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 .
Notice to Applicants
This communication is filed in response to the action filed on 11/30/2023.
The claims 5, 7, 9-10, 12, 14, 16-17, 22, 24, 27-30, 32, 34 are currently amended. The claims 2-4, 6, 8, 13, 19-21, 23, 25, 31 are canceled. Claims 1, 5, 7, 9-12, 14-18, 22, 24, 26-30, and 32-34 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/30/2023 has been considered fully.
Claim Objections
Claims 5, and 22 are objected to because of the following informalities: the claims contain a misspelling/typo all occurrences of the phrase “neighbouring” are to be interpreted for purposes of this examination as the proper spelled form of “neighboring”. Appropriate correction is required.
Claims 9, 15, 26, and 33 are objected to because of the following informalities: the claims contain a misspelling/typo all occurrences of the phrase “parameterised” are to be interpreted for purposes of this examination as the proper spelled form of “parameterized”. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5, 10-12, 14, 17-18, 22, 24, 27-30, 32 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 2020/0041649 A1 to GREEN et al. (hereinafter “GREEN”).
As per claim 1, GREEN discloses a method of detecting surface deformation of a production asset (a system and corresponding method for generating a geometry model of an object of interest using 3D point cloud scans of the object which is acting as the asset; abstract; figs 3-5 and 15; paragraphs [0036], [0043], [0074-0075]), the method comprising: receiving a point cloud for a surface of the production asset (the computing system comprising a LIDAR sensor is used to acquire (receive) a plurality of points from a 3D point cloud situated about the object of interest; abstract; figs 3-5 and 15; paragraphs [0036], [0043], [0074-0075]); determining a model surface for the production asset from the point cloud (forming a surface geometry model of the candidate object based on the points collected from the LIDAR point cloud of the object; paragraphs [0034], [0037], [0043]), the model surface being an estimate of a deformation free representation of the surface of the production asset (forming a range hypothesis test comparing an expected range from the geometry model of the candidate object in comparison with the measured range of points in the LiDAR point cloud scan and deriving an error measure there between; paragraph [0043]), the model surface being determined from points in the point cloud including points representing a surface deformation (testing the range hypothesis for a series of expected locations for the surface geometry model of the candidate object and determining a likely lowest error measure; paragraph [0043]); determining a distance between at least one point in the point cloud and the model surface (the system and method is adapted to determine a mismatch distance between the kinematic reference and the machine geometry, the length (a determined distance) of the pitch-brace is altered to optimize the rake angle of the dipper teeth and updated to the internal model; paragraph [0080]); and outputting the distance (the parameters include limb/element length and would be output to be displayed via a output of device A going into an input of device B to be displayed or viewed by users; paragraphs [0036], [0154]).
As per claim 5, GREEN discloses the method according to claim 1,further comprising: smoothing points in the point cloud using locations of a plurality of neighbouring points in the point cloud (the point cloud geometry model parameters include a smoothing parameter among others; paragraph [0108]).
As per claim 7, GREEN discloses the method according to claim 1,further comprising: calculating a maximum distance between points in the point cloud and the model surface (a maximum displacement distance is calculated between the object and the point cloud generated via lidar sensor; fig 14; paragraphs [0124]); and associating the maximum distance with the production asset (the maximum displacement is indicated by a number included in each cell of the object of interest; fig 14; paragraphs [0060], [0124]).
As per claim 10, GREEN discloses the method according to claim 1,wherein the distance between the at least one point in the point cloud and the model surface is compensated for the model surface being determined from points in the point cloud including points representing the surface deformation (the geometry model chosen to fit the point cloud measured vis LIDAR sensor of the object of interest is corrected to have the lowest error value of fit and the model is adjusted to fit the point cloud points representing the object, where the adjustment step comprises compensation of the model to fit the object surface; paragraphs [0062-0065], [0082], [0096]).
As per claim 11, GREEN discloses the method according to claim 10, wherein the distance is compensated independent of a location of the at least one point in the point cloud (FIG. 10 shows the acceptance band in the extension space that corresponds to observing the candidate point-cloud measurements the negative hypothesis would not have been rejected if either the crowd or hoist were 0.01 m from the values as measured by the motor resolvers; fig 10 ; paragraph [0096]).
As per claim 12, GREEN discloses the method according to claim 10, wherein the distance is compensated using a linear transform applied to an initial distance between the at least one point in the point cloud and the model surface (a linear transform such as a linear least squares minimization is applied to the model and the corresponding point cloud of the object; paragraph [0120]).
As per claim 14, GREEN discloses the method according to claim 1,wherein the model surface is determined using a model selected from a plurality of models (the geometry model is selected from six base models and adjusted to fit the point cloud data of the object accordingly; paragraph [0037]).
As per claim 17, GREEN discloses the method according to claim 1, wherein outputting the distance further comprises: determining a maximum distance between point in the point cloud and the model surface (a maximum displacement distance is calculated between the object and the point cloud generated via lidar sensor; fig 14; paragraphs [0124]); classifying the point cloud for the surface according to the maximum distance (the point cloud point is classified as within the error tolerance or not within the error tolerance according to the displacement distance maximum that was calculated; fig 14; paragraphs [0037-0039], [0124]); and displaying the point cloud to a user according to the classification (the parameters including those associated with the maximum distance to the point cloud and would be output to be displayed via a output of device A going into an input of device B to be displayed or viewed by users; paragraphs [0036], [0154]).
As per claim 18, GREEN discloses a system for detecting surface deformation of a production asset comprising at least one processing system configured to (a system and corresponding method for generating a geometry model of an object of interest using 3D point cloud scans of the object which is acting as the asset; abstract; figs 3-5 and 15; paragraphs [0036], [0043], [0074-0075]): receive a point cloud for a surface of the production asset (the computing system comprising a LIDAR sensor is used to acquire (receive) a plurality of points from a 3D point cloud situated about the object of interest; abstract; figs 3-5 and 15; paragraphs [0036], [0043], [0074-0075]); determine a model surface for production asset from the point cloud (forming a surface geometry model of the candidate object based on the points collected from the LIDAR point cloud of the object; paragraphs [0034], [0037], [0043]), the model surface being an estimate of a deformation free representation of the surface of the production asset (forming a range hypothesis test comparing an expected range from the geometry model of the candidate object in comparison with the measured range of points in the LiDAR point cloud scan and deriving an error measure there between; paragraph [0043]), the model surface being determined from points in the point cloud including points representing a surface deformation (testing the range hypothesis for a series of expected locations for the surface geometry model of the candidate object and determining a likely lowest error measure; paragraph [0043]); and determine a distance between at least one point in the point cloud and the model surface (the system and method is adapted to determine a mismatch distance between the kinematic reference and the machine geometry, the length (a determined distance) of the pitch-brace is altered to optimize the rake angle of the dipper teeth and updated to the internal model; paragraph [0080]); and output the distance (the parameters include limb/element length and would be output to be displayed via a output of device A going into an input of device B to be displayed or viewed by users; paragraphs [0036], [0154]).
As per claim 22, GREEN discloses the system according to claim 18,wherein the at least one processing system is further configured to: smooth points in the point cloud using locations of a plurality of neighbouring points in the point cloud (the point cloud geometry model parameters include a smoothing parameter among others; paragraph [0108]).
As per claim 24, GREEN discloses the system according to claim 18, wherein the at least one processing system is further configured to: calculate a maximum distance between points in the point cloud and the model surface (a maximum displacement distance is calculated between the object and the point cloud generated via lidar sensor; fig 14; paragraphs [0124]); and associating the maximum distance with the production asset (the maximum displacement is indicated by a number included in each cell of the object of interest; fig 14; paragraphs [0060], [0124]).
As per claim 27, GREEN discloses the system according to claim 18,wherein the at least one processing system is further configured to, when outputting the distance: determine a maximum distance between point in the point cloud and the model surface (a maximum displacement distance is calculated between the object and the point cloud generated via lidar sensor; fig 14; paragraphs [0124]); classify the point cloud for the surface according to the maximum distance (the point cloud point is classified as within the error tolerance or not within the error tolerance according to the displacement distance maximum that was calculated; fig 14; paragraphs [0037-0039], [0124]); and display the point cloud to a user according to classification (the parameters including those associated with the maximum distance to the point cloud and would be output to be displayed via a output of device A going into an input of device B to be displayed or viewed by users; paragraphs [0036], [0154]).
As per claim 28, GREEN discloses the system according to claim 18,wherein the distance between the at least one point in the point cloud and the model surface is compensated for the model surface being determined from points in the point cloud including the points representing the surface deformation (the geometry model chosen to fit the point cloud measured vis LIDAR sensor of the object of interest is corrected to have the lowest error value of fit and the model is adjusted to fit the point cloud points representing the object, where the adjustment step comprises compensation of the model to fit the object surface; paragraphs [0062-0065], [0082], [0096]).
As per claim 29, GREEN discloses the system according to claim 28, wherein the distance is compensated independent of a location of the at least one point in the point cloud (FIG. 10 shows the acceptance band in the extension space that corresponds to observing the candidate point-cloud measurements the negative hypothesis would not have been rejected if either the crowd or hoist were 0.01 m from the values as measured by the motor resolvers; fig 10 ; paragraph [0096]).
As per claim 30, GREEN discloses the system according to claim 28,wherein the distance is compensated using a linear transform applied to an initial distance between the at least one point in the point cloud and the model surface (a linear transform such as a linear least squares minimization is applied to the model and the corresponding point cloud of the object; paragraph [0120]).
As per claim 32, GREEN discloses the system according to claim 18,wherein the model surface is determined using a model selected from a plurality of models (the geometry model is selected from six base models and adjusted to fit the point cloud data of the object accordingly; paragraph [0037]).
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 non-obviousness.
Claims 9, 15-16, 26, 33-34 are rejected under 35 § U.S.C. 103 as being obvious over US 2020/0041649 A1 to GREEN et al. (hereinafter “GREEN”) in view of US 2017/0193699 A1 to MEHR et al. (hereinafter “MEHR”).
As per claim 9, GREEN discloses the method according to claim 1. GREEN fails to disclose wherein the model surface may be fitted to a curved surface and is a parameterised polynomial model.
MEHR discloses wherein the model surface may be fitted to a curved surface and is a parameterised polynomial model (the system is adapted to apply 3D mesh models adapted to detect curvature on the surface of the mesh, and is based on a polynomial deformation model; paragraphs [0054] [0087], [0098]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have the model fitted to a curved surface using a polynomial model of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 9.
As per claim 15, GREEN discloses the method according to claim 14. GREEN fails to disclose wherein the plurality of models includes at least two models selected from the set including a parameterised polynomial model, a piecewise polynomial model and a rigid shape defined by a set of parameters.
MEHR discloses wherein the plurality of models includes at least two models selected from the set including a parameterised polynomial model, a piecewise polynomial model and a rigid shape defined by a set of parameters (the system is adapted to generate a parameterized model of polynomial to find that of best fit to match the shape of the object which would be rigid; paragraphs [0054], [0059-0061], [0087], [0098]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have a piecewise polynomial model and a rigid shape defined by a set of parameters of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 15.
As per claim 16, GREEN discloses the method according to claim 14. GREEN fails to disclose wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit.
MEHR discloses wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit (the system is adapted to be based on selected parameters chosen by the user choose a polynomial model of best fit to fit the object of interest; paragraphs [0054], [0059-0061]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 16.
As per claim 26, GREEN discloses the system according to claim 18. GREEN fails to disclose wherein the model surface may be fitted to a curved surface and is a parameterised polynomial model.
MEHR discloses wherein the model surface may be fitted to a curved surface and is a parameterised polynomial model (the system is adapted to apply 3D mesh models adapted to detect curvature on the surface of the mesh, and is based on a polynomial deformation model; paragraphs [0054] [0087], [0098]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have wherein the model surface may be fitted to a curved surface and is a polynomial model of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 26.
As per claim 33, GREEN discloses the system according to claim 32. GREEN fails to disclose wherein the plurality of models includes at least two models selected from the set including a parameterised polynomial model, a piecewise polynomial model and a rigid shape defined by a set of parameters.
MEHR discloses wherein the plurality of models includes at least two models selected from the set including a parameterised polynomial model, a piecewise polynomial model and a rigid shape defined by a set of parameters (the system is adapted to generate a parameterized model of polynomial to find that of best fit to match the shape of the object which would be rigid; paragraphs [0054], [0059-0061], [0087], [0098]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have a piecewise polynomial model and a rigid shape defined by a set of parameters of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 33.
As per claim 34, GREEN discloses the system according to claim 32. GREEN fails to disclose wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit.
MEHR discloses wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit (the system is adapted to be based on selected parameters chosen by the user choose a polynomial model of best fit to fit the object of interest; paragraphs [0054], [0059-0061]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify GREEN to have wherein each of the plurality of models is compared to the point cloud and the model is selected according to a best fit of MEHR reference. The Suggestion/motivation for doing so would have been to provide ability to more accurately measure curvature or lack thereof using mesh models and to provide a best fit model as suggested by paragraphs [0054], [0059]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine MEHR with GREEN to obtain the invention as specified in claim 34.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 2019/0108679 A1
US 2022/0044441 A1
US 2022/0051420 A1
US 2022/0138969 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800- 786-9199 (IN USA OR CANADA) or 571-272-1000.
/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/Jonathan S Lee/Primary Examiner, Art Unit 2677