about the orange peel; and from Par. 0156-0157, post processing information is then communicated to the historical or NAS server 70 where it is stored for archival purposes and the data is also made available to the display computer 50 in order that operators may use the output monitor 52 to view, either in “real time” the identified defects or to view historical defect data residing within the NAS server 70, [i.e., the calculated orange peel delta value technically represents the post processing information, as the orange peel information “score” can be added to an orange peel defect record, and which can be made available to the operators for viewing in “real time” using the output monitors 52]);
Gripp and Richardson are combinable because they are both concerned with an automatic workpiece inspection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Gripp, to use the ScoreEdge algorithm, as though by Richardson, in order to calculate the orange_edge_score for both the top_edge_image and the bottom edge image using the ScoreEdge algorithm, and these two scores are averaged together to create the final orange_peel_score, (Richardson, Par. 0413).
The combine teaching Gripp and Richardson as whole does not expressly disclose a motive robotic arm configured to move with respect to a worksurface; that the orange peel light source, being mounted to the motive robotic arm, and wherein the area includes a known surface defect; and that the orange peel camera being mounted to the motive robotic arm; and that the orange peel processor communicably coupled to the orange peel camera.
However, Matthews discloses a motive robotic arm configured to move with respect to a worksurface, (see at least: Par. 0018-0019, the automated robots 28 can be programmed to approach the surface of the vehicle body 16 along the normal vector to ensure even forces across the sanding pad or other tool, [i.e., a motive robotic arm, “one or more automated robots 28”, being configured to move with respect to a worksurface, “approaching the surface of the vehicle body 16”]); and wherein the area includes a known surface defect, (see at least: Par. 0007, paint defect data and the paint defect coordinates are stored with reference to the vehicle body; and Par. 0016, the defect data 14 can be referenced to the vehicle body 16, [i.e., wherein the area, “surface of vehicle body 16”, includes a known surface defect, “paint defect data and the paint defect coordinates”]); and that the orange peel processor communicably coupled to the orange peel camera, (see at least: Fig. 1, Par. 0017, the robot cell controller 24 is implicitly in communication with the vision cell controller 18, and the imaging system 12, [i.e., the orange peel processor, “the robot cell controller 24”, is implicitly communicably coupled to the orange peel camera, “as shown in Fig. 1, the robot cell controller 24 is implicitly communicably coupled to the imaging system 12, via the vision cell controller 18”]).
Gripp, Richardson, and Matthews are combinable because they are both concerned with an automatic workpiece inspection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Gripp and Richardson, to include the robot cell controller 24, and the automated robot(s) 28, as though by Matthews, in order to accomplish a variety of tasks including sanding and polishing the paint defect for the vehicle body 16, (Matthews, Par. 0018)
The combine teaching Gripp, Richardson, and Matthews as whole does not expressly disclose that the orange peel light source, being mounted to the motive robotic arm; and that the orange peel camera being mounted to the motive robotic arm.
However, Paquin discloses that the orange peel light source, being mounted to the motive robotic arm; and that the orange peel camera being mounted to the motive robotic arm, (see at least: Fig. 1, and Par. 0012, robotic arm with an arm-mounted camera able to acquire images of the working area; and from Figs. 2-3, and Par. 0029, the camera 50 can be integrated to the robotic arm system 15, where the camera 50 …. accommodating camera optics 53 with a pair of lighting sources 55L and 55R arranged to each side of the camera optics 53, [i.e., the orange peel light source, being mounted to the motive robotic arm, “pair of lighting sources 55L and 55R are mounted to the motive robotic arm, implicitly via the camera optics 53”; and that the orange peel camera being mounted to the motive robotic arm, “camera 50 being integrated to the robotic arm system 15”]).
Gripp, Richardson, Matthews, and Paquin are combinable because they are all concerned with an automatic repair and/or inspection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Gripp, Richardson, and Matthews, to mount the camera 50, and the pair of lighting sources 55L and 55R, on the robotic arm system 15, as though by Paquin, in order to acquire images of the working surface, (Paquin, Fig. 1, and Par. 0012).
In regards to claim 14, the combine teaching Gripp, Richardson, Matthews, and Paquin as whole discloses the limitations of claim 12.
Furthermore, Gripp discloses a mount configured to couple the orange peel camera to a robotic repair unit, (see at least: see at least: Figs. 7A-7B, and Par. 0093, image-capturing device, “camera 730”, can be fixed or mobile, for example using a robotic arm 740. See also, Fig. 8B, the optical inspection system 800 may be fixed or movable, for example, by using a robotic arm 840, “coupling the orange peel camera to a robotic repair unit implicitly using a mount shown in Fig. 8B”).
In regards to claim 17, the combine teaching Gripp, Richardson, Matthews, and Paquin as whole discloses the limitations of claim 12.
Furthermore, Richardson discloses wherein analyzing the orange peel image comprises sampling pixels in the orange peel image, (Richardson, see at least: Par. 0410-0414, the algorithm extracts a percentile table mapping each light bar image pixel intensity value to a percentile value, and the pixel values having an intensity less than the threshold of 30 are ignored for the percentile table calculation; and creating the outer edge image 2003, shown in FIG. 110, by converting all pixels in light bar image having intensity values in the percentile_table less than the 10.sup.th percentile to 0, and then setting all other pixels equal to 255, [i.e., implicitly sampling pixels in the orange peel image]).
In regards to claim 20, the combine teaching Gripp, Richardson, Matthews, and Paquin as whole discloses the limitations of claim 12.
a repair generation system configured to generate a repair strategy for the defect, and wherein the repair strategy is generated at least in part by the analysis of the orange peel image, (see at least: Par. 0095-0096, device is used for processing the captured images, identifying and classifying the defects of the inspected material, and creating inspection report of the inspected material; and from Par. 0096, the inspection report evidencing the defects of the part 820 is shown, for example, in one or more monitor 860, which may be close to the optical system 800 or in another location, for example, where the quality information is useful to correct defects in the inspected materials 820, [i.e., generate a repair strategy for the defect, “creating inspection report of the inspected material”, and wherein the repair strategy is generated at least in part by the analysis of the orange peel image, “processing the captured images, identifying and classifying the defects of the inspected material”]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Gripp, Richardson, Matthews, and Paquin, as applied to claims 1 and 12 above; and further in view of Saphier et al, (US-PGPUB 20160028936)
The combine teaching Gripp, Richardson, Matthews, and Paquin as whole discloses the limitations of claim 12.
The combine teaching Gripp, Richardson, Matthews, and Paquin as whole does not expressly disclose wherein analyzing the orange peel image comprises smearing the orange peel image.
Saphier discloses smear an image, (see at least: Par. 0108, the camera and/or DMS provide auto-focus illumination pulse 720, e.g., with pattern generators 350, 351, or 352 for capturing an auto-focus image, using the auto-focus illumination pulse 720 has a longer duration, which provides smearing of an image during scanning which is typically desirable while capturing an auto-focus image, [i.e., smearing the image]).
Gripp, Richardson, Matthews, Paquin, and Saphier are combinable because they are all concerned with workpiece inspection. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combine teaching Gripp, Richardson, Matthews, and Paquin, to apply the DMS, as though by Saphier to the Richardson’s orange peel image, for capturing an auto-focus image using the auto-focus illumination pulse, which provides smearing of an image, (Saphier, Par. 0108).
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
-- Claim 1 is allowable over the prior art of record
-- Claims 2-7, and 11 are allowable as they depend from claim 1.
With respect to claim 1, the prior art of record, alone or in reasonable
combination, does not teach or suggest, the following underlined limitation(s), (in consideration of the claim as a whole):
“a defect repair strategy modifier configured to modify the selected repair strategy template based on the orange peel characterization of the worksurface; and a defect repair tool, mounted to the motive robotic arm, configured to contact the worksurface and execute the modified repair strategy”.
The relevant prior art of record, Gripp et al (US-PGPUB 20190287237) discloses an imaging and repair system, (see at least: Figs. 8A, 8B), comprising:
a first imaging system configured to image and detect a defect on a worksurface wherein the first imaging system comprises a first camera configured to capture a plurality of first images of the worksurface, wherein the plurality of first images are stored in a data source, (see at least: Par. 0097, a first image-capturing system, for example, wherein one or more cameras are used for the inspection of unwanted waviness and dents with a diameter in the order of centimeters, [i.e., a first imaging system configured to image and detect a defect on a worksurface, wherein the first imaging system comprises a first camera configured to capture a plurality of first images of the worksurface, “one or more cameras implicitly acquires one or more images and used for the inspection of unwanted waviness and dents”]; and from Par. 0095, all data and pictures generated by the equipment and software described in this patent application can be stored … for documentation or for the improvement of methods and systems for the automatic inspection of material quality, [i.e., wherein the plurality of first images are stored in a data source]);
a second imaging system configured to image and characterize an orange peel of the worksurface in an area proximate the defect, fringe, while the second image-capturing system uses the captured images to access paint defects cause internal degradation of the fringes and degradation at the edges of the fringes, [i.e., using of a second imaging system configured to image an orange peel of the worksurface, “second image-capturing system for inspecting paint defects in the order of millimeters, such as dirt, scratches, bubbles and surface roughness”, in an area proximate the defect, “the internal as well the edge degradations of fringe area corresponds to the area proximate the defect”]); and
a defect repair processor configured to select a repair strategy classifying the defects of the inspected material, and creating inspection report of the inspected material; and from Par. 0096, the inspection report evidencing the defects of the part 820 is shown, for example, in one or more monitor 860, which may be close to the optical system 800 or in another location, for example, where the quality information is useful to correct defects in the inspected materials 820, [i.e., a defect repair processor, “device”, configured to select a repair strategy, “creating inspection report of the inspected material”, based on a defect type, “implicit by classifying the defects of the inspected material”]).
However, while disclosing selecting the repair strategy based on a defect type, Gripp fails to teach or suggest, either alone or in combination with the other cited references, modifying the selected repair strategy template based on the orange peel characterization of the worksurface.
A further prior art of record, Richardson et al, (US-PGPUB 20190096057) discloses wherein characterizing the worksurface comprises identifying a delta value of orange peel, (see at least: Par. 0409-0415, using an algorithm for creating orange peel score by processing the edges of the light reflection bar in an inspection image frame section to create a numerical value, “delta value of orange peel”, that is correlated with the smoothness or roughness of the edge, ”i.e., comprises identifying a delta value of orange peel”; and calculating orange_edge_score using the ScoreEdge algorithm for both the top_edge_image and the bottom_edge_image, and averaging these two scores together to create the final orange_peel_score, where the Orange peel score tolerances for this example could be set so that orange peel scores below 15 are considered no defect, orange peel scores between 15 and 25 are categorized as mild orange peel, and orange peel scores above 25 could be considered severe orange peel. However, while disclosing the computing orange-peel score; the Richardson's computed orange-peel score is not used by any robotic device to modify a repair operation, (based on Applicant’s arguments). Thus, Richardson fails to teach or suggest, either alone or in combination with the other cited references, modifying the selected repair strategy template based on the orange peel characterization of the worksurface.
Another prior art of record, Matthews et al, (US-PGPUB 20030139836) discloses a motive robotic arm, (28 in Fig. 1); selecting a repair strategy template based on defect type, (see at least: Par. 0015, the vision controller 18 can be utilized to sort paint defects based upon size, type and location; and from Par. 0016-0017, the vision cell controller 18 stores the paint defect data 14, and the paint defect coordinates 20 “template”, and the robot cell controller 24 develops a repair strategy based upon the paint defect data and the paint defect coordinates, and based on a variety of known approaches toward paint defect repair. This may include path and processing parameters, tools, and robot choice, [i.e., selecting a repair strategy template, “the robot cell controller 24 develops a repair strategy”, based on defect type, “based on the paint defect data 14, and the paint defect coordinates 20, which implicitly include paint defects based upon size, type and location]. Further, Par. 0018, automated robotic repair system 26 performs an automated repair on the paint defects based upon the repair strategy.
However, Matthews "repair strategy" is predetermined; it is not modified based on a delta value or surface roughness measurement, (based on Applicant’s arguments). Thus, Matthews fails to teach or suggest, either alone or in combination with the other cited references, modifying the selected repair strategy template based on the orange peel characterization of the worksurface.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 01/24/2026