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 .
Application Notes
In order to practice compact prosecution, the examiner left a voicemail for Thomas Franklin (Reg. No. 63,456) on June 9, 2026 to give the applicant the opportunity to file a Terminal Disclaimer for US Patent 10,719,641 and US 11,599,689 as this was the only outstanding issue. Since the applicant did not return the examiner’s voicemail, the Double Patenting Rejection can be found below.
Information Disclosure Statement
The information disclosure statement submitted on May 5, 2025 has been considered by the examiner and made of record in the application file.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially createddoctrine grounded in public policy (a policy reflected in the statute) so as to prevent theunjustified or improper timewise extension of the "right to exclude" granted by a patentand to prevent possible harassment by multiple assignees. A nonstatutoryobviousness-type double patenting rejection is appropriate where the conflicting claimsare not identical, but at least one examined application claim is not patentably distinctfrom the reference claim(s) because the examined application claim is either anticipatedby, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir.1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d)may be used to overcome an actual or provisional rejection based on a nonstatutorydouble patenting ground provided the conflicting application or patent either is shown tobe commonly owned with this application, or claims an invention made as a result ofactivities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign aterminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with37 CFR 3.73(b).
Claims 21-31 are rejected on the ground of nonstatutory obvious-type double patenting as being unpatentable over claims 1-10 and 13 of U.S. Patent 10,719,641 in view of Zhang et al. (US PGPUB 2015/0006117 A1, hereinafter Zhang). Although the conflicting claims are not identical, they are not patentably distinct from each other. More specifically, the present application is a broader version of 10,719,641.
18,680,125
10,719,641
Claim Interpretation
21.A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the
code configured to, when executed by the processor, cause the processor to:
receive point cloud and/or orthomosaic data having a plurality of points;
classify, using at least a machine learning model, individual points of the plurality of points into a category from a plurality of categories;
define a plurality of shapes from the plurality of points by identifying, for the individual points of the plurality of points, a set of adjacent points from the plurality of points belonging to a common category from the plurality of categories;
define a boundary of each shape of the plurality of shapes by analyzing with respect to a criterion a position of each point associated with a border of that shape;
assign each shape from the plurality of shapes to a layer associated with the category from the plurality of categories for that shape; and
generate a data set using the boundary of each shape from the plurality of shapes and the layer for each category from the plurality of categories.
1.A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:
receive aerial data having a plurality of points arranged in a pattern; Claim 3 further teaches that the aerial data is from a point cloud;
provide an indication associated with each point from the plurality of points as an input to a machine learning model,
the machine learning model configured to classify each point from the plurality of points into a category from a plurality of non-binary categories;
for each point from the plurality of points, identify a set of points from the plurality of points adjacent to that point in the pattern and having a common category from the plurality of non-binary categories to define a shape from a plurality of shapes; define a polyline boundary of each shape from the plurality of shapes by analyzing with respect to a criterion a position of each point associated with a border of that shape relative to the position of at least one other point associated with the border of that shape;
define a layer for each category from the plurality of non-binary categories, the layer for a category from the plurality of non-binary categories including each shape from the plurality of shapes associated with that category; and
generate a computer-aided design file using the polyline boundary of each shape from the plurality of shapes and the layer for each category from the plurality of categories.
As can be seen with the side-by-side comparison, the present application is essentially a broader version of US 10,719,641 with minor word changes in view of the Zhang reference.
US 10,719,641 substantially discloses the claimed invention but fails to explicitly teach generating a data set including at least one of a two-dimensional (2D) model or a three-dimensional (3D) model of the point cloud and/or orthomosaic data.
However, Zhang teaches generate a data set including at least one of a two-dimensional (2D) model or a three-dimensional (3D) model of the point cloud and/or orthomosaic data (paragraphs 20, 21, read as three-dimensional (3D) modeling using three-dimensional point cloud data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s claimed invention to have incorporated the teachings of Zhang into the invention of US 10,719,641 in order to render three-dimensional modeling more efficiently.
Please see the following table for the dependent claims:
18/817,086
10,719,641
Claim Interpretation
22. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is from an aerial orthomosaic image formed by a plurality of pixels, and each point from the plurality of points is associated with a pixel from the plurality of pixels in the aerial orthomosaic image.
2. The non-transitory processor-readable medium of claim 1, wherein the aerial data is from an aerial orthomosaic image formed by a plurality of pixels, each point from the plurality of points is associated with a pixel from the plurality of pixels in the aerial orthomosaic image.
No substantial difference.
23. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is from a point cloud.
3. The non-transitory processor-readable medium of claim 1, wherein the aerial data is from a point cloud.
No substantial difference.
24. The non-transitory processor-readable medium of claim 21, wherein each category from the plurality of categories is associated with one of a manmade structure or a geological feature.
4. The non-transitory processor-readable medium of claim 1, wherein each category from the plurality of non-binary categories is associated with at least one of a manmade structure or a geological feature.
No substantial difference.
25. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to classify each point from the plurality of points into a category from the plurality of categories further comprises code configured to cause the processor to: classify, via the machine learning model, a point from the plurality of points into a set of possible categories from the plurality of categories; and select, from the set of possible categories, the category for the point from the plurality of points based on a predefined category hierarchy.
5. The non-transitory processor-readable medium of claim 1, the code further comprising code to cause the processor to:
From claim 1: the machine learning model configured to classify each point from the plurality of points into a category from a plurality of non-binary categories
select, from a set of categories, the category for a point from the plurality of points based on a predefined category hierarchy, each category from the set of categories being from the plurality of non-binary categories, the point being classified by the machine learning model to the set of categories.
No substantial difference.
26. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data includes elevation data associated with each point from the plurality of points.
6. The non-transitory processor-readable medium of claim 1, wherein the aerial data includes elevation data associated with each point from the plurality of points.
No substantial difference.
27. The non-transitory processor-readable medium of claim 21, wherein the machine learning model includes at least one of a neural network, a full resolution residual network (FRRN), a decision tree model, a random forest model, a Bayesian network or a clustering model.
7. The non-transitory processor-readable medium of claim 1, wherein the machine learning model includes at least one of a residual neural network, a full resolution residual network (FRRN), a decision tree model, a random forest model, a Bayesian network or a clustering model.
No substantial difference.
28. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to define the boundary includes code configured to cause the processor to define the boundary for each shape from the plurality of shapes as (1) encompassing that shape and (2) distinct from the boundary for the remaining shapes from the plurality of shapes.
8. The non-transitory processor-readable medium of claim 1, wherein the code to cause the processor to define the polyline boundary includes code to cause the processor to define the polyline boundary for each shape from the plurality of shapes as (1) encompassing that shape and (2) distinct from the polyline boundary for the remaining shapes from the plurality of shapes.
No substantial difference.
29. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is verified using ground control points.
9. The non-transitory processor-readable medium of claim 1, wherein the aerial data is verified using ground control points.
No substantial difference.
30. The non-transitory processor-readable medium of claim 21, wherein the criterion is a predetermined deviation threshold, the code to cause the processor to define the boundary includes code to cause the processor to define the boundary for that shape from the plurality of shapes as a straight line between a first point associated with the border and a second point associated with the border when a deviation of the position of each point associated with the border otherwise on the boundary is less than the predetermined deviation threshold.
10. The non-transitory processor-readable medium of claim 1, wherein the criterion is a predetermined deviation threshold, the code to cause the processor to define the polyline boundary includes code to cause the processor to define the polyline boundary for that shape from the plurality of shapes as a straight line between a first point associated with the border and a second point associated with the border when a deviation of the position of each point associated with the border otherwise on the polyline boundary is less than the predetermined deviation threshold.
No substantial difference.
31. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to identify the set of adjacent points from the plurality of points having the common category includes code configured to cause the processor to:
identify the set of adjacent points from the plurality of points having the common category based on a category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of categories.
13. The non-transitory processor-readable medium of claim 1, wherein the code to cause the processor to identify the set of adjacent points from the plurality of points having the common category to define the shape from the plurality of shapes includes code to cause the processor to:
identify the set of adjacent points from the plurality of points having the common category based on a category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of non-binary categories.
No substantial difference.
Claims 21-32 are rejected on the ground of nonstatutory anticipation-type double patenting as being unpatentable over claims 1-12 of U.S. Patent 11,599,689. Although the conflicting claims are not identical, they are not patentably distinct from each other. More specifically, the present application is a broader version of 11,599,689.
18,680,125
11,599,689
Claim Interpretation
21.A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the
code configured to, when executed by the processor, cause the processor to:
receive point cloud and/or orthomosaic data having a plurality of points;
classify, using at least a machine learning model, individual points of the plurality of points into a category from a plurality of categories;
define a plurality of shapes from the plurality of points by identifying, for the individual points of the plurality of points, a set of adjacent points from the plurality of points belonging to a common category from the plurality of categories;
define a boundary of each shape of the plurality of shapes by analyzing with respect to a criterion a position of each point associated with a border of that shape;
assign each shape from the plurality of shapes to a layer associated with the category from the plurality of categories for that shape; and
generate a data set using the boundary of each shape from the plurality of shapes and the layer for each category from the plurality of categories.
1.A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:
receive aerial data having a plurality of points arranged in a pattern; Claim 3 further teaches that the aerial data is from a point cloud;
classify, using at least a machine learning model, each point from the plurality of points into a category from a plurality of non-binary categories;
for each point from the plurality of points, identify a set of adjacent points from the plurality of points having a common category from the plurality of non-binary categories to define a shape from a plurality of shapes;
define a polyline boundary of each shape from the plurality of shapes by analyzing with respect to a criterion a position of each point associated with a border of that shape relative to the position of at least one other point associated with the border of that shape;
assign each shape from the plurality of shapes to a layer associated with the category from the plurality of non-binary categories for that shape from the plurality of shapes; and generate a file including at least one of a two-dimensional (2D) model or a three-dimensional (3D) model of aerial data using the polyline boundary of each shape from the plurality of shapes and the layer for each category from the plurality of non-binary categories.
As can be seen with the side-by-side comparison, the present application is essentially a broader version of US 11,599,689. Therefore, claim 1 of 11,599,689 anticipates claim 21 of the present application.
Please see the following table for the dependent claims:
18/817,086
11,599,689
Claim Interpretation
22. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is from an aerial orthomosaic image formed by a plurality of pixels, and each point from the plurality of points is associated with a pixel from the plurality of pixels in the aerial orthomosaic image.
2. The non-transitory processor-readable medium of claim 1, wherein the aerial data is from an aerial orthomosaic image formed by a plurality of pixels, each point from the plurality of points is associated with a pixel from the plurality of pixels in the aerial orthomosaic image.
No substantial difference.
23. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is from a point cloud.
3. The non-transitory processor-readable medium of claim 1, wherein the aerial data is from a point cloud.
No substantial difference.
24. The non-transitory processor-readable medium of claim 21, wherein each category from the plurality of categories is associated with one of a manmade structure or a geological feature.
4. The non-transitory processor-readable medium of claim 1, wherein each category from the plurality of non-binary categories is associated with one of a manmade structure or a geological feature.
No substantial difference.
25. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to classify each point from the plurality of points into a category from the plurality of categories further comprises code configured to cause the processor to: classify, via the machine learning model, a point from the plurality of points into a set of possible categories from the plurality of categories; and select, from the set of possible categories, the category for the point from the plurality of points based on a predefined category hierarchy.
5. The non-transitory processor-readable medium of claim 1, wherein the code to cause the processor to classify each point from the plurality of points into a category from the plurality of non-binary categories further comprises code to cause the processor to: classify, via the machine learning model, a point from the plurality of points into a set of possible categories from the plurality of non-binary categories; and select, from the set of possible categories, the category for the point from the plurality of points based on a predefined category hierarchy.
No substantial difference.
26. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data includes elevation data associated with each point from the plurality of points.
6. The non-transitory processor-readable medium of claim 1, wherein the aerial data includes elevation data associated with each point from the plurality of points.
No substantial difference.
27. The non-transitory processor-readable medium of claim 21, wherein the machine learning model includes at least one of a neural network, a full resolution residual network (FRRN), a decision tree model, a random forest model, a Bayesian network or a clustering model.
7. The non-transitory processor-readable medium of claim 1, wherein the machine learning model includes at least one of a neural network, a full resolution residual network (FRRN), a decision tree model, a random forest model, a Bayesian network or a clustering model.
No substantial difference.
28. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to define the boundary includes code configured to cause the processor to define the boundary for each shape from the plurality of shapes as (1) encompassing that shape and (2) distinct from the boundary for the remaining shapes from the plurality of shapes.
8. The non-transitory processor-readable medium of claim 1, wherein the code to cause the processor to define the polyline boundary includes code to cause the processor to define the polyline boundary for each shape from the plurality of shapes as (1) encompassing that shape and (2) distinct from the polyline boundary for the remaining shapes from the plurality of shapes.
No substantial difference.
29. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is verified using ground control points.
9. The non-transitory processor-readable medium of claim 1, wherein the aerial data is verified using ground control points.
No substantial difference.
30. The non-transitory processor-readable medium of claim 21, wherein the criterion is a predetermined deviation threshold, the code to cause the processor to define the boundary includes code to cause the processor to define the boundary for that shape from the plurality of shapes as a straight line between a first point associated with the border and a second point associated with the border when a deviation of the position of each point associated with the border otherwise on the boundary is less than the predetermined deviation threshold.
10. The non-transitory processor-readable medium of claim 1, wherein the criterion is a predetermined deviation threshold, the code to cause the processor to define the polyline boundary includes code to cause the processor to define the polyline boundary for that shape from the plurality of shapes as a straight line between a first point associated with the border and a second point associated with the border when a deviation of the position of each point associated with the border otherwise on the polyline boundary is less than the predetermined deviation threshold.
No substantial difference.
31. The non-transitory processor-readable medium of claim 21, wherein the code configured to cause the processor to identify the set of adjacent points from the plurality of points having the common category includes code configured to cause the processor to:
identify the set of adjacent points from the plurality of points having the common category based on a category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of categories.
11. The non-transitory processor-readable medium of claim 1, wherein the code to cause the processor to identify the set of adjacent points from the plurality of points having the common category includes code to cause the processor to:
identify the set of adjacent points from the plurality of points having the common category based on a category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of non-binary categories.
No substantial difference.
32. The non-transitory processor-readable medium of claim 21, wherein the point cloud and/or orthomosaic data is aerial data of a site of interest, the code further comprising code configured to cause the processor to: select the plurality of categories from a group of non-binary categories based at least in part on a set of characteristics associated with the site of interest; and define a category hierarchy for the plurality of categories based at least in part on the set of characteristics associated with the site of interest, wherein the code to cause the processor to identify the set of adjacent points from the plurality of points having the common category includes code to cause the processor to: identify the set of adjacent points from the plurality of points having the common category based on the category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of non-binary categories.
12. The non-transitory processor-readable medium of claim 1, wherein the aerial data is aerial data of a site of interest, the code further comprising code to cause the processor to: select the plurality of non-binary categories from a group of non-binary categories based at least in part on a set of characteristics associated with the site of interest; and define a category hierarchy for the plurality of non-binary categories based at least in part on the set of characteristics associated with the site of interest, the code to cause the processor to identify the set of adjacent points from the plurality of points having the common category includes code to cause the processor to: identify the set of adjacent points from the plurality of points having the common category based on the category hierarchy, the category hierarchy being based on at least one characteristic associated with each category from the plurality of non-binary categories.
No substantial difference.
Allowable Subject Matter
Applicant’s independent claim 21 recites a particular combination of elements, which is neither taught nor suggested by the prior art. Munoz, Suzuki, Stassopoulou, Kacyra, the other cited references, and a thorough search in the art disclose various aspects and features of applicant's claimed invention. However, Munoz, Suzuki, Stassopoulou, Kacyra, the other cited references, a thorough search in the art do not disclose or suggest receive point cloud and/or orthomosaic data having a plurality of points; classify, using at least a machine learning model, individual points of the plurality of points into a category from a plurality of categories; define a plurality of shapes from the plurality of points by identifying, for the individual points of the plurality of points, a set of adjacent points from the plurality of points belonging to a common category from the plurality of categories; define a boundary of each shape of the plurality of shapes by analyzing with respect to a criterion a position of each point associated with a border of that shape; assign each shape from the plurality of shapes to a layer associated with the category from the plurality of categories for that shape; and generate a data set including at least one of a two-dimensional (2D) model or a three-dimensional (3D) model of the point cloud and/or orthomosaic data using the boundary of each shape from the plurality of shapes and the layer for each category from the plurality of categories. Moreover, one of ordinary skill in the art would not have been motivated to arrive at applicant's claimed invention unless one was using applicant's claims and specification as a roadmap, thus using impermissible hindsight.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER M BRANDT whose telephone number is (571)270-1098. The examiner can normally be reached Mon - Fri 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy can be reached at 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER M BRANDT/Primary Examiner, Art Unit 2645 June 12, 2026
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